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@STRING{ieee	= {Proceedings of the IEEE} }
@STRING{ieeeit	= {IEEE Trans.\ Information Theory} }
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@STRING{iu	= {Proc.\ Image Understanding Workshop} }
@STRING{jacm	= {Journal of the ACM} }
@STRING{jair	= {Journal of Artificial Intelligence Research} }
@STRING{jalc	= {Journal of Automata, Languages and Combinatorics} }
@STRING{jasa	= {Journal of the American Statistical Association} }
@STRING{jat	= {Journal of Approximation theory} }
@STRING{jcb	= {Journal of Computational Biology} }
@STRING{jda	= {Journal of Discrete Algorithms} }
@STRING{jmb	= {Journal of Molecular Biology} }
@STRING{jmlr	= {Journal of Machine Learning Research} }
@STRING{josa	= {Journal of the Optical Society of America} }
@STRING{jrr	= {International Journal of Robotics Research} }
@STRING{kdd	= kdd_long }
@STRING{kdd_short={KDD} }
@STRING{kluwer	= {Kluwer Academic} }
@STRING{lnai	= {Lecture Notes in Artificial Intelligence} }
@STRING{lncs	= {Lecture Notes in Computer Science} }
@STRING{mathcomp= {Math. of Computation} }
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@STRING{mitai	= {Artificial Intelligence Laboratory, Massachusetts
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@STRING{ml	= {Machine Learning} }
@STRING{morgan	= {Morgan Kaufmann Publishers} }
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@STRING{naacl_short={NAACL} }
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@STRING{nips1	= {Advances in Neural Information Processing Systems 1} }
@STRING{nips10	= {Advances in Neural Information Processing Systems 10} }
@STRING{nips10e	= {M.~I. Jordan and M.~J. Kearns and S.~A. Solla} }
@STRING{nips11	= {Advances in Neural Information Processing Systems 11} }
@STRING{nips11e	= {M.~S. Kearns and S.~A. Solla and D.~A. Cohn} }
@STRING{nips12	= {Advances in Neural Information Processing Systems 12} }
@STRING{nips12e	= {S.~A. Solla and T.~K. Leen and K.-R. M\"uller} }
@STRING{nips13	= {Advances in Neural Information Processing Systems 13} }
@STRING{nips13e	= {T.~K. Leen and T.~G. Dietterich and V. Tresp} }
@STRING{nips14	= {Advances in Neural Information Processing Systems 14} }
@STRING{nips14e	= {T.~G. Dietterich and S. Becker and Z. Ghahramani} }
@STRING{nips15	= {Advances in Neural Information Processing Systems 15} }
@STRING{nips15e	= {S. Becker and S. Thrun and K. Obermayer} }
@STRING{nips16	= {Advances in Neural Information Processing Systems 16} }
@STRING{nips16e	= {S. Thrun and L. Saul and B. Sch\"olkopf} }
@STRING{nips17	= {Advances in Neural Information Processing Systems 17} }
@STRING{nips17e	= {L. K. Saul and Y. Weiss and L. Bottou} }
@STRING{nips18	= {Advances in Neural Information Processing Systems 18} }
@STRING{nips18e	= {Y. Weiss and B. Sch\"olkopf and J. Platt} }
@STRING{nips19	= {Advances in Neural Information Processing Systems 19} }
@STRING{nips19e	= {B. Sch\"olkopf and J. Platt and T. Hofmann} }
@STRING{nips1e	= {D.~S. Touretzky} }
@STRING{nips2	= {Advances in Neural Information Processing Systems 2} }
@STRING{nips20	= {Advances in Neural Information Processing Systems 20} }
@STRING{nips20e	= {J.~C. Platt and D. Koller and Y. Singer and S. Roweis} }
@STRING{nips21	= {Advances in Neural Information Processing Systems 21} }
@STRING{nips21e	= {D. Koller and Y. Bengio and D. Schuurmans and L. Bottou
		  and A. Culotta} }
@STRING{nips22	= {Advances in Neural Information Processing Systems 22} }
@STRING{nips22e	= {Yoshua Bengio and Dale Schuurmans and John Lafferty and
		  Chris Williams and Alan Culota} }
@STRING{nips23	= {Advances in Neural Information Processing Systems 23} }
@STRING{nips23e	= {Richard Zemel and John Shawe-Taylor and John Lafferty and
		  Chris Williams and Alan Culota} }
@STRING{nips24	= {Advances in Neural Information Processing Systems 24} }
@STRING{nips2e	= {D.~S. Touretzky} }
@STRING{nips3	= {Advances in Neural Information Processing Systems 3} }
@STRING{nips3e	= {R. Lippmann and J.~E. Moody and D.~S. Touretzky} }
@STRING{nips4	= {Advances in Neural Information Processing Systems 4} }
@STRING{nips4e	= {J.~E. Moody and S.~J. Hanson and R. Lippmann} }
@STRING{nips5	= {Advances in Neural Information Processing Systems 5} }
@STRING{nips5e	= {S.~J. Hanson and J.~D. Cowan and C.~L. Giles} }
@STRING{nips6	= {Advances in Neural Information Processing Systems 6} }
@STRING{nips6e	= {J.~D. Cowan and G. Tesauro and J. Alspector} }
@STRING{nips7	= {Advances in Neural Information Processing Systems 7} }
@STRING{nips7e	= {G. Tesauro and D.~S. Touretzky and T.~K. Leen} }
@STRING{nips8	= {Advances in Neural Information Processing Systems 8} }
@STRING{nips8e	= {D.~S. Touretzky and M.~C. Mozer and M.~E. Hasselmo} }
@STRING{nips9	= {Advances in Neural Information Processing Systems 9} }
@STRING{nips9e	= {M.~C. Mozer and M.~I. Jordan and T. Petsche} }
@STRING{nips_short={NIPS} }
@STRING{nn	= {Neural Networks} }
@STRING{ny	= {New York} }
@STRING{opteng	= {Optical Engineering} }
@STRING{pami	= {IEEE Transactions on Pattern Analysis and Machine
		  Intelligence} }
@STRING{patt	= {Proc.\ Intl.\ Conf.\ Pattern Recognition} }
@STRING{pnas	= {Proc.\ Natl.\ Acad.\ Sci.\ USA} }
@STRING{pr	= {Pattern Recognition} }
@STRING{prip	= {Proceedings of IEEE Computer Society Conference on Pattern
		  Recognition and Image Processing} }
@STRING{prsl	= {Proceedings of the Royal Society of London} }
@STRING{prslb	= {Proceedings of the Royal Society of London B} }
@STRING{recsys	= recsys_long }
@STRING{recsys_short={RecSys} }
@STRING{robau	= {Proceedings of IEEE Conference on Robotics and
		  Automation} }
@STRING{sciam	= {Scientific American} }
@STRING{siam	= {SIAM J. Comp.} }
@STRING{siamopt	= {SIAM Journal on Optimization} }
@STRING{siamstat= {SIAM J.\ on Scientific and Statistical Computing} }
@STRING{sigir	= sigir_long }
@STRING{sigir_short={SIRIR} }
@STRING{sml	= {Statistical Machine Learning Program} }
@STRING{spring	= {Springer} }
@STRING{stoc	= {Proceedings ACM Symposium on Theory of Computing (STOC)} }
@STRING{tams	= {Transactions of the American Mathematical Society} }
@STRING{theoprob= {Theory of Probability and its Applications} }
@STRING{tmc	= {Thinking Machines Corporation} }
@STRING{uai	= uai_long }
@STRING{uai_short={UAI} }
@STRING{visres	= {Vision Research} }
@STRING{weizman	= {Weizman Institute of Science} }
@STRING{wiley	= {John Wiley and Sons} }
@STRING{wsdm	= wsdm_long }
@STRING{wsdm_short={WSDM} }
@STRING{www	= www_long }
@STRING{www10	= {19th International Conference on the World Wide Web} }
@STRING{www11	= {20th International Conference on the World Wide Web} }
@STRING{www11e	= {S. Srinivasan and K. Ramamritham and A. Kumar and M. P.
		  Ravindra and E. Bertino and R. Kumar} }
@STRING{www_short={WWW} }

@article{Aaronsonetal96,
  author  = {J. Aaronson and R. Burton and H. Dehling and D. Gilat and
             T. Hill and B. Weiss},
  title   = {Strong laws for {L} and {U}-statistics},
  journal = {Transactions of the American Mathematical Society},
  year    = 1996,
  volume  = 348,
  pages   = {2845--2865}
}

@book{Abarbanel96,
  title     = {Analysis of Observed Chaotic Data},
  publisher = {Springer},
  year      = {1996},
  author    = {H.~D.~I. Abarbanel},
  address   = {New York}
}

@article{AbbKolNg06,
  author  = {P. Abbeel and D. Koller and A. Ng},
  title   = {Learning Factor Graphs in Polynomial Time and Sample
             Complexity},
  journal = {Journal of Machine Learning Research},
  year    = {2006},
  volume  = {7},
  pages   = {1743--1788}
}

@phdthesis{Abe89,
  author = {N. Abe},
  title  = {Polynomial Learnability as a Formal Model of Natural
            Language Acquisition},
  school = {Department of Computer and Information Science, University
            of Pennsylvania},
  year   = {1989}
}

@inproceedings{Abe89b,
  author    = {N. Abe},
  title     = {Polynomial learning of semilinear sets},
  booktitle = {Proc. 2nd Annu. Workshop on Comput. Learning Theory},
  year      = {1989},
  pages     = {25--40},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{Abe91,
  author  = {N. Abe},
  title   = {Learning Commutative Deterministic Finite State Automata
             in Polynomial Time},
  journal = {New Generation Computing},
  year    = {1991},
  volume  = {8},
  pages   = {319--336},
  number  = {4},
  note    = {First appeared in Proc. First Int. Workshop on Alg.
             Learning Theory, 1990}
}

@inproceedings{Abe92,
  author    = {N. Abe},
  title     = {Preservation of predictability under polynomially sparse
               variations and its applications},
  booktitle = alt-ws,
  year      = {1992},
  pages     = {163--174},
  publisher = {IOS Press}
}

@inproceedings{Abe93,
  author    = {N. Abe},
  title     = {On the sample complexity of various learning strategies in
               the probabilistic {PAC} learning paradigms},
  booktitle = {Proc. 2nd Internat. Workshop on Nonmonotonic and Inductive
               Logic},
  year      = {1993},
  volume    = {659},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {89-106},
  publisher = {Springer-Verlag}
}

@article{Abe97,
  author  = {N. Abe},
  title   = {Towards Realistic Theories of Learning},
  journal = {New Generation Computing},
  year    = {1997},
  volume  = {15},
  pages   = {3-26},
  number  = {1},
  note    = {Special issue for AII '94 and ALT '94}
}

@article{AbeBacEvgVer09,
  author    = {J. Abernethy and F. Bach and T. Evgeniou and J.-P. Vert},
  title     = {A New Approach to Collaborative Filtering: Operator
               Estimation with Spectral Regularization},
  journal   = {Journal of Machine Learning Research},
  publisher = {Microtome Publishing},
  volume    = 10,
  pages     = {803--826},
  year      = 2009,
  month     = mar
}

@inproceedings{AbeBarRakTew08,
  author    = {Jacob Abernethy and Peter L. Bartlett and Alexander
               Rakhlin and Ambuj Tewari},
  title     = {Optimal Strategies and Minimax Lower Bounds for Online
               Convex Games},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2008}
}

@inproceedings{AbeLanWar06,
  author    = {J. Abernethy and J. Langford and M. K. Warmuth},
  title     = {Continuous Experts and the Binning Algorithm},
  booktitle = colt,
  year      = 2006,
  address   = {Pittsburgh},
  month     = {June},
  pages     = {544--558}
}

@inproceedings{AbeLi96,
  author    = {N. Abe and Hang Li},
  title     = {Learning word association norms using tree cut pair
               models},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {3--11},
  publisher = {Morgan Kaufmann}
}

@inproceedings{AbeLon99,
  author    = {N. Abe and P. M. Long},
  title     = {Associative reinforcement learning using linear
               probabilistic concepts},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {3--11},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{Abelson77,
  author  = {H. Abelson},
  title   = {Computational geometry of linear threshold functions},
  journal = {Information and Control},
  year    = {1977},
  volume  = {34},
  pages   = {66--92}
}

@article{Abelson78,
  author  = {H. Abelson},
  title   = {Towards a theory of local and global in computation},
  journal = {Theoretical Computer Science},
  year    = {1978},
  volume  = {6},
  pages   = {41--67}
}

@inproceedings{AbeMam94,
  author    = {N. Abe and Hiroshi Mamitsuka},
  title     = {A new method for predicting protein secondary structures
               based on stochastic tree grammars},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1994},
  pages     = {3--11},
  publisher = {Morgan Kaufmann}
}

@article{AbeMam97,
  author    = {N. Abe and Hiroshi Mamitsuka},
  title     = {Predicting protein secondary structure using stochastic
               tree grammars},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {29},
  pages     = {275--301},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{AbeMam98,
  author    = {N. Abe and Hiroshi Mamitsuka},
  title     = {Query learning strategies using boosting and bagging},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {1--9},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{AbeNak99,
  author    = {N. Abe and Atsuyoshi Nakamura},
  title     = {Leaning to optimally schedule internet banner
               advertisements},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {12--21},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{AbeTak93,
  author    = {N. Abe and J. Takeuchi},
  title     = {The `lob-pass' problem and an on-line learning model of
               rational choice},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {422--428},
  publisher = {ACM Press, New York, NY}
}

@article{AbeTakWar01,
  title   = {{Polynomial Learnability of Stochastic Rules with Respect
             to the KL-Divergence and Quadratic Distance}},
  author  = {Abe, N. and Takeuchi, J. and Warmuth, M.K.},
  journal = {IEICE Transactions on Information and Systems},
  volume  = {84},
  number  = {3},
  pages   = {299--316},
  year    = {2001}
}

@inproceedings{AbeTakWar91,
  author    = {N. Abe and J. Takeuchi and M. K. Warmuth},
  title     = {Polynomial learnability of probabilistic concepts with
               respect to the {K}ullback-{L}eibler divergence},
  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {277--289},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{AbeWar92,
  author    = {N. Abe and M. K. Warmuth},
  title     = {On the Computational Complexity of Approximating
               Distributions by Probabilistic Automata},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {9},
  pages     = {205--260},
  note      = {Special issue for COLT90},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{AbeWat92,
  author  = {N. Abe and O. Watanabe},
  title   = {Polynomially Sparse Variations and Reducibility among
             prediction problems},
  journal = {IEICE Trans. Inf. and Syst.},
  year    = {1992},
  volume  = {E75-D},
  pages   = {449--458},
  number  = {4},
  month   = jul,
  comment = {Gives an example to distinguish Turing-reducibility and
             prediction-preserving reducibility}
}

@inproceedings{Abney02,
  title     = {Bootstrapping},
  author    = {Steven Abney},
  booktitle = acl,
  year      = {2002}
}

@article{Abney04,
  title     = {Understanding the {Y}arowsky Algorithm},
  author    = {Steven Abney},
  journal   = {Computational Linguistics},
  volume    = {30},
  number    = {3},
  pages     = {365--395},
  year      = {2004},
  publisher = {{MIT} Press}
}

@article{AboKurOhl04,
  author  = {M.~I. Abouelhoda and S. Kurtz and E. Ohlebusch},
  title   = {Replacing suffix trees with enhanced suffix arrays},
  journal = {Journal of Discrete Algorithms},
  year    = {2004},
  volume  = {2},
  pages   = {53-86}
}

@phdthesis{Abouelhoda05,
  author = {M.~I. Abouelhoda},
  title  = {Algorithms and a Software System for Comparative Genome
            Analysis},
  school = {University of Ulm, Germany},
  year   = {2005}
}

@techreport{Abrahamsen92,
  author      = {P. Abrahamsen},
  title       = {A review of {Gaussian} random fields and correlation
                 functions},
  institution = {Norwegian Computing Center},
  year        = {1992},
  type        = {Rapport},
  number      = {917},
  address     = {Oslo},
  note        = {www.nr.no/publications/917\_Rapport.ps}
}

@article{AbrEriKoi08,
  author  = {T. Abrudan and J. Eriksson and V. Koivunen},
  title   = {Steepest Descent Algorithms for Optimization Under Unitary
             Matrix Constraint},
  journal = {IEEE Transactions on Signal Processing},
  volume  = {56},
  number  = {3},
  year    = {2008}
}

@book{AbrMarRat83,
  title     = {Manifolds, Tensor Analysis, and Applications},
  publisher = {Addison-Wesley},
  year      = {1983},
  author    = {R. Abraham and J.~E. Marsden and T. Ratiu},
  address   = {Reading, MA}
}

@book{AbrSte65,
  title     = {Handbook of Mathematical Functions},
  publisher = {Dover Publications},
  year      = {1965},
  author    = {Milton Abramowitz and Irene A. Stegun},
  address   = {New York}
}

@article{Abu-Mostafa89,
  author  = {Y. S. Abu-Mostafa},
  title   = {The {V}apnik-{C}hervonenkis Dimension: Information verses
             Complexity in Learning},
  journal = {Neural Computation},
  year    = {1989},
  volume  = {1},
  pages   = {312--317},
  number  = {3}
}

@inproceedings{Abu-Mostafa92,
  author    = {Y. Abu-Mostafa},
  title     = {A Method for Learning from Hints},
  booktitle = {Advances in Neural Information Processing Systems 5},
  year      = {1992},
  editor    = {S.~J. Hanson and J.~D. Cowan and C.~L. Giles},
  pages     = {73--80},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@article{Abu-Mostafa93,
  author  = {Y.~S. Abu-Mostafa},
  title   = {Hints and the {VC-dimension}},
  journal = {Neural Computation},
  year    = {1993},
  volume  = {5},
  pages   = {278--288}
}

@article{Abu-Mostafa95,
  author  = {Y.~S. Abu-Mostafa},
  title   = {Hints},
  journal = {Neural Computation},
  year    = {1995},
  volume  = {7},
  pages   = {639--671},
  number  = {4},
  type    = {Review}
}

@inproceedings{AcaKolDun11,
  title     = {All-at-once optimization for coupled matrix and tensor
               factorizations},
  author    = {Evrim Acar and Tamara G. Kolda and Daniel M. Dunlavy},
  booktitle = {Mining and Learning with Graphs (MLG 2011)},
  year      = {2011}
}

@article{Achlioptas03,
  title   = {Database-friendly random projections:
             Johnson-Lindenstrauss with binary coins},
  author  = {D. Achlioptas},
  journal = {J. Comput. Syst. Sci},
  year    = {2003},
  number  = {4},
  volume  = {66},
  pages   = {671--687}
}

@inproceedings{AchMcS01,
  author    = {D. Achlioptas and F. McSherry},
  title     = {Fast computation of low-rank matrix approximations},
  booktitle = {the 33rd ACM Symposium on Theory of Computing},
  year      = {2001},
  pages     = {611--618}
}

@inproceedings{AchMcS05,
  author    = {D. Achlioptas and F. McSherry},
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  booktitle = {To appear in Journal of ACM},
  year      = {2005}
}

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  author    = {Dimitris Achlioptas and Frank McSherry and Bernhard
               Sch{\"o}lkopf},
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  year      = 2002,
  editor    = nips15e,
  address   = {Cambridge, MA},
  publisher = mitpr
}

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  doi      = {10.1007/s10994-018-5769-2},
  language = {en},
  number   = {8-9},
  journal  = {Machine Learning},
  author   = {Adamskiy, Dmitry and Bellotti, Anthony and Dzhamtyrova,
              Raisa and Kalnishkan, Yuri},
  month    = sep,
  year     = {2019},
  pages    = {1231--1260},
  file     = {Adamskiy et al - Aggregating Algorithm for prediction of
              packs (2019).pdf:/Users/zcranko/Documents/Library/Journal
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}

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  title     = {The Gaussian Process Density Sampler},
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  publisher = {MIT Press},
  year      = {2009},
  note      = {In press}
}

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             Série},
  author  = {Adly, Samir and Ernst, Emil and Théra, Michel},
  year    = {2003},
  note    = {tex.fjournal: Journal de Mathématiques Pures et
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             46N10 49J40) tex.mrnumber: 2012809 tex.mrreviewer:
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  file    = {Adly et al - On the closedness of the algebraic difference
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}

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@inproceedings{AgaBarRavetal09,
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               and Martin Wainwright},
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               Complexity of Convex Optimization},
  booktitle = {Neural Information Processing Systems},
  year      = {2009}
}

@inproceedings{AgaChe09,
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               Knowledge Discovery and Data Mining},
  publisher = {ACM},
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  editor    = {J.F. Elder and F. Fogelman-Souli{\'e} and P.A. Flach and
               M.J. Zaki},
  pages     = {19--28},
  url       = {http://doi.acm.org/10.1145/1557019.1557029}
}

@inproceedings{AgaDuc11,
  author    = {Alekh Agarwal and John C. Duchi},
  title     = {Distributed Delayed Stochastic Optimization},
  booktitle = nips,
  year      = {2011}
}

@inproceedings{AgaHazKalSch06,
  author    = {Amit Agarwal and Elad Hazan and Satyen Kale and Robert E.
               Schapire},
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               Method},
  booktitle = icml,
  year      = 2006,
  pages     = {9--16}
}

@inproceedings{AgaHazSch06,
  author    = {Amit Agarwal and Elad Hazan and Satyen Kale and Robert E.
               Schapire},
  title     = {Algorithms for portfolio management based on the Newton
               method},
  booktitle = icml,
  year      = 2006,
  pages     = {9--16}
}

@inproceedings{AgaMer07,
  title     = {Predictive discrete latent factor models for large scale
               dyadic data},
  author    = {D. Agarwal and S. Merugu},
  booktitle = {KDD},
  publisher = {ACM},
  year      = {2007},
  editor    = {P. Berkhin and R. Caruana and X. Wu},
  pages     = {26--35}
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@inproceedings{agarwal2018reductions,
  author    = {Alekh Agarwal and Alina Beygelzimer and Miroslav
               Dud{\'{\i}}k and John Langford and Hanna M. Wallach},
  title     = {A Reductions Approach to Fair Classification},
  booktitle = {International Conference on Machine Learning},
  pages     = {60--69},
  year      = {2018}
}

@inproceedings{Agarwaletal10,
  title     = {Estimating rates of rare events with multiple hierarchies
               through scalable log-linear models},
  author    = {D. Agarwal and R. Agrawal and R. Khanna and N. Kota},
  booktitle = {the 16th {ACM} {SIGKDD} International Conference on
               Knowledge Discovery and Data Mining},
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  year      = {2010},
  editor    = {B. Rao and B. Krishnapuram and A. Tomkins and Q. Yang},
  pages     = {213--222},
  url       = {http://doi.acm.org/10.1145/1835804.1835834}
}

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  author    = {Pankaj K. Agarwal and Sameer Sen},
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  address   = {The Netherlands}
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  title     = {Davenport-Schinzel sequences and their geometric
               applications},
  publisher = {North-Holland},
  year      = 2000,
  address   = {New York},
  pages     = {1--47}
}

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  editor    = {J. van Leeuwen},
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  year        = {2004},
  address     = {Cambridge, UK},
  month       = {August},
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                 of videos. We model a moving face as a linear dynamical
                 system whose appearance changes with pose. An
                 autoregressive and moving average (ARMA) model is used to
                 represent such a system. The choice of ARMA model is based
                 on its ability to take care of the change in appearance
                 while modeling the dynamics of pose, expression etc},
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               Scheuermann, Richard H and others},
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  year      = {2019}
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  editor    = {R.A. Baeza-Yates and P. Boldi and B.A. Ribeiro-Neto and
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  url       = {http://doi.acm.org/10.1145/1498759.1498766}
}

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  booktitle    = www,
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  year      = {2008},
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@article{AizHarSai89,
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@inproceedings{AizHelPit92,
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  year      = {1992},
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@inproceedings{AizPit91,
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  year      = {1991},
  pages     = {170--179},
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@inproceedings{AizPit92,
  author    = {H. Aizenstein and L. Pitt},
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  year      = {1992},
  pages     = {71--76},
  publisher = {ACM Press, New York, NY}
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@article{AizPit95,
  author    = {H. Aizenstein and Leonard Pitt},
  title     = {On The Learnability Of Disjunctive Normal Form Formulas},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {19},
  pages     = {183--208},
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@inproceedings{AkaPerHarSch13,
  title        = {Label-embedding for attribute-based classification},
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  booktitle    = cvpr,
  pages        = {819--826},
  year         = {2013},
  organization = {IEEE}
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@article{Akashi90,
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@article{Akataetal13,
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  pages   = {507--520}
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@inproceedings{AkkGuv96,
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  publisher = {Morgan Kaufmann}
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@inproceedings{AkuMiyKuh00,
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  title     = {A Simple Greedy Algorithm for Finding Functional
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  year      = {2000},
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  series    = {Lecture Notes in Artificial Intelligence},
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@inproceedings{AkuTak93,
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  volume    = {743},
  series    = {Lecture Notes in Artificial Intelligence},
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  year    = {1993},
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}

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  abstract = {[Let P$_{\textrm{1}}$ and P$_{\textrm{2}}$ be two
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  number   = {1},
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  volume  = {4},
  pages   = {605--618},
  number  = {4}
}

@article{Amahroq_SubdifferentiabilityDifference_2008,
  title    = {On the subdifferentiability of the difference of two
              functions and local minimization},
  volume   = {16},
  issn     = {0927-6947, 1572-932X},
  doi      = {10.1007/s11228-008-0085-9},
  language = {en},
  number   = {4},
  journal  = {Set-Valued Analysis},
  author   = {Amahroq, Tijani and Penot, Jean-Paul and Syam, Aïcha},
  month    = dec,
  year     = {2008},
  pages    = {413--427},
  file     = {Amahroq et al - On the subdifferentiability of the
              difference of two functions and local
              (2008).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Amahroq et al - On the subdifferentiability of the
              difference of two functions and local
              (2008).pdf:application/pdf}
}

@article{AmaKan95,
  author  = {E. Amaldi and V. Kann},
  title   = {The complexity and approximability of finding maximum
             feasible subsystems of linear relations},
  journal = {Theoretical Computer Science},
  year    = {1995},
  volume  = {147},
  pages   = {181--210}
}

@article{AmaKan98,
  author  = {E. Amaldi and V. Kann},
  title   = {On the approximability of minimizing nonzero variables or
             unsatisfied relations in linear systems},
  journal = {Theoretical Computer Science},
  year    = {1998}
}

@mastersthesis{Amakawa91,
  author = {K. Amakawa},
  title  = {Self-learning reaching motion of a multi-joint arm using a
            trial-and-error heuristic and a neural network},
  school = {UC Santa Cruz},
  year   = {1991}
}

@article{AmaMurMulFinetal97,
  author  = {Amari, Shun-ichi and N. Murata and K.-R. M\"uller and M.
             Finke and H. Yang},
  title   = {Asymptotic Statistical Theory of Overtraining and
             Cross-Validation},
  journal = {IEEE Transactions on Neural Networks},
  year    = {1997},
  volume  = {8},
  pages   = {985--996},
  number  = {5}
}

@book{AmaNag93,
  author    = {Amari, Shun-ichi and H. Nagaoka},
  title     = {Methods of Information Geometry},
  publisher = {Oxford University Press},
  year      = {1993}
}

@article{AmaParFuk00,
  author  = {Amari, Shun-ichi and Hyeyoung Park and Kenji Fukumizu},
  title   = {Adaptive Method of Realizing Natural Gradient Learning for
             Multilayer Perceptrons},
  journal = {Neural Computation},
  volume  = {12},
  number  = {6},
  year    = {2000},
  pages   = {1399--1409}
}

@article{Amari01,
  author  = {Amari, Shun-ichi},
  title   = {Information geometry on hierarchy of probability
             distributions},
  journal = {IEEE Transactions on Information Theory},
  year    = {2001},
  volume  = {47},
  pages   = {1701--1711},
  number  = {5},
  month   = {July}
}

@article{Amari67,
  author  = {Amari, Shun-ichi},
  title   = {A theory of adaptive pattern classifiers},
  journal = {IEEE Transactions on Electronic Computers},
  year    = {1967},
  volume  = {16},
  pages   = {299--307},
  key     = {Amari}
}

@book{Amari85,
  author    = {Amari, Shun-ichi},
  title     = {Differential-Geometrical Methods in Statistics},
  publisher = {Springer},
  year      = {1985},
  address   = {Berlin}
}

@article{Amari90,
  author  = {Amari, Shun-ichi},
  title   = {Mathematical foundations of neurocomputing},
  journal = {the IEEE},
  year    = {1990},
  volume  = {78},
  pages   = {1443--1463},
  number  = {9}
}

@article{Amari91,
  author  = {Amari, Shun-ichi},
  title   = {Mathematical Theory of Neural Learning},
  journal = {New Generation Computing},
  year    = {1991},
  volume  = {8},
  pages   = {281--294},
  number  = {4}
}

@article{Amari95,
  author  = {Amari, Shun-ichi},
  title   = {The {EM} algorithm and Information geometry in neural
             network learning},
  journal = {Neural Computation},
  year    = {1995},
  volume  = {7},
  pages   = {13--18},
  number  = {1},
  month   = jan
}

@article{Amari95b,
  author  = {Amari, Shun-ichi},
  title   = {Information Geometry of the {EM} and {em} Algorithms for
             Neural Networks},
  journal = {Neural Networks},
  year    = {1995},
  volume  = {8},
  pages   = {1379--1408},
  number  = {9}
}

@article{Amari98,
  author  = {Amari, Shun-ichi},
  title   = {Natural Gradient Works Efficiently in Learning},
  journal = {Neural Computation},
  year    = {1998},
  volume  = {10},
  pages   = {251--276},
  number  = {2}
}

@book{Amari_InformationGeometry_2016,
  address   = {Japan},
  series    = {Applied mathematical sciences},
  title     = {Information geometry and its applications},
  volume    = {194},
  isbn      = {978-4-431-55977-1},
  publisher = {Springer},
  author    = {Amari, Shun'ichi},
  year      = {2016},
  note      = {OCLC: ocn930997330},
  file      = {Amari - Information geometry and its applications
               (2016).pdf:/Users/zcranko/Documents/Library/Book/Amari -
               Information geometry and its applications
               (2016).pdf:application/pdf}
}

@article{AmaWu99,
  author  = {Amari, Shun-ichi and S. Wu},
  title   = {Improving support vector machine classifiers by modifying
             kernel functions},
  journal = {Neural Networks},
  volume  = {12},
  number  = {6},
  year    = {1999},
  pages   = {783--789}
}

@inproceedings{AmaWu99b,
  author    = {Amari, Shun-ichi and S. Wu},
  title     = {An information-geometrical method for improving
               performance of support vector machine classifiers},
  booktitle = {{ICANN}'99},
  year      = {1999},
  editor    = {D. Willshaw and A. Murray},
  volume    = {1},
  pages     = {85--90},
  publisher = {{IEE} Press}
}

@article{Ambainis00,
  author  = {Andris Ambainis},
  title   = {How rich is the structure of the intrinsic complexity of
             learning},
  journal = {Information Processing Letters},
  year    = {2000},
  volume  = {75},
  pages   = {109--112},
  number  = {3}
}

@article{Ambainis01,
  author  = {Andris Ambainis},
  title   = {On learning formulas in the limit and with assurance},
  journal = {Information Processing Letters},
  year    = {2001},
  volume  = {77},
  pages   = {9--11},
  number  = {1},
  month   = jan
}

@inproceedings{Ambainis95,
  author    = {Andris Ambainis},
  title     = {Application of {K}olmogorov complexity to inductive
               inference with limited memory},
  booktitle = alt-ws,
  year      = {1995},
  pages     = {313--318},
  publisher = {Springer-Verlag}
}

@incollection{Ambainis95b,
  author    = {A. Ambainis},
  title     = {Optimization problem in inductive inference},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {96--107}
}

@inproceedings{Ambainis95c,
  author    = {Andris Ambainis},
  title     = {The power of procrastination in inductive inference: how
               it depends on used ordinal notations},
  booktitle = {Computational Learning Theory: EuroColt '95},
  year      = {1995},
  pages     = {99--111},
  publisher = {Springer-Verlag}
}

@inproceedings{Ambainis96,
  author    = {Andris Ambainis},
  title     = {Probabilistic and team {PFIN}-type learning: general
               properties},
  booktitle = {Proc. 9th Annu. Conf. on Comput. Learning Theory},
  year      = {1996},
  pages     = {157--168},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{AmbApsCalFreetal97,
  author    = {Andris Ambainis and Kalvis Aps{\={\i}}tis and Cristian
               Calude and R{\=u}si{\c n}{\v s} Freivalds and Marek
               Karpinski and Tomas Larfeldt and Iveta Sala and Juris Smotrovs},
  title     = {Effects of {K}olmogorov complexity present in inductive
               inference as well},
  booktitle = alt,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {244--259},
  publisher = {Springer-Verlag}
}

@inproceedings{AmbApsFreGasetal97,
  author    = {Andris Ambainis and Kalvis Aps{\={\i}}tis and R{\=u}si{\c
               n}{\v s} Freivalds and William Gasarch and Carl H. Smith},
  title     = {Team learning as a game},
  booktitle = alt,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {2--17},
  publisher = {Springer-Verlag}
}

@article{AmbApsFreSmi01,
  author  = {Andris Ambainis and Kalvis Aps{\={\i}}tis and R{\=u}si{\c
             n}s Freivalds and Carl H. Smith},
  title   = {Hierarchies of probabilistic and team {FIN}-learning},
  journal = {Theoret. Comput. Sci.},
  year    = {2001},
  volume  = {261},
  pages   = {91--117},
  number  = {1},
  note    = {Special issue for ALT '97}
}

@inproceedings{AmbDesFarKan97,
  author    = {Andris Ambainis and R. Desper and Martin Farach and
               Sampath Kannan},
  title     = {Nearly tight bounds on the learnability of evolution},
  booktitle = {Proc. 38th Annu. IEEE Symposium on Foundations of Computer
               Science},
  year      = {1997},
  pages     = {524--533},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@inproceedings{AmbFre96,
  author    = {Andris Ambainis and R{\=u}si{\c n}s Freivalds},
  title     = {transformations that preserve learnability},
  booktitle = alt,
  year      = {1996},
  volume    = {1160},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {299--311},
  publisher = {Springer-Verlag}
}

@inproceedings{AmbFreSmi96,
  author    = {Andris Ambainis and R{\=u}si{\c n}s Freivalds and Carl H.
               Smith},
  title     = {General Inductive Inference Types Based on
               Linearly-Ordered Sets},
  booktitle = {Proc.\ 13th Annual Symposium on Theoretical Aspects of
               Computer Science: STACS'96},
  year      = {1996},
  volume    = {1046},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {243--253},
  publisher = {Springer-Verlag}
}

@article{AmbFreSmi99,
  author  = {Andris Ambainis and Rusins Freivalds and Carl H. Smith},
  title   = {Inductive Inference with Procrastination: Back to
             Definitions},
  journal = {Fundamenta Informaticae},
  year    = {1999},
  volume  = {40},
  pages   = {1--16}
}

@article{AmbJaiSha99,
  author  = {Andris Ambainis and Sanjay Jain and Arun Sharma},
  title   = {Ordinal mind change complexity of language
             identification},
  journal = {Theoret. Comput. Sci.},
  year    = {1999},
  volume  = {220},
  pages   = {323--343},
  number  = {2},
  comment = {first in EuroColt '97}
}

@article{AmbMcL02,
  author  = {C. Ambroise and G.J. McLachlan},
  title   = {Selection bias in gene extraction on the basis of
             microarray gene-expression data},
  journal = {the National Academy of Science},
  year    = {2002},
  volume  = {99},
  pages   = {6562--6566},
  number  = {10}
}

@incollection{Ambrosio_UserGuide_2013,
  address      = {Berlin, Germany},
  title        = {A user’s guide to optimal transport},
  volume       = {2062},
  isbn         = {978-3-642-32159-7 978-3-642-32160-3},
  abstract     = {This text is an expanded version of the lectures given by
                  the ﬁrst author in the 2009 CIME summer school of
                  Cetraro. It provides a quick and reasonably account of the
                  classical theory of optimal mass transportation and of its
                  more recent developments, including the metric theory of
                  gradient ﬂows, geometric and functional inequalities
                  related to optimal transportation, the ﬁrst and second
                  order differential calculus in the Wasserstein space and
                  the synthetic theory of metric measure spaces with Ricci
                  curvature bounded from below.},
  language     = {en},
  booktitle    = {Modelling and {Optimisation} of {Flows} on {Networks}},
  publisher    = {Springer-Verlag},
  author       = {Ambrosio, Luigi and Gigli, Nicola},
  collaborator = {Ambrosio, Luigi and Bressan, Alberto and Helbing, Dirk and
                  Klar, Axel and Zuazua, Enrique},
  year         = {2013},
  doi          = {10.1007/978-3-642-32160-3_1},
  pages        = {1--155},
  file         = {Ambrosio, Gigli - A user’s guide to optimal transport
                  (2013).pdf:/Users/zcranko/Documents/Library/Book
                  Section/Ambrosio, Gigli - A user’s guide to optimal
                  transport (2013).pdf:application/pdf}
}

@inproceedings{AmbSmo94,
  author    = {A. Ambainis and J. Smotrovs},
  title     = {Enumerable classes of total recursive functions:
               complexity of inductive inference},
  booktitle = alt-ws,
  year      = {1994},
  pages     = {10--25},
  publisher = {Springer-Verlag}
}

@article{Amdahl67,
  author    = {Gene Amdahl},
  title     = {Validity of the Single Processor Approach to Achieving
               Large-Scale Computing Capabilities},
  booktitle = {AFIPS Conference Proceedings},
  volume    = {30},
  pages     = {483-485},
  year      = {1967}
}

@inproceedings{AmeFisHofHei94,
  author    = {F. Ameur and P. Fischer and K. U. H{\"o}ffgen and F. Meyer
               auf der Heide},
  title     = {Trial and Error: a New Approach to Space-bounded
               Learning},
  booktitle = {Computational Learning Theory: EuroColt '93},
  year      = {1994},
  volume    = {New Series Number 53},
  series    = {The Institute of Mathematics and its Applications
               Conference Series},
  pages     = {133--144},
  address   = {Oxford},
  publisher = {Oxford University Press},
  isbn      = {0 19 853492 2}
}

@article{AmeSanVidBen01,
  author    = {Juan-Carlos Amengual and Alberto Sanchis and Enrique Vidal
               and Jos{\'{e}}-Miguel Benedi},
  title     = {Language Simplification through Error-Correcting and
               Grammatical Inference Techniques},
  journal   = {Machine Learning},
  year      = {2001},
  volume    = {44},
  pages     = {143--159},
  number    = {1/2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Ameur95,
  author    = {Foued Ameur},
  title     = {A space-bounded learning algorithm for axis-parallel
               rectangles},
  booktitle = {Computational Learning Theory: EuroColt '95},
  year      = {1995},
  pages     = {313--321},
  publisher = {Springer-Verlag}
}

@inproceedings{AmeVez00,
  author    = {Esma A{\"{\i}}meur and Mathieu V{\'{e}}zeau},
  title     = {Short-Term Profiling for a Case-Based Reasoning},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {23--30},
  publisher = {Springer, Berlin}
}

@article{AmiFarGalGiaetal94,
  author  = {A. Amir and M. Farach and Z. Galil and R. Giancarlo and K.
             Park},
  title   = {Dynamic Dictionary Matching},
  journal = {Journal of Computer and System Science},
  year    = {1994},
  volume  = {49},
  pages   = {208--222},
  number  = {2},
  month   = {October}
}

@incollection{aminietal09,
  author    = {Massih Amini and Nicolas Usunier and Cyril Goutte},
  title     = {Learning from Multiple Partially Observed Views - an
               Application to Multilingual Text Categorization},
  booktitle = {Advances in Neural Information Processing Systems (NIPS)
               22},
  year      = {2009},
  pages     = {28--36}
}

@book{Amit84,
  title     = {Field theory, the renormalization group, and critical
               phenomena},
  publisher = {World Scientific},
  year      = {1984},
  author    = {D.~J. Amit},
  address   = {Singapore}
}

@inproceedings{AmmDyeSmi14,
  title     = {Conditional Random Field Autoencoders for Unsupervised
               Structured Prediction},
  author    = {Waleed Ammar and Chris Dyer and Noah A. Smith},
  booktitle = nips,
  year      = {2014}
}

@inproceedings{AmoCulTad98,
  author    = {T. R. Amoth and Paul Cull and Prasad Tadepalli},
  title     = {Exact learning of tree patterns from queries and
               counterexamples},
  booktitle = {Proc. 11th Annu. Conf. on Comput. Learning Theory},
  year      = {1998},
  pages     = {175--186},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{AmoCulTad99,
  author    = {T. R. Amoth and Paul Cull and Prasad Tadepalli},
  title     = {Exact learning of unordered tree patterns from queries},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {323--332},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{AmoKol17,
  author    = {Brandon Amos and J. Zico Kolter },
  title     = {{OptNet}: Differentiable Optimization as a Layer in Neural
               Networks},
  booktitle = icml,
  year      = 2017
}

@inproceedings{AmoRodSacetal18,
  title     = {Differentiable MPC for End-to-end Planning and Control},
  author    = {B. Amos and I. Rodriguez and J. Sacks and B. Boots and J.
               Kolter},
  booktitle = nips,
  year      = {2018}
}

@inproceedings{Amsterdam88,
  author    = {J. Amsterdam},
  title     = {Extending the {V}aliant learning model},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1988},
  pages     = {381--394},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@inproceedings{Amsterdam88b,
  author       = {J. Amsterdam},
  title        = {Some Philosophical Problems with Formal Learning Theory},
  booktitle    = {Proc. AAAI-88},
  year         = {1988},
  pages        = {580--584},
  address      = {Saint Paul, Minn.},
  month        = aug,
  organization = {American Association for Artificial Intelligence},
  comment      = {A philosophical attack on computational learning theory}
}

@mastersthesis{Amsterdam88c,
  author = {J. Amsterdam},
  title  = {The Valiant Learning Model: Extensions and Assessment},
  school = {MIT Department of Electrical Engineering and Computer
            Science},
  year   = {1988},
  month  = jan
}

@article{An_DifferentialStability_2018,
  title    = {Differential stability of convex optimization problems
              with possibly empty solution sets},
  abstract = {As a complement to two recent papers by An and Yen [An,
              D.T.V., Yen, N.D.: Differential stability of convex
              optimization problems under inclusion constraints. Appl.
              Anal., 94, 108--128 (2015)], and by An and Yao [An, D.T.V.,
              Yao, J.-C.: Further results on differential stability of
              convex optimization problems. J. Optim. Theory Appl., 170,
              28--42 (2016)] on subdifferentials of the optimal value
              function of infinite-dimensional convex optimization
              problems, this paper studies the differential stability of
              convex optimization problems, where the solution set may be
              empty. By using a suitable sum rule for
              \${\textbackslash}varepsilon\$-subdifferentials, we obtain
              exact formulas for computing the
              \${\textbackslash}varepsilon\$-subdifferential of the
              optimal value function. Several illustrative examples are
              also given.},
  journal  = {arXiv:1805.01656 [math]},
  author   = {An, Duong Thi Viet and Yao, Jen-Chih},
  month    = may,
  year     = {2018},
  note     = {arXiv: 1805.01656},
  keywords = {⛔ No DOI found, Mathematics - Optimization and Control},
  file     = {An, Yao - Differential stability of convex optimization
              problems with possibly empty
              (2018).pdf:/Users/zcranko/Documents/Library/Journal
              Article/An, Yao - Differential stability of convex
              optimization problems with possibly empty
              (2018).pdf:application/pdf}
}

@inproceedings{AnaGe16,
  author    = {Anima Anandkumar and Rong Ge},
  title     = {Efficient approaches for escaping higher order saddle
               points in non-convex optimization},
  booktitle = colt,
  year      = {2016}
}

@inproceedings{AnaGeHsuKak13,
  title     = {A Tensor Spectral Approach to Learning Mixed Membership
               Community Models},
  author    = {Anandkumar, Anima and Ge, Rong and Hsu, Daniel and Kakade,
               Sham M},
  booktitle = colt,
  year      = {2013}
}

@article{Anandkumaretal14,
  author  = {Animashree Anandkumar and Rong Ge and Daniel Hsu and Sham
             M. Kakade and Matus Telgarsky},
  title   = {Tensor Decompositions for Learning Latent Variable
             Models},
  journal = {Journal of Machine Learning Research},
  year    = {2014},
  volume  = {15},
  pages   = {2773-2832}
}

@inproceedings{And-e-SilLud00,
  author    = {Ricardo Bezerra de Andrade e Silva and Teresa Bernarda
               Ludermir},
  title     = {Obtaining Simplified Rule Bases by Hybrid Learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {879--886},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{AndAroBiletal13,
  title     = {Deep Canonical Correlation Analysis},
  author    = {Galen Andrew and Raman Arora and Jeff A. Bilmes and Karen
               Livescu},
  booktitle = icml,
  year      = {2013}
}

@book{AndBaiBisDemetal95,
  title     = {{LAPACK} Users' Guide},
  publisher = {SIAM},
  year      = {1995},
  author    = {E. Anderson and Z. Bai and C. Bischof and J. Demmel and J.
               Dongarra and J. Du Croz and A. Greenbaum and S. Hammarling
               and A. McKenney and S. Ostrouchov and D. Sorensen},
  address   = {Philadelphia},
  edition   = {Second}
}

@inproceedings{AndDraPet00,
  author    = {Charles W. Anderson and Bruce A. Draper and David A.
               Peterson},
  title     = {Behavioral Cloning of Student Pilots with Modular Neural
               Networks},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {25--32},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@book{Anderberg73,
  title     = {Cluster Analysis for Applications},
  publisher = {Academic Press},
  year      = {1973},
  author    = {M.~R. Anderberg},
  address   = {New York}
}

@article{Anderson63,
  author  = {Anderson, T. W.},
  title   = {Asymptotic theory for principal component analysis},
  journal = {Ann. Math. Statist.},
  year    = {1963},
  volume  = {34},
  pages   = {122--148}
}

@article{Anderson63b,
  author  = {Anderson, T. W.},
  title   = {Asymptotic theory for principal component analysis},
  journal = {Ann. Math. Statist.},
  year    = {1963},
  volume  = {34},
  pages   = {122--148}
}

@article{Anderson84,
  author  = {J.~A. Anderson},
  title   = {Regression and Ordered Categorical Variables (with
             Discussion)},
  journal = {Journal of the Royal Statistical Society -- Series B},
  year    = {1984},
  volume  = {46},
  pages   = {1--30}
}

@inproceedings{Anderson89,
  author    = {C.~H. Anderson},
  title     = {A conditional probabilty interpretation of {Kanerva}'s
               sparse distributed memory},
  booktitle = {the International Joint Conference on Neural Networks},
  year      = {1989},
  pages     = {I-415--I-417},
  address   = {Washington D.C.},
  month     = jun,
  publisher = {IEEE TAB Neural Network Committee}
}

@inproceedings{Anderson99,
  author    = {John R. Anderson},
  title     = {{ACT-R} and learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {522},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{AndFreDouJor03,
  author  = {Christophe Andrieu and Nando de Freitas and Arnaud Doucet
             and Michael I. Jordan},
  title   = {An Introduction to MCMC for Machine Learning},
  journal = {Machine Learning},
  year    = {2003},
  volume  = {50},
  pages   = {5--43}
}

@article{AndFuj99,
  author  = {S. Suzuki H. Ando and T. Fujita},
  title   = {Unsupervised visual learning of three-dimensional objects
             using a modular network architecture},
  journal = {Neural Networks},
  year    = {1999},
  volume  = {12},
  pages   = {1037--1051},
  number  = {7--8}
}

@inproceedings{AndGao07,
  author    = {Galen Andrew and Jianfeng Gao},
  title     = {Scalable Training of $L_1$-Regularized Log-Linear Models},
  booktitle = icml,
  year      = 2007,
  pages     = {33--40},
  location  = {Corvalis, Oregon},
  publisher = {ACM},
  address   = {New York, NY, USA}
}

@article{AndHalTit94,
  author  = {N. Anderson and P. Hall and D. Titterington},
  title   = {Two-sample test statistics for measuring discrepancies
             between two multivariate probability density functions
             using kernel-based density estimates},
  journal = {Journal of Multivariate Analysis},
  year    = {1994},
  volume  = {50},
  pages   = {41--54}
}

@article{AndMat92,
  author    = {John R. Anderson and M. Matessa},
  title     = {Explorations of an Incremental, Bayesian Algorithm for
               Categorization},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {9},
  pages     = {275--308},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{AndMooCoh00,
  author    = {Brigham S. Anderson and Andrew W. Moore and David Cohn},
  title     = {A Nonparametric Approach to Noisy and Costly
               Optimization},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {17--24},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{andor2016globally,
  title   = {Globally normalized transition-based neural networks},
  author  = {Andor, Daniel and Alberti, Chris and Weiss, David and
             Severyn, Aliaksei and Presta, Alessandro and Ganchev,
             Kuzman and Petrov, Slav and Collins, Michael},
  journal = {arXiv:1603.06042},
  year    = {2016}
}

@article{AndPhi81,
  author  = {J.~A. Anderson and P.~R. Philips},
  title   = {Regression, Discrimination and Measurement Models for
             Ordered Categorical Variables},
  journal = {Applied Statistics},
  year    = {1981},
  volume  = {30},
  pages   = {22--31}
}

@techreport{Andreae85,
  author      = {P. M. Andreae},
  title       = {Justified Generalization: Acquiring Procedures from
                 Examples},
  institution = {MIT Artificial Intelligence Laboratory},
  year        = {1985},
  number      = {AI-TR-834},
  month       = jan,
  comment     = {Ph.D. thesis. Examines traces of a correct procedure
                 (physical motion procedure) to infer loops, etc.}
}

@book{AndRos88,
  title     = {Neurocomputing: Foundations of Research},
  publisher = {{MIT} Press},
  year      = {1988},
  editor    = {J. A. Anderson and E. Rosenfeld},
  address   = {Cambridge, Massachusetts}
}

@inproceedings{Andrychowiczetal16,
  author    = {Marcin Andrychowicz and Misha Denil and Sergio Gomez and
               Matthew W. Hoffman and David Pfau and Tom Schaul and
               Brendan Shillingford and Nando de Freitas},
  title     = {Learning to learn by gradient descent by gradient
               descent},
  booktitle = nips,
  year      = 2016
}

@inproceedings{AndTsoHof03,
  author    = {S. Andrews and I. Tsochantaridis and T. Hofmann},
  title     = {Support Vector Machines for Multiple-Instance Learning},
  booktitle = {Advances in Neural Information Processing Systems 15},
  year      = {2003},
  editor    = {S. Becker and S. Thrun and K. Obermayer},
  publisher = {{MIT} Press}
}

@inproceedings{AndZha05,
  title        = {A high-performance semi-supervised learning method for
                  text chunking},
  author       = {Ando, Rie Kubota and Zhang, Tong},
  booktitle    = acl,
  pages        = {1--9},
  year         = {2005},
  organization = {Association for Computational Linguistics}
}

@article{AneDuaSejMak06,
  title   = {Spatio-Temporal Dynamics in fMRI Recordings Revealed with
             Complex Independent Component Analysis},
  journal = {Neurocomputing},
  volume  = {69},
  pages   = {1502-1512},
  author  = {Anemuller, J. and Duann, J.-R. and Sejnowski, T. J. and
             Makeig, S.},
  year    = {2006}
}

@inproceedings{AngBirFelRivetal88,
  author    = {D. Angluin and L. Birnbaum and J. Feldman and R. Rivest
               and L. Valiant},
  title     = {Summary of the panel discussion},
  booktitle = {Proc. 1st Annu. Workshop on Comput. Learning Theory},
  year      = {1988},
  pages     = {398--401},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@inproceedings{AngCsu97,
  author    = {Dana Angluin and Mikl{\'o}s Cs{\H u}r{\"o}s},
  title     = {Learning {M}arkov chains with variable length memory from
               noisy output},
  booktitle = {Proc. 10th Annu. Conf. on Comput. Learning Theory},
  year      = {1997},
  pages     = {298--308},
  publisher = {ACM Press, New York, NY}
}

@article{AngFraPit92,
  author    = {D. Angluin and M. Frazier and L. Pitt},
  title     = {Learning conjunctions of {H}orn clauses},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {9},
  pages     = {147--164},
  annote    = {Earlier version in FOCS 1990 conference},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{AngGasSmi89,
  author  = {D. Angluin and W. Gasarch and C. Smith},
  title   = {Training sequences},
  journal = {Theoret. Comput. Sci.},
  year    = {1989},
  volume  = {66},
  pages   = {255--272},
  annote  = {University of Maryland, CS-TR-1894, UMIACS-TR-87-37,
             1987},
  comment = {Shows how to learn hard total functions by being taught
             relevant other functions first.}
}

@inproceedings{AngGhiOne13,
  title     = {A public domain dataset for human activity recognition
               using smartphones.},
  author    = {Anguita, Davide and Ghio, Alessandro and Oneto, Luca and
               Parra, Xavier and Reyes-Ortiz, Jorge Luis},
  booktitle = {European Symposium on Artificial Neural Networks},
  year      = {2013}
}

@inproceedings{AngGioBelSai98,
  author    = {Cosimo Anglano and Attilio Giordana and Giuseppe Lo Bello
               and Lorenza Saitta},
  title     = {An experimental evaluation of coevolutive concept
               learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {19--27},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{AngHelKar93,
  author  = {D. Angluin and L. Hellerstein and M. Karpinski},
  title   = {Learning read-once formulas with queries},
  journal = {J. ACM},
  year    = {1993},
  volume  = {40},
  pages   = {185--210},
  annote  = {Was International Computer Science Institute TR-89-05099,
             and University of California at Berkeley TR 89-528}
}

@article{AngKha95,
  author  = {Dana Angluin and M. Kharitonov},
  title   = {When won't membership queries help?},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1995},
  volume  = {50},
  pages   = {336--355},
  number  = {2},
  note    = {Earlier version appeared in 23rd STOC, 1991}
}

@inproceedings{AngKri94,
  author    = {D. Angluin and M. Kri{\c k}is},
  title     = {Learning with malicious membership queries and
               exceptions},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {57--66},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{AngKri97,
  author    = {Dana Angluin and M{\=a}rti{\c n}{\v s} Krikis},
  title     = {Teachers, Learners and Black Boxes},
  booktitle = {Proc. 10th Annu. Conf. on Comput. Learning Theory},
  year      = {1997},
  pages     = {285--297},
  publisher = {ACM Press, New York, NY}
}

@article{AngKriSloTur97,
  author    = {Dana Angluin and M{\=a}rti{\c n}{\v s} Krikis and Robert
               H. Sloan and Gy{\"o}rgy Tur{\'a}n},
  title     = {Malicious omissions and errors in answers to membership
               queries},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {28},
  pages     = {211--255},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{AngLai88,
  author    = {D. Angluin and P. Laird},
  title     = {Learning from noisy examples},
  journal   = {Machine Learning},
  year      = {1988},
  volume    = {2},
  pages     = {343--370},
  number    = {4},
  annote    = {Preliminary version appeared as YALE/DCS/RR-478.},
  comment   = {Error model assumes random error rate < 1/2 in
               classifications provided for examples. Algorithm tries to
               minimize number of examples which are misclassified by
               chosen hypothesis.},
  publisher = {Kluwer Academic Publishers, Boston}
}

@phdthesis{Angluin76,
  author = {D. Angluin},
  title  = {An Application of the Theory of Computational Complexity
            to the Study of Inductive Inference},
  school = {Electrical Engineering and Computer Science Department,
            University of California, Berkeley},
  year   = {1976}
}

@article{Angluin78,
  author  = {D. Angluin},
  title   = {On the Complexity of Minimum Inference of Regular Sets},
  journal = {Inform. Control},
  year    = {1978},
  volume  = {39},
  pages   = {337--350},
  number  = {3}
}

@article{Angluin80,
  author  = {D. Angluin},
  title   = {Inductive Inference of formal languages from positive
             data},
  journal = {Information and Control},
  year    = {1980},
  volume  = {48},
  pages   = {117--135}
}

@article{Angluin80b,
  author  = {D. Angluin},
  title   = {Inductive Inference of Formal Languages from Positive
             Data},
  journal = {Inform. Control},
  year    = {1980},
  volume  = {45},
  pages   = {117--135},
  number  = {2},
  month   = may,
  comment = {Can be done iff each language Li contains a finite subset
             Ti such that Ti is a subset of Li and if i!=j and Ti is a
             subset of Lj, then Lj is not a proper subset of Li; and Ti
             is computable from i.}
}

@article{Angluin82,
  author  = {D. Angluin},
  title   = {Inference of Reversible Languages},
  journal = {J. ACM},
  year    = {1982},
  volume  = {29},
  pages   = {741--765},
  number  = {3},
  month   = jul
}

@article{Angluin82b,
  author  = {D. Angluin},
  title   = {A note on the number of queries needed to identify regular
             languages},
  journal = {Inform. Control},
  year    = {1982},
  volume  = {51},
  pages   = {76--87}
}

@techreport{Angluin86,
  author      = {D. Angluin},
  title       = {Types of queries for concept learning},
  institution = {Yale University, Department of Computer Science},
  year        = {1986},
  number      = {YALEU/DCS/TR-479},
  month       = jun,
  comment     = {Surveys kinds of learning possible with equivalence,
                 membership, subset, superset, and disjointness queries, and
                 with random samplying.}
}

@techreport{Angluin87,
  author      = {D. Angluin},
  title       = {Debugging a {DAG} Efficiently},
  institution = {Yale University},
  year        = {1987},
  number      = {591}
}

@techreport{Angluin87b,
  author      = {D. Angluin},
  title       = {Learning $k$-Bounded Context-Free Grammars},
  institution = {Department of Computer Science, Yale University},
  year        = {1987},
  number      = {YALEU/ DCS/RR-557},
  month       = aug
}

@techreport{Angluin87c,
  author      = {D. Angluin},
  title       = {Learning k-term {DNF} Formulas using Queries and
                 Counterexamples},
  institution = {Department of Computer Science, Yale University},
  year        = {1987},
  number      = {YALEU/DCS/RR-559},
  month       = aug
}

@techreport{Angluin87d,
  author      = {D. Angluin},
  title       = {Learning propositional {H}orn sentences with hints},
  institution = {Department of Computer Science, Yale University},
  year        = {1987},
  number      = {YALEU/DCS/RR-590},
  month       = dec
}

@article{Angluin87e,
  author  = {D. Angluin},
  title   = {Learning Regular Sets from Queries and Counterexamples},
  journal = {Inform. Comput.},
  year    = {1987},
  volume  = {75},
  pages   = {87--106},
  number  = {2},
  month   = nov,
  comment = {Learning regular sets from a teacher who answers
             membership queries, and responds to false conjectures with
             counterexamples. Based on Gold's approach. Also learning
             context-free languages from teacher. Was Yale TR
             YALEU/DCS/TR-464}
}

@article{Angluin88,
  author  = {D. Angluin},
  title   = {Queries and Concept learning},
  journal = {Machine Learning},
  year    = {1988},
  volume  = {2},
  pages   = {319--342}
}

@techreport{Angluin88b,
  author      = {D. Angluin},
  title       = {Identifying languages from stochastic examples},
  institution = {Yale University, Dept. of Computer Science},
  year        = {1988},
  number      = {YALEU/ DCS/RR-614},
  address     = {New Haven, CT}
}

@inproceedings{Angluin88c,
  author    = {D. Angluin},
  title     = {Learning with hints},
  booktitle = {Proc. 1st Annu. Workshop on Comput. Learning Theory},
  year      = {1988},
  pages     = {167--181},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{Angluin88d,
  author    = {D. Angluin},
  title     = {Queries and Concept Learning},
  journal   = {Machine Learning},
  year      = {1988},
  volume    = {2},
  pages     = {319--342},
  number    = {4},
  month     = apr,
  annote    = {Preliminary version appeared as YALE/DCS/RR-479.},
  publisher = {Kluwer Academic Publishers, Boston}
}

@techreport{Angluin88e,
  author      = {D. Angluin},
  title       = {Requests for hints that return no hints},
  institution = {Yale University, YALE/DCS/RR-647},
  year        = {1988}
}

@inproceedings{Angluin89,
  author    = {D. Angluin},
  title     = {Equivalence queries and approximate fingerprints},
  booktitle = {Proc. 2nd Annu. Workshop on Comput. Learning Theory},
  year      = {1989},
  pages     = {134--145},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@techreport{Angluin89b,
  author      = {D. Angluin},
  title       = {Using queries to identify $\mu$-formulas},
  institution = {Yale University, YALE/DCS/RR-694},
  year        = {1989}
}

@article{Angluin90,
  author    = {D. Angluin},
  title     = {Negative results for equivalence queries},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  pages     = {121--150},
  annote    = {Was Yale University TR YALEU/DCS/RR-648},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Angluin92,
  author    = {D. Angluin},
  title     = {Computational learning theory: survey and selected
               bibliography},
  booktitle = {Proc. 24th Annu. ACM Sympos. Theory Comput.},
  year      = {1992},
  pages     = {351--369},
  publisher = {ACM Press, New York, NY}
}

@techreport{Angluin94,
  author      = {Dana Angluin},
  title       = {Exact learning of {$\mu$}-{DNF} formulas with malicious
                 membership queries},
  institution = {Yale University},
  year        = {1994},
  number      = {YALEU/DCS/TR-1020},
  month       = mar
}

@inproceedings{AngSlo91,
  author    = {D. Angluin and D. K. Slonim},
  title     = {Learning monotone {DNF} with an incomplete membership
               oracle},
  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {139--146},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{AngSlo94,
  author    = {Dana Angluin and Donna K. Slonim},
  title     = {Randomly Fallible Teachers: Learning Monotone {DNF} with
               an Incomplete Membership Oracle},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {14},
  pages     = {7--26},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{AngSmi83,
  author  = {D. Angluin and C. H. Smith},
  title   = {A survey of Inductive Inference: Theory and Methods},
  journal = {ACM Comput. Surv.},
  year    = {1983},
  volume  = {15},
  pages   = {237--269},
  number  = {3},
  month   = sep,
  comment = {Comprehensive survey of inductive inference a la Gold
             [Go67].}
}

@incollection{AngSmi87,
  author    = {Dana Angluin and Carl H. Smith},
  title     = {Inductive Inference},
  booktitle = {Encyclopedia of Artificial Intelligence},
  publisher = {J. Wiley and Sons, New York},
  year      = {1987},
  pages     = {409--418}
}

@inproceedings{Angulo00,
  author    = {Andreu Catal{\`{a}} Cecilio Angulo},
  title     = {${K}$-{SVRC}. {A} Multi-class Support Vector Machine},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {31--38},
  publisher = {Springer, Berlin}
}

@article{AngVal79,
  author  = {D. Angluin and L. G. Valiant},
  title   = {Fast Probabilistic Algorithms for Hamiltonian Circuits and
             Matchings},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1979},
  volume  = {18},
  pages   = {155--193},
  number  = {2},
  annote  = {contains oft-cited Chernoff bounds}
}

@article{AngWesZhu00,
  author  = {Dana Angluin and Jeffery Westbrook and Wenhong Zhu},
  title   = {Robot Navigation with Distance Queries},
  journal = {{SIAM} J. Comput.},
  year    = {2000},
  volume  = {30},
  pages   = {110--144},
  number  = {1}
}

@inproceedings{Anick03,
  author     = {P. Anick},
  title      = {Using terminological feedback for web search refinement: a
                log-based study},
  booktitle  = {SIGIR'03},
  booktitle1 = {the 26th {ACM} International Conference on Research and
                Development in Information Retrieval},
  year       = 2003,
  pages      = {88--95}
}

@article{anil2018sorting,
  title   = {Sorting out Lipschitz function approximation},
  author  = {Anil, Cem and Lucas, James and Grosse, Roger},
  journal = {arXiv:1811.05381},
  year    = {2018}
}

@inproceedings{Anil_SortingOut_2019,
  address   = {Long Beach, CA, USA},
  series    = {Proceedings of machine learning research},
  title     = {Sorting out {Lipschitz} function approximation},
  volume    = {97},
  abstract  = {Training neural networks under a strict Lipschitz
               constraint is useful for provable adversarial robustness,
               generalization bounds, interpretable gradients, and
               Wasserstein distance estimation. By the composition
               property of Lipschitz functions, it suffices to ensure that
               each individual affine transformation or nonlinear
               activation is 1-Lipschitz. The challenge is to do this
               while maintaining the expressive power. We identify a
               necessary property for such an architecture: each of the
               layers must preserve the gradient norm during
               backpropagation. Based on this, we propose to combine a
               gradient norm preserving activation function, GroupSort,
               with norm-constrained weight matrices. We show that
               norm-constrained GroupSort architectures are universal
               Lipschitz function approximators. Empirically, we show that
               norm-constrained GroupSort networks achieve tighter
               estimates of Wasserstein distance than their ReLU
               counterparts and can achieve provable adversarial
               robustness guarantees with little cost to accuracy.},
  booktitle = {Proceedings of the 36th international conference on
               machine learning},
  publisher = {PMLR},
  author    = {Anil, Cem and Lucas, James and Grosse, Roger},
  editor    = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  month     = jun,
  year      = {2019},
  pages     = {291--301}
}

@inproceedings{AniLucGro19,
  title     = {Sorting out {L}ipschitz function approximation},
  author    = {Anil, Cem and Lucas, James and Grosse, Roger},
  booktitle = icml,
  year      = {2019}
}

@article{AnlBie89,
  author  = {J.~K. Anlauf and M. Biehl},
  title   = {The {AdaTron}: an adaptive perceptron algorithm},
  journal = {Europhysics Letters},
  year    = {1989},
  volume  = {10},
  pages   = {687--692}
}

@inproceedings{AnoFisPolSim92,
  author    = {S. Anoulova and P. Fischer and S. P{\"o}lt and H. U.
               Simon},
  title     = {{PAB}-decisions for {B}oolean and real-valued features},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {353--362},
  publisher = {ACM Press, New York, NY}
}

@unpublished{Anonymous06,
  author = {Anonymous},
  title  = {Implicit Online Learning with Kernels},
  note   = {manuscript, in preparation},
  year   = {2006}
}

@inproceedings{Anonymous19,
  title     = {Adversarial Attacks on Graph Neural Networks via Meta
               Learning},
  author    = {Anonymous},
  booktitle = {Submitted to International Conference on Learning
               Representations},
  year      = {2019},
  url       = {https://openreview.net/forum?id=Bylnx209YX},
  note      = {under review}
}

@inproceedings{AnoPol94,
  author    = {S. Anoulova and S. P{\"o}lt},
  title     = {Using {K}ullback-{L}eibler Divergence in Learning Theory},
  booktitle = {Computational Learning Theory: EuroColt '93},
  year      = {1994},
  volume    = {New Series Number 53},
  series    = {The Institute of Mathematics and its Applications
               Conference Series},
  pages     = {145--156},
  address   = {Oxford},
  publisher = {Oxford University Press},
  isbn      = {0 19 853492 2}
}

@book{Anselin88,
  title     = {Spatial econometrics: methods and models},
  publisher = {Kluwer},
  year      = {1988},
  author    = {L. Anselin},
  address   = {Dordrecht}
}

@article{AnsLeiRosMut16,
  title     = {Unsupervised learning of invariant representations},
  author    = {Anselmi, Fabio and Leibo, Joel Z and Rosasco, Lorenzo and
               Mutch, Jim and Tacchetti, Andrea and Poggio, Tomaso},
  journal   = {Theoretical Computer Science},
  volume    = {633},
  pages     = {112--121},
  year      = {2016},
  publisher = {Elsevier}
}

@article{Anstee87,
  title   = {A polynomial algorithm for b-matchings: An alternative
             approach},
  journal = {Information Processing Letters},
  volume  = {24},
  number  = {3},
  pages   = {153--157},
  year    = {1987},
  author  = {Richard P. Anstee}
}

@article{AnTao05,
  author    = {L. T. H. An and P. D. Tao},
  title     = {The {DC} (difference of convex functions) Programming and
               {DCA} revisited with {DC} models of real world nonconvex
               optimization problems},
  journal   = {Annals of Operations Research},
  year      = {2005},
  volume    = {133},
  pages     = {23--46},
  number    = {1--4},
  publisher = {Springer Netherlands}
}

@techreport{AntBar94,
  author      = {M. Anthony and P. Bartlett},
  title       = {Function learning from interpolation},
  institution = {NeuroCOLT},
  year        = {1994},
  number      = {94-013},
  note        = {An extended abstract appeared in EuroCOLT'95, Paul
                 Vitanyi, ed., {LNAI} 904, Springer, 1995, 211--221}
}

@inproceedings{AntBar95,
  author    = {M. Anthony and P. Bartlett},
  title     = {Function learning from interpolation},
  booktitle = {Computational Learning Theory: EuroColt '95},
  year      = {1995},
  pages     = {211--221},
  publisher = {Springer-Verlag}
}

@book{AntBar99,
  title     = {A Theory of Learning in Artificial Neural Networks},
  publisher = {Cambridge University Press},
  year      = {1999},
  author    = {M. Anthony and P. Bartlett}
}

@book{AntBar99b,
  title     = {Neural Network Learning: Theoretical Foundations},
  publisher = {Cambridge University Press},
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  number  = {1},
  note    = {Special issue for AII '94 and ALT '94}
}

@article{Arnold57,
  author  = {V.~I. Arnol'd},
  title   = {On functions of three variables},
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}

@book{Arnold80,
  title     = {Mathematical methods of classical mechanics},
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  address   = {Berlin},
  edition   = {2nd}
}

@article{AroBabSteSwe97,
  author  = {S. Arora and L. Babai and J. Stern and Z. Sweedyk},
  title   = {Hardness of approximate optima in lattices, codes, and
             linear systems},
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  year    = {1997},
  volume  = {54},
  pages   = {317--331},
  number  = {2}
}

@article{Aronszajn44,
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  year    = {1944},
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  title   = {Theory of reproducing kernels},
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  pages   = {337--404}
}

@inproceedings{Aroraetal12,
  author    = {Sanjeev Arora and Rong Ge and Ravi Kannan and Ankur
               Moitra},
  title     = {Computing a Nonnegative Matrix Factorization -- Provably},
  booktitle = {ACM Symposium on Theory of Computing},
  year      = {2012}
}

@inproceedings{aroraetal14,
  author    = {S. Arora and A. Bhaskara and R. Ge and T. Ma},
  title     = {Provable bounds for learning some deep representations},
  booktitle = {ICML},
  year      = {2014}
}

@article{Aroraetal16,
  author  = {Sanjeev Arora and Rong Ge and Ravi Kannan and Ankur
             Moitra},
  title   = {Computing a nonnegative matrix factorization -- provably},
  journal = {SIAM Journal on Computing},
  year    = {2016},
  volume  = {45},
  number  = {4},
  pages   = {1582--1611}
}

@inproceedings{Arpitzetal17,
  author    = {Devansh Arpitz and Stanislaw Jastrzebski and Nicolas
               Ballas and David Krueger and Emmanuel Bengio and Maxinder
               S. Kanwal and Tegan Maharaj and Asja Fischer and Aaron
               Courville and Yoshua Bengio and Simon Lacoste-Julien},
  title     = {A Closer Look at Memorization in Deep Networks},
  booktitle = icml,
  year      = {2017}
}

@article{ArvVin00,
  author  = {V. Arvind and N. V. Vinodchandran},
  title   = {Exact learning via teaching assistants},
  journal = {Theoret. Comput. Sci.},
  year    = {2000},
  volume  = {241},
  pages   = {51--81},
  number  = {1--2},
  note    = {Special issue for ALT '96}
}

@inproceedings{ArvVin96,
  author    = {V. Arvind and N. V. Vinodchandran},
  title     = {The complexity of exactly learning algebraic concepts},
  booktitle = alt-ws,
  year      = {1996},
  volume    = {1160},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {100--112},
  publisher = {Springer-Verlag}
}

@inproceedings{ArvVin97,
  author    = {V. Arvind and N. V. Vinodchandran},
  title     = {Exact learning via teaching assistants},
  booktitle = alt-ws,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {291--306},
  publisher = {Springer-Verlag}
}

@article{AryMouNetSiletal98,
  author  = {S. Arya and D.~M. Mount and N.~S. Netanyahu and R.
             Silverman and A.~Y. Wu},
  title   = {An Optimal Algorithm For Approximate Nearest Neighbor
             Search in Fixed Dimensions},
  journal = {Journal of the ACM},
  year    = {1998},
  volume  = {45},
  pages   = {891--923},
  number  = {6},
  month   = {November}
}

@article{AsaNodTawHos96,
  author    = {Minoru Asada and Shoichi Noda and Sukoya Tawaratsumida and
               Koh Hosoda},
  title     = {Purposive Behavior Acquisition for a Real Robot by
               Vision-Based Reinforcement Learning},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {23},
  pages     = {279--303},
  publisher = {Kluwer Academic Publishers, Boston}
}

@book{Ash65,
  title     = {Information Theory},
  publisher = {Interscience Publishers},
  year      = {1965},
  author    = {R.\ Ash},
  address   = {New York}
}

@article{AshBalBlaBotetal00,
  author  = { Ashburner, M. and Ball, C. A. and Blake, J. A. and
             Botstein, D. and Butler, H. and Cherry, J. M. and Davis, A.
             P. and Dolinski, K. and Dwight, S. S. and Eppig, J. T. and
             Harris, M. A. and Hill, D. P. and Issel-Tarver, L. and
             Kasarskis, A. and Lewis, S. and Matese, J. C. and
             Richardson, J. E. and Ringwald, M. and Rubin, G. M. and
             Sherlock, G. },
  journal = { Nat Genet },
  pages   = {25-29 },
  title   = { Gene ontology: tool for the unification of biology. The
             Gene Ontology Consortium },
  volume  = { 25 },
  year    = { 2000 }
}

@inproceedings{asif2013inferring,
  title     = {Inferring and Learning Subgoal Sequences},
  author    = {Asif, Kaiser and Ziebart, Brian D},
  booktitle = {ICML Workshop on Robot Learning},
  year      = {2013}
}

@inproceedings{asif2015adversarial,
  title     = {Adversarial Cost-Sensitive Classification},
  author    = {Asif, Kaiser and Xing, Wei and Behpour, Sima and Ziebart,
               Brian D},
  booktitle = uai,
  pages     = {92--101},
  year      = {2015}
}

@inproceedings{Asifetal15,
  author    = {Kaiser Asif and Wei Xing and Sima Behpour and Brian D.
               Ziebart},
  title     = {Adversarial Cost-sensitive Classification},
  booktitle = uai,
  year      = {2015}
}

@article{Askari_NaiveFeature_2019,
  title      = {Naive feature selection: sparsity in naive bayes},
  shorttitle = {Naive feature selection},
  abstract   = {Due to its linear complexity, naive Bayes classification
                remains an attractive supervised learning method,
                especially in very large-scale settings. We propose a
                sparse version of naive Bayes, which can be used for
                feature selection. This leads to a combinatorial
                maximum-likelihood problem, for which we provide an exact
                solution in the case of binary data, or a bound in the
                multinomial case. We prove that our bound becomes tight as
                the marginal contribution of additional features decreases.
                Both binary and multinomial sparse models are solvable in
                time almost linear in problem size, representing a very
                small extra relative cost compared to the classical naive
                Bayes. Numerical experiments on text data show that the
                naive Bayes feature selection method is as statistically
                effective as state-of-the-art feature selection methods
                such as recursive feature elimination, \$l\_1\$-penalized
                logistic regression and LASSO, while being orders of
                magnitude faster. For a large data set, having more than
                with \$1.6\$ million training points and about \$12\$
                million features, and with a non-optimized CPU
                implementation, our sparse naive Bayes model can be trained
                in less than 15 seconds.},
  journal    = {arXiv:1905.09884 [cs, stat]},
  author     = {Askari, Armin and d'Aspremont, Alexandre and El Ghaoui,
                Laurent},
  month      = may,
  year       = {2019},
  note       = {arXiv: 1905.09884},
  keywords   = {⛔ No DOI found},
  file       = {arXiv.org
                Snapshot:/Users/zcranko/Documents/Zotero/storage/CXTRJWAD/1905.html:text/html;Askari
                et al - Naive feature selection
                (2019).pdf:/Users/zcranko/Documents/Library/Journal
                Article/Askari et al - Naive feature selection
                (2019).pdf:application/pdf}
}

@inproceedings{Asker94,
  author    = {L. Asker},
  title     = {Improving accuracy of incorrect domain theories},
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  year      = {1994},
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  publisher = {Morgan Kaufmann}
}

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  author    = {Lars Asker and R. Maclin},
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               study in {V}enusian volcano detection},
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  year      = {1997},
  pages     = {3--11},
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}

@inproceedings{Aslam00,
  author    = {Javed A. Aslam},
  title     = {Improving Algorithms for Boosting},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {200--207},
  publisher = {Morgan Kaufmann, San Francisco}
}

@mastersthesis{Aslam92,
  author = {J. A. Aslam},
  title  = {Inferring Graphs from Walks},
  school = {Massachusetts Institute of Technology},
  year   = {1992},
  month  = jan
}

@inproceedings{AslCheZhaSch13,
  author    = {Ozlem Aslan and Hao Cheng and Xinhua Zhang and Dale
               Schuurmans},
  title     = {Convex Two-Layer Modeling},
  booktitle = nips,
  year      = 2013
}

@inproceedings{AslDec93,
  author    = {Javed A. Aslam and Scott E. Decatur},
  title     = {General Bounds on Statistical Query Learning and {PAC}
               Learning with Noise via Hypothesis Boosting},
  booktitle = {34th Symposium on Foundations of Computer Science},
  year      = {1993}
}

@inproceedings{AslDec95,
  author    = {Javed A. Aslam and Scott E. Decatur},
  title     = {Specification and simulation of statistical query
               algorithms for efficiency and noise tolerance},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {437--446},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{AslDha91,
  author    = {J. A. Aslam and A. Dhagat},
  title     = {Searching in the presence of linearly bounded errors},
  booktitle = {Proc. of the 23rd Symposium on Theory of Computing},
  year      = {1991},
  pages     = {486--493},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{AslRiv90,
  author    = {J. A. Aslam and R. L. Rivest},
  title     = {Inferring graphs from walks},
  booktitle = {Proc. 3rd Annu. Workshop on Comput. Learning Theory},
  year      = {1990},
  pages     = {359--370},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@inproceedings{AslZhaSch14,
  author    = {Ozlem Aslan and Xinhua Zhang and Dale Schuurmans},
  title     = {Convex Deep Learning via Normalized Kernels},
  booktitle = nips,
  year      = 2014
}

@article{Asplund_FrechetDifferentiability_1968,
  title   = {Fréchet differentiability of convex functions},
  volume  = {121},
  issn    = {0001-5962},
  doi     = {10.1007/bf02391908},
  journal = {Acta Mathematica},
  author  = {Asplund, Edgar},
  year    = {1968},
  note    = {tex.fjournal: Acta Mathematica tex.mrclass: 46.45
             tex.mrnumber: 231199 tex.mrreviewer: M. Edelstein},
  pages   = {31--47},
  file    = {Asplund - Fréchet differentiability of convex functions
             (1968).pdf:/Users/zcranko/Documents/Library/Journal
             Article/Asplund - Fréchet differentiability of convex
             functions (1968).pdf:application/pdf}
}

@article{Asplund_GradientsConvex_1969,
  title    = {Gradients of convex functions},
  volume   = {139},
  issn     = {0002-9947},
  doi      = {10.1090/s0002-9947-1969-0240621-x},
  language = {en},
  journal  = {Transactions of the American Mathematical Society},
  author   = {Asplund, E. and Rockafellar, Ralph Tyrrell},
  year     = {1969},
  pages    = {443--443},
  file     = {Asplund, Rockafellar - Gradients of convex functions
              (1969).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Asplund, Rockafellar - Gradients of convex
              functions (1969).pdf:application/pdf}
}

@article{Aspremont08,
  author  = {Alexandre d'Aspremont},
  title   = {Smooth Optimization with Approximate Gradient},
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  year    = {2008},
  volume  = {19},
  number  = {3},
  pages   = {1171--1183}
}

@article{Assouad83,
  author  = {P. Assouad},
  title   = {Densit{\'e} et Dimension},
  journal = {Ann. Inst. Fourier},
  year    = {1983},
  pages   = {233--282},
  address = {Grenoble},
  comment = {In French. Survey of properties of VC classes.}
}

@inproceedings{AstCap92,
  author    = {M. Aste and B. Caprile},
  title     = {{Learning Autonomous Navigation Abilities Using Radial
               Basis Functions Networks}},
  booktitle = {{Intelligent Vehicles '92 Symposium}},
  year      = {{1992}},
  address   = {Detroit}
}

@inproceedings{AsuSmyWel08,
  title     = {Asynchronous Distributed Learning of Topic Models},
  author    = {A. Asuncion and P. Smyth and M. Welling},
  publisher = mitpr,
  year      = {2008},
  booktitle = {NIPS},
  editor    = {D. Koller and D. Schuurmans and Y. Bengio and L. Bottou},
  pages     = {81--88},
  url       = {http://books.nips.cc/papers/files/nips21/NIPS2008_0762.pdf}
}

@inproceedings{athalye2018obfuscated,
  title     = {Obfuscated Gradients Give a False Sense of Security:
               Circumventing Defenses to Adversarial Examples},
  author    = {Athalye, Anish and Carlini, Nicholas and Wagner, David},
  booktitle = {Proceedings of the 35th International Conference on
               Machine Learning},
  pages     = {274--283},
  year      = {2018}
}

@inproceedings{AthCarWag18,
  title     = {Obfuscated gradients give a false sense of security:
               Circumventing defenses to adversarial examples},
  author    = {Athalye, Anish and Carlini, Nicholas and Wagner, David},
  booktitle = icml,
  year      = {2018}
}

@article{AthSut17,
  title     = {Synthesizing robust adversarial examples},
  author    = {Athalye, Anish and Sutskever, Ilya},
  booktitle = icml,
  year      = {2017}
}

@article{Atiyah_HowResearch_1974,
  title    = {How research is carried out},
  volume   = {10},
  issn     = {0905-5628},
  journal  = {Bulletin of the Institute of Mathematics and its
              Applications},
  author   = {Atiyah, Michael F.},
  year     = {1974},
  note     = {tex.fjournal:},
  keywords = {⛔ No DOI found},
  pages    = {232--234}
}

@incollection{Atkeson89,
  author    = {C. G. Atkeson},
  title     = {Learning Arm Kinematics and Dynamics},
  booktitle = {Annual Review of Neuroscience},
  publisher = {Annual Reviews},
  year      = {1989},
  volume    = {12},
  pages     = {157--183}
}

@article{AtkMooSch97,
  author  = {C.G. Atkeson and A.W. Moore and S.Schaal},
  title   = {Locally weighted learning},
  journal = {Artificial Intelligence Review},
  year    = {1997},
  volume  = {11},
  number  = {73}
}

@inproceedings{AtkSch97,
  author    = {Christopher G. Atkeson and Stefan Schaal},
  title     = {Robot learning from demonstration},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {12--20},
  publisher = {Morgan Kaufmann}
}

@article{Attias99,
  author  = {Hagai Attias},
  title   = {Independent Factor Analysis},
  journal = {Neural Computation},
  year    = {1999},
  volume  = {11},
  pages   = {803--852},
  number  = {4}
}

@book{Aubin_AppliedNonlinear_1984,
  address   = {Mineola, USA},
  series    = {Pure and applied mathematics},
  title     = {Applied nonlinear analysis},
  isbn      = {0-471-05998-6},
  language  = {eng},
  publisher = {Wiley-Interscience},
  author    = {Aubin, Jean-Pierre and Ekeland, Ivar},
  year      = {1984},
  note      = {OCLC: 794591327},
  file      = {Aubin, Ekeland - Applied nonlinear analysis
               (1984).pdf:/Users/zcranko/Documents/Library/Book/Aubin,
               Ekeland - Applied nonlinear analysis
               (1984).pdf:application/pdf}
}

@book{Aubin_ConvexAnalysis_1982,
  series    = {Research notes in mathematics},
  title     = {Convex analysis and optimization},
  volume    = {57},
  isbn      = {0-273-08547-6},
  publisher = {Pitman (Advanced Publishing Program), Boston,
               Mass.-London},
  editor    = {Aubin, Jean-Pierre and Vinter, Richard B.},
  year      = {1982},
  note      = {tex.mrclass: 49-06 (46N05 58C20) tex.mrnumber: 647475
               Papers presented at a Colloquium held in honor of A. D.
               Ioffe at Imperial College, London, February 28–29, 1980}
}

@article{Aubin_EstimatesDuality_1976,
  title    = {Estimates of the duality gap in nonconvex optimization},
  volume   = {1},
  issn     = {0364765X, 15265471},
  doi      = {10.1287/moor.1.3.225},
  abstract = {[We associate with every real-valued function a number
              which measures its lack of convexity. This number is used
              to estimate the duality gap in optimization problems where
              the criterion and/or the constraints are nonconvex. It is
              shown that when the number of variables is very great with
              respect to the number of constraints, this duality gap is
              small in relative value. Approximating in this way problems
              where the criterion and constraints are given as integrals,
              we show that the duality gap vanishes.]},
  number   = {3},
  journal  = {Mathematics of Operations Research},
  author   = {Aubin, Jean-Pierre and Ekeland, Ivar},
  year     = {1976},
  keywords = {duality gap},
  pages    = {225--245},
  file     = {Aubin, Ekeland - Estimates of the duality gap in nonconvex
              optimization
              (1976).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Aubin, Ekeland - Estimates of the duality gap in
              nonconvex optimization (1976).pdf:application/pdf}
}

@book{Aubin_MathematicalMethods_2014,
  title     = {Mathematical methods of game and economic theory},
  isbn      = {978-0-08-087528-6},
  abstract  = {This book presents a unified treatment of optimization
               theory, game theory and a general equilibrium theory in
               economics in the framework of nonlinear functional
               analysis. It not only provides powerful and versatile tools
               for solving specific problems in economics and the social
               sciences but also serves as a unifying theme in the
               mathematical theory of these subjects as well as in pure
               mathematics itself.},
  language  = {English},
  publisher = {Elsevier Science},
  author    = {Aubin, Jean-Pierre},
  year      = {2014},
  note      = {OCLC: 1058520096},
  file      = {Aubin - Mathematical methods of game and economic theory
               (2014).pdf:/Users/zcranko/Documents/Library/Book/Aubin -
               Mathematical methods of game and economic theory
               (2014).pdf:application/pdf}
}

@book{Aubin_SetvaluedAnalysis_2009,
  address   = {Boston, MA, USA},
  series    = {Modern {Birkhauser} classics},
  title     = {Set-valued analysis},
  isbn      = {978-0-8176-4847-3},
  publisher = {Birkhäuser},
  author    = {Aubin, Jean-Pierre and Frankowska, Hélène},
  year      = {2009},
  file      = {Aubin, Frankowska - Set-valued analysis
               (2009).pdf:/Users/zcranko/Documents/Library/Book/Aubin,
               Frankowska - Set-valued analysis
               (2009).pdf:application/pdf}
}

@article{AueCes98,
  author  = {P. Auer and N. Cesa-Bianchi},
  title   = {On-line learning with malicious noise and the closure
             algorithm},
  journal = {Annals of Mathematics and Artificial Intelligence},
  year    = {1998},
  volume  = {23},
  pages   = {83--99},
  number  = {1/2},
  note    = {Special issue for AII '94 and ALT '94}
}

@inproceedings{AueCesFreSch95,
  author    = {P. Auer and N. Cesa-Bianchi and Yoav Freund and Robert E.
               Schapire},
  title     = {Gambling in a rigged casino: the adversarial multi-armed
               bandit problem},
  booktitle = {Proc. of the 36th Annual Symposium on Foundations of
               Computer Science},
  year      = {1995},
  pages     = {322--331},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@inproceedings{AueGen00,
  author    = {P. Auer and Claudio Gentile},
  title     = {Adaptive and Self-Confident On-Line Learning Algorithms},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {107--117},
  publisher = {Morgan Kaufmann, San Francisco}
}

@inproceedings{AueHerWar96,
  author    = {P. Auer and M. Herbster and M. K. Warmuth},
  title     = {Exponentially many local minima for single neurons},
  booktitle = {Advances in Neural Information Processing Systems 9},
  year      = {1996}
}

@techreport{AueHerWar96b,
  author      = {P. Auer and M. Herbster and M. K. Warmuth},
  title       = {Exponentially many local minima for single neurons},
  institution = {Univ. of Calif.Computer Research Lab},
  year        = {1996},
  number      = {UCSC-CRL-96-1},
  address     = {Santa Cruz, CA},
  note        = {In preparation}
}

@inproceedings{AueHolMaa95,
  author    = {P. Auer and Robert C. Holte and Wolfgang Maass},
  title     = {Theory and Applications of Agnostic {PAC}-Learning with
               Small Decision Trees},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {21--29},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{AueKweMaaWar96,
  author    = {P. Auer and Stephen Kwek and Wolfgang Maass and M. K.
               Warmuth},
  title     = {Learning of depth two neural networks with constant fan-in
               at the hidden nodes},
  booktitle = {Proc. 9th Annu. Conf. on Comput. Learning Theory},
  year      = {1996},
  pages     = {333--343},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{AueLon94,
  author    = {P. Auer and P. M. Long},
  title     = {Simulating Access to hidden information while learning},
  booktitle = {Proc. of Twenty-sixth ACM Symposium on Theory of
               Computing},
  year      = {1994},
  pages     = {263--272}
}

@article{AueLon99,
  author    = {P. Auer and P. M. Long},
  title     = {Structural Results About On-line Learning Models With and
               Without Queries},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {36},
  pages     = {147--181},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{AueLonMaaWoe95,
  author    = {P. Auer and P. M. Long and W. Maass and G. J. Woeginger},
  title     = {On the complexity of function learning},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {18},
  pages     = {187--230},
  number    = {2/3},
  annote    = {First appeared in 6th COLT, 1993},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Auer00,
  author    = {P. Auer},
  title     = {An Improved On-line Algorithm for Learning Linear
               Evaluation Functions},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {118--125},
  publisher = {Morgan Kaufmann, San Francisco}
}

@inproceedings{Auer00b,
  author    = {P. Auer},
  title     = {Using Upper Confidence Bounds for Online Learning},
  booktitle = {Proc. 41st Annual Symposium on Foundations of Computer
               Science},
  year      = {2000},
  pages     = {270--279},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@article{auer2002finite,
  title     = {Finite-time analysis of the multiarmed bandit problem},
  author    = {Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
  journal   = {Machine learning},
  volume    = {47},
  number    = {2-3},
  pages     = {235--256},
  year      = {2002},
  publisher = {Springer}
}

@inproceedings{Auer93,
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  booktitle = nips,
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@book{Bacak_ConvexAnalysis_2014,
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  title     = {Convex analysis and optimization in {Hadamard} spaces},
  isbn      = {978-3-11-036103-2},
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@techreport{BacFinSha99,
  author      = {R. Bachrach and S. Fine and E. Shamir},
  title       = {Query by Committee, Linear Separation and Random Walks},
  institution = {The Hewbrew University},
  year        = {1999}
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@inproceedings{Bach10,
  author    = {Francis Bach},
  title     = {Structured Sparsity-Inducing Norms through Submodular
               Functions},
  booktitle = nips,
  year      = {2010}
}

@techreport{Bach10b,
  author = {Francis Bach},
  title  = {Convex Analysis and Optimization with Submodular
            Functions: a Tutorial},
  year   = {2010},
  number = {HAL 00527714}
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@techreport{Bach11,
  author = {F. Bach},
  title  = {Learning with Submodular Functions: A Convex Optimization
            Perspective},
  year   = {2011},
  number = {HAL 00645271}
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@techreport{Bach12,
  author = {F. Bach},
  title  = {Duality between Subgradient and Conditional Gradient
            Methods},
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  number = {HAL 00757696}
}

@article{Bach13,
  author  = {F. Bach},
  journal = {Foundations and Trends in Machine Learning},
  pages   = {145--373},
  title   = {Learning with Submodular Functions: A Convex Optimization
             Perspective},
  volume  = {6},
  number  = {2--3},
  year    = {2013}
}

@unpublished{Bach13b,
  author = {Francis Bach},
  title  = {Convex relaxations of structured matrix factorizations},
  note   = {HAL:00861118},
  year   = {2013}
}

@unpublished{Bach14,
  author = {F. Bach},
  title  = {Breaking the Curse of Dimensionality with Convex Neural
            Networks},
  note   = { HAL-01098505-v2},
  year   = {2014}
}

@article{Bach15,
  author = {Francis Bach},
  title  = {Duality Between Subgradient and Conditional Gradient
            Methods},
  volume = {25},
  number = {1},
  pages  = {115--129},
  year   = { 2015}
}

@article{Bach17,
  year    = {2017},
  journal = jmlr,
  title   = {Breaking the Curse of Dimensionality with Convex Neural
             Networks},
  author  = {Francis Bach},
  volume  = {18},
  number  = {19},
  pages   = {1--53}
}

@article{BacHasWanNaretal99,
  author  = {L.K. Bachrach and T. Hastie and M.C. Wang and B.
             Narashimhan and R. Marcus},
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  school  = {Department of Electrical Engineering and Computer Science,
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@unpublished{Bachetal08,
  author = {Francis Bach and Julien Mairal and Jean Ponce},
  title  = {Convex Sparse Matrix Factorizations},
  note   = {arXiv:0812.1869v1},
  year   = {2008}
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@article{Bachetal12,
  author  = {Francis Bach and Rodolphe Jenatton and Julien Mairal and
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  number  = {4},
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  year    = {2012}
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@techreport{bachjordan06,
  author      = {Francis Bach and Michael Jordan},
  title       = {A probabilistic interpretation of canonical correlation
                 analysis},
  institution = {Department of Statistics, University of California,
                 Berkeley},
  year        = {2006},
  number      = {688}
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  author    = {Bach, Stephen H. and Huang, Bert and London, Ben and Lise
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@article{BacJenMaiObo12,
  author  = {Francis Bach and Rodolphe Jenatton and Julien Mairal and
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  title   = {Optimization with Sparsity-inducing Penalties},
  journal = {Foundations and Trends in Machine Learning},
  year    = {2012},
  volume  = {4},
  pages   = {1--106},
  number  = {1}
}

@techreport{BacJor01,
  author      = {F.~R. Bach and M.~I. Jordan},
  title       = {Kernel Independent Component Analysis},
  institution = {Computer Science Division ({EECS})},
  year        = {2001},
  number      = {{UCB/CSD-01-1166}},
  address     = {University of Berkeley, CA}
}

@article{BacJor02,
  author  = {F.~R. Bach and M.~I. Jordan},
  title   = {Kernel Independent Component Analysis},
  journal = {J. Mach. Learn. Res.},
  year    = {2002},
  volume  = {3},
  pages   = {1--48}
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@inproceedings{BacJor02b,
  author    = {F. Bach and M. Jordan},
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  year      = {2002},
  volume    = {18}
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@inproceedings{BacJor02c,
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  title     = {Thin junction trees},
  booktitle = {NIPS},
  year      = {2002}
}

@article{BacJor04,
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  year    = {2004}
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@inproceedings{BacLanJor04,
  author    = {F.R. Bach and G.R.G. Lanckriet and M.I. Jordan},
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               algorithm},
  booktitle = {International Conference on Machine Learning},
  year      = {2004},
  editor    = {C.E. Brodley}
}

@inproceedings{BacLes11,
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  booktitle    = wsdm,
  pages        = {635--644},
  year         = {2011},
  organization = {ACM}
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@unpublished{BacMaiPon08,
  author = {Francis Bach and Julien Mairal and Jean Ponce},
  title  = {Convex Sparse Matrix Factorizations},
  note   = {arXiv:0812.1869v1},
  year   = 2008
}

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@inproceedings{BalCasMerSte00,
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@article{Banerji85,
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@inbook{Banerji97,
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@inproceedings{BanVicBraHaretal96,
  author    = {Cesar Bandera and Francisco J. Vico and Jose M. Bravo and
               Mance E. Harmon and Leemon C. Baird III},
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  year      = {1996},
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@article{Bar-Joseph04,
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  year    = {2004},
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@article{Barahona82,
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  pages   = {509-512}
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  year      = {1988},
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@article{BarBar91,
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@inproceedings{BarBar93,
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  volume    = {659},
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  pages     = {132--140},
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@article{Barbara_ConcaveGauge_1994,
  title    = {Concave gauge functions and applications},
  volume   = {40},
  issn     = {1432-5217},
  doi      = {10.1007/bf01414029},
  abstract = {Many problems of optimization involve the minimization of
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              define a concave gauge function which will be used in
              interior point methods.},
  number   = {1},
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  author   = {Barbara, Abdessamad and Crouzeix, Jean-Pierre},
  month    = mar,
  year     = {1994},
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               n}{\u s} and Kalvis Aps{\={\i}}tis and U{\u g}is Sarkans},
  title     = {Towards efficient inductive synthesis of expressions from
               input/output examples},
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  year      = {1993},
  volume    = {744},
  series    = {Lecture Notes in Artificial Intelligence},
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@inproceedings{BarBax00,
  author    = {Peter L. Bartlett and Jonathan Baxter},
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               Reinforcement Learning},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {133--141},
  publisher = {Morgan Kaufmann, San Francisco}
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@unpublished{BarBaxLonWil97,
  author = {P.~L. Bartlett and J. Baxter and P.~M. Long and R.~C.
            Williamson},
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            Learning Nonconvex Classes of Functions},
  note   = {In preparation},
  year   = {1997}
}

@inproceedings{BarBen99,
  author    = {P. Bartlett and S. Ben-David},
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               Problems},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1999},
  editor    = {P. Fischer and H.~U. Simon},
  volume    = {1572},
  series    = {LNAI},
  pages     = {50--62},
  address   = {Berlin},
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}

@article{BarBenKul00,
  author    = {Peter L. Bartlett and Shai Ben-{D}avid and Sanjeev R.
               Kulkarni},
  title     = {Learning Changing Concepts by Exploiting the Structure of
               Change},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {41},
  pages     = {153--174},
  number    = {2},
  note      = {first in COLT '96},
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@inproceedings{BarBenKul96,
  author    = {Peter L. Bartlett and Shai Ben-David and Sanjeev R.
               Kulkarni},
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  booktitle = {Proc. 9th Annu. Conf. on Comput. Learning Theory},
  year      = {1996},
  pages     = {131--139},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BarBouLug00,
  author    = {Peter L. Bartlett and St{\'e}phane Boucheron and G{\'a}bor
               Lugosi},
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  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {286--297},
  publisher = {Morgan Kaufmann, San Francisco}
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@inproceedings{BarBouMen02,
  author    = {P. L. Bartlett and O. Bousquet and S. Mendelson},
  title     = {Localized {Rademacher} Averages},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2002},
  pages     = {44-58}
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@article{BarBra99,
  author  = {O. Barzilay and V.~L. Brailovsky},
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  journal = {Pattern Recognition Letters},
  year    = {1999},
  volume  = {20},
  pages   = {475--484}
}

@article{BarBroOzdBosetal05,
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             Jurisica and J. L. Wrana},
  title   = {High-throughput mapping of a dynamic signaling network in
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  number  = {5715},
  pages   = {1621-1625},
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  year    = {2005}
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@book{Barbu_ConvexityOptimization_2012,
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  series    = {Springer {Monographs} in {Mathematics}},
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  author    = {Barbu, Viorel and Precupanu, Teodor},
  year      = {2012},
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@inproceedings{BarClaHau93,
  author    = {A. Barron and B. Clarke and D. Haussler},
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  year      = {1993},
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@article{BarCov91,
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@article{BarCov91b,
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  year    = {1991},
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@article{BarDuyFreForetal03,
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  title   = {Matching Words and Pictures},
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@article{Bareiss91,
  author    = {Ray Bareiss},
  title     = {A Reply to Reich's Book Review of Exemplar-Based Knowledge
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  journal   = {Machine Learning},
  year      = {1991},
  volume    = {6},
  pages     = {105--106},
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  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {318--327},
  publisher = {ACM Press, New York, NY}
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@book{BarFlo89,
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  publisher = {deGryter},
  year      = {1989},
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@article{BarFre72,
  author  = {J. M. Barzdin and R. V. Freivald},
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  journal = {Soviet Math. Doklady},
  year    = {1972},
  volume  = {13},
  pages   = {1224--1228},
  comment = {Introduces the on-line mistake bound learning model
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@article{BarFre74,
  author  = {J. M. Barzdin and R. Freivalds},
  title   = {Prediction and limiting synthesis of recursively
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  year    = {1974},
  volume  = {210},
  pages   = {101--111}
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@inproceedings{BarFreSmi96,
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               Freivalds and Carl H. Smith},
  title     = {Learning with Confidence},
  booktitle = {Proc. 13th Annual Symposium on Theoretical Aspects of
               Computer Science: STACS '96},
  year      = {1996},
  volume    = {1046},
  series    = {Lecture Notes in Computer Science},
  pages     = {207--218},
  publisher = {Springer-Verlag}
}

@inproceedings{BarFreSmi97,
  author    = {J{\=a}nis B{\=a}rzdi{\c n}{\u s} and R{\=u}si{\c n}s
               Freivalds and Carl H. Smith},
  title     = {Learning formulae from elementary facts},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  pages     = {272--285},
  publisher = {Springer-Verlag}
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@article{BarGinMat99,
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  volume  = {27},
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  pages   = {1009-1071}
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@article{BarGioKaiNutetal96,
  author    = {C. Baroglio and A. Giordana and M. Kaiser and M. Nuttin
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  year      = {1996},
  volume    = {23},
  pages     = {221--249},
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@inproceedings{BarGur06,
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  title     = {Random sampling from a search engine's index}
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@inproceedings{BarHazRak08,
  author    = {P.L.~Bartlett and E. Hazan and A. Rakhlin},
  title     = {Adaptive Online Gradient Descent},
  booktitle = nips20,
  editor    = nips20e,
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@inproceedings{BarHerSheWei03,
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               Weinshall},
  title     = {Learning distance functions using equivalence relation},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2003}
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@incollection{BarKinPod74,
  author    = {J. M. Barzdin and E. B. Kinber and K. M. Podnieks},
  title     = {Concerning synthesis and prediction of functions},
  booktitle = {Theory of Algorithms and Programs},
  publisher = {Latvian State University, Riga},
  year      = {1974},
  volume    = {1},
  pages     = {117--128}
}

@article{BarKulPos97,
  author  = {P.~L. Bartlett and S.~R. Kulkarni and S.~E. Posner},
  title   = {Covering numbers for real-valued function classes},
  journal = {IEEE Transactions on Information Theory},
  year    = {1997},
  volume  = {43},
  pages   = {1721--1724},
  number  = {5}
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@mastersthesis{Barland92,
  author = {I. Barland},
  title  = {Some ideas on learning with directional feedback},
  school = {UC Santa Cruz},
  year   = {1992},
  month  = jun
}

@inproceedings{BarLinLug97,
  author    = {P. Bartlett and T. Linder and G. Lugosi},
  title     = {The minimax distortion redundancy in empirical quantizer
               design},
  booktitle = {the European Conference on Computational Learning Theory},
  year      = {1997}
}

@article{BarLinLug98,
  author    = {Peter L. Bartlett and Tam\`as Lindner and G\`abor Lugosi},
  title     = {The Minimax Distorsion Redundancy in Empirical Quantizer
               Design},
  journal   = {IEEE Transactions on Information Theory},
  year      = {1998},
  volume    = {44},
  pages     = {1802--1813},
  publisher = {IEEE}
}

@inproceedings{BarLon95,
  author    = {P.~L. Bartlett and P.~M. Long},
  title     = {More Theorems about Scale-Sensitive Dimensions and
               Learning},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1995},
  pages     = {392--401},
  address   = {New York},
  publisher = {ACM Press}
}

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  author    = {Rakesh D. Barve and P. M. Long},
  title     = {On the complexity of learning from drifting
               distributions},
  booktitle = {Proc. 9th Annu. Conf. on Comput. Learning Theory},
  year      = {1996},
  pages     = {122--130},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BarLonWil94,
  author    = {P. L. Bartlett and P. M. Long and R.~C. Williamson},
  title     = {Fat-shattering and the learnability of real-valued
               functions},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {299--310},
  publisher = {ACM Press, New York, NY}
}

@article{BarLonWil96,
  author  = {P. Bartlett and P. M. Long and R.~C. Williamson},
  title   = {Fat-shattering and the learnability of real-valued
             functions},
  journal = {J. Comput. System Sci.},
  year    = {1996},
  volume  = {52},
  pages   = {434--452},
  number  = {3}
}

@article{BarLonWil96b,
  author  = {P. L. Bartlett and P. Long and R.~C. Williamson},
  title   = {Fat-Shattering and the Learnability of Real-Valued
             Functions},
  journal = {Journal of Computer and System Sciences},
  year    = {1996},
  volume  = {52},
  pages   = {434--452},
  number  = {3}
}

@article{BarLug99,
  author  = {Peter Bartlett and G\`abor Lugosi},
  title   = {An inequality for uniform deviations of sample averages
             from their means},
  journal = {Statistics and Probability Letters},
  year    = {1999},
  volume  = {44},
  pages   = {55--62}
}

@article{BarMah86,
  author  = {Francisco Barahona and Ali Ridha Mahjoub},
  title   = {On the Cut Polytope},
  journal = {Mathematical Programming},
  year    = {1986},
  volume  = {36},
  number  = {2},
  pages   = {157--173}
}

@article{BarMen02,
  author  = {P. L. Bartlett and S. Mendelson},
  title   = {Rademacher and {G}aussian Complexities: Risk Bounds and
             Structural Results},
  journal = {J. Mach. Learn. Res.},
  year    = {2002},
  volume  = {3},
  pages   = {463-482}
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@article{BarMouRioCaietal05,
  author  = {S. E. Baranzini and P. Mousavi and J. Rio and S. J.
             Caillier and A. Stillman and P. Villoslada and M. M. Wyatt
             and M. Comabella and L. D. Greller and R. Somogyi and X.
             Montalban and J. R. Oksenberg},
  title   = {Transcription-based prediction of response to {IFNbeta}
             using supervised computational methods},
  journal = {{PLoS} Biology},
  year    = {2005},
  volume  = {3},
  pages   = {e2},
  number  = {1},
  month   = {Jan},
  address = {Department of Neurology, School of Medicine University of
             California, San Francisco, USA.}
}

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  title     = {Information and Exponential Families in Statistical
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  publisher = {John Wiley and Sons},
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}

@book{Barndorff-Nielsen_InformationExponential_2014,
  address    = {Chichester},
  title      = {Information and exponential families: in statistical
                theory},
  isbn       = {978-1-118-85750-2},
  shorttitle = {Information and exponential families},
  language   = {eng},
  publisher  = {Wiley},
  author     = {Barndorff-Nielsen, Ole},
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  keywords   = {link functions},
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@mastersthesis{Barnes99,
  author = {J. Barnes},
  title  = {Capacity Control in Boosting using a $p$-Convex Hull},
  school = {Department of Engineering, Australian National
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  year   = {1999}
}

@article{barocas2016big,
  title     = {Big data's disparate impact},
  author    = {Barocas, Solon and Selbst, Andrew D},
  journal   = {Cal. L. Rev.},
  volume    = {104},
  pages     = {671},
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  publisher = {HeinOnline}
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@inproceedings{BarOdoVer02,
  author    = {A. Barla and F. Odone and A. Verri},
  title     = {Hausdorff Kernel for {3D} Object Acquisition and
               Detection},
  booktitle = {European Conference on Computer Vision '02},
  year      = {2002},
  number    = {2353},
  series    = {LNCS},
  pages     = {20},
  publisher = {Springer}
}

@inproceedings{BarPalPetPonetal99,
  author    = {N. Barabino and M. Pallavicini and A. Petrolini and M.
               Pontil and A. Verri},
  title     = {Support Vector Machines vs Multi-Layer Perceptrons in
               Particle Identification},
  booktitle = {Proceedings ESANN},
  year      = {1999},
  editor    = {M. Verleysen},
  pages     = {257--262},
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  publisher = {D Facto}
}

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  author    = {J. M. Barzdin and K. M. Podnieks},
  title     = {Towards a theory of inductive inference},
  booktitle = {Proc. 2nd Conf. Mathematical Foundations of Computer
               Science},
  year      = {1973},
  pages     = {9--15},
  publisher = {Math. Inst. of the Slovak Academy of Sciences}
}

@article{BarPorMur89,
  author    = {Ray Bareiss and Bruce W. Porter and Kenneth S. Murray},
  title     = {Supporting Start-to-Finish Development of Knowledge
               Bases},
  journal   = {Machine Learning},
  year      = {1989},
  volume    = {4},
  pages     = {259--283},
  publisher = {Kluwer Academic Publishers, Boston}
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@incollection{Barron84,
  author    = {A. Barron},
  title     = {Predicted squared error: a criterion for automatic model
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  publisher = {Marcel Dekker},
  year      = {1984},
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  address   = {New York}
}

@inproceedings{Barron89,
  author    = {A.~R. Barron},
  title     = {Statistical properties of artificial neural networks},
  booktitle = {the 28th Conference on Decision and Control},
  year      = {1989},
  pages     = {280--285},
  address   = {Tampa, Florida},
  month     = dec
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@inproceedings{Barron90,
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  title     = {Complexity Regularization with Applications to Artificial
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  year      = {1990 },
  editor    = {G. Roussa},
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  publisher = {Kluwer Academic}
}

@techreport{Barron91,
  author      = {A.~R. Barron},
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                 sigmoidal function},
  institution = {Department of Statistics, University of Illinois at
                 Urbana-Champaign},
  year        = {1991},
  type        = {Technical Report},
  number      = {58},
  address     = {Champaign, IL},
  month       = mar
}

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  author      = {A.~R. Barron},
  title       = {Approximation and estimation bounds for artificial neural
                 networks},
  institution = {Department of Statistics, University of Illinois at
                 Urbana-Champaign},
  year        = {1991},
  type        = {Technical Report},
  number      = {59},
  address     = {Champaign, IL},
  month       = mar
}

@techreport{Barron91c,
  author      = {A.~R. Barron},
  title       = {Complexity regularization with application to artificial
                 neural networks},
  institution = {Department of Statistics, University of Illinois at
                 Urbana-Champaign},
  year        = {1991},
  type        = {Technical Report},
  number      = {57},
  address     = {Champaign, IL},
  month       = mar
}

@inproceedings{Barron92,
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  title     = {Neural Net Approximation},
  booktitle = {Proc.~7th Yale Workshop on Adaptive and Learning Systems},
  year      = {1992 }
}

@article{Barron93,
  author  = {A.~R. Barron},
  title   = {Universal approximation bounds for superpositions of a
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  year    = {1993},
  volume  = {39},
  pages   = {930--945},
  number  = {3},
  month   = may
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@article{Barron94,
  author  = {A.~R. Barron},
  title   = {Approximation and estimation bounds for artificial neural
             networks},
  journal = {Machine Learning},
  year    = {1994},
  volume  = {14},
  pages   = {115--133}
}

@inproceedings{Barron97,
  author    = {Andrew R. Barron},
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               and neural nets},
  booktitle = {Proc. 10th Annu. Conf. on Comput. Learning Theory},
  year      = {1997},
  pages     = {1},
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  author    = {J{\=a}nis B{\=a}rzdi{\c n}{\v s} and U{\v g}is Sarkans},
  title     = {Incorporating hypothetical knowledge into the process of
               inductive synthesis},
  booktitle = alt-ws,
  year      = {1996},
  volume    = {1160},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {156--168},
  publisher = {Springer-Verlag}
}

@incollection{BarSha98,
  author    = {Peter Bartlett and John Shawe-Taylor},
  title     = {Generalization Performance of Support Vector Machines and
               other Pattern Classifiers},
  booktitle = {Advances in Kernel Methods\,---\,Support Vector Learning},
  publisher = {{MIT} Press},
  year      = {1998},
  pages     = {43--54}
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  author    = {P.~L. Bartlett and J. Shawe-Taylor},
  title     = {Generalization Performance of Support Vector Machines and
               Other Pattern Classifiers},
  booktitle = {Advances in Kernel Methods\,---\,Support Vector Learning},
  year      = {1999},
  editor    = {B. Sch{\"o}lkopf and C.~J.~C. Burges and A. J. Smola},
  pages     = {43--54},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@techreport{BarSut81,
  author      = {Andrew G. Barto and R. S. Sutton},
  title       = {Goal Seeking Components for Adaptive Intelligence: An
                 Initial Assessment},
  institution = {Air Force Wright Aeronautical Laboratories},
  year        = {1981},
  number      = {AFWAL-TR-81-1070},
  address     = {Wright-Patterson AFB, Ohio 45433, USA}
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@article{BarSuzTroWiletal05,
  author  = {T. Barrett and T. O. Suzek and D. B. Troup and S. E.
             Wilhite and W. C. Ngau and P. Ledoux and D. Rudnev and A.
             E. Lash and W. Fujibuchi and R. Edgar},
  title   = {{NCBI GEO}: mining millions of expression
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  journal = {Nucleic Acids Research},
  year    = {2005},
  volume  = {33},
  pages   = {D562-D566},
  number  = {Database issue},
  month   = {Jan},
  address = {National Center for Biotechnology Information, National
             Library of Medicine, National Institutes of Health, 45
             Center Drive, Bethesda, MD, USA.}
}

@article{bartlett1996fat,
  title     = {Fat-shattering and the learnability of real-valued
               functions},
  author    = {Bartlett, Peter L and Long, Philip M and Williamson,
               Robert C},
  journal   = {journal of computer and system sciences},
  volume    = {52},
  number    = {3},
  pages     = {434--452},
  year      = {1996},
  publisher = {Elsevier}
}

@inproceedings{bartlett2001rademacher,
  title        = {Rademacher and Gaussian complexities: Risk bounds and
                  structural results},
  author       = {Bartlett, Peter L and Mendelson, Shahar},
  booktitle    = {International Conference on Computational Learning
                  Theory},
  pages        = {224--240},
  year         = {2001},
  organization = {Springer}
}

@inproceedings{bartlett2017spectrally,
  title     = {Spectrally-normalized margin bounds for neural networks},
  author    = {Bartlett, Peter L and Foster, Dylan J and Telgarsky, Matus
               J},
  booktitle = {Advances in Neural Information Processing Systems},
  pages     = {6240--6249},
  year      = {2017}
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@inproceedings{Bartlett92,
  author    = {P. L. Bartlett},
  title     = {Learning with a slowly changing distribution},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {243--252},
  publisher = {ACM Press, New York, NY}
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@inproceedings{Bartlett93,
  author    = {P. Bartlett},
  title     = {Lower bounds on the {V}apnik-{C}hervonenkis dimension of
               multi-layer threshold networks},
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  year      = {1993},
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  publisher = {ACM Press, New York, NY}
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@inproceedings{Bartlett97,
  author    = {P.~L. Bartlett},
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               Important than the Size of the Network},
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  year      = {1997},
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               Mozer and M.~I. Jordan and T. Petsche},
  pages     = {134},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
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@article{Bartlett98,
  author    = {Peter L. Bartlett},
  title     = {The sample complexity of pattern classification with
               neural networks: The size of the weights is more important
               than the size of the network},
  journal   = {IEEE Transactions on Information Theory},
  year      = {1998},
  volume    = {44},
  pages     = {525--536},
  number    = {2},
  publisher = {IEEE}
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@incollection{Bartlett_LargeMargin_2004,
  title     = {Large margin classifiers: convex loss, low noise, and
               convergence rates},
  booktitle = {Advances in neural information processing systems 16},
  publisher = {MIT Press},
  author    = {Bartlett, Peter L. and Jordan, Michael I. and Mcauliffe,
               Jon D.},
  editor    = {Thrun, S. and Saul, L. K. and Schölkopf, B.},
  year      = {2004},
  keywords  = {\#nosource},
  pages     = {1173--1180},
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  author  = {Peter L. Bartlett and Mikhail Traskin},
  title   = {AdaBoost is Consistent},
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  volume  = {8},
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  year    = {2007}
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  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {24--32},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
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  author  = {P.~L. Bartlett and R.~C. Williamson},
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             networks with discrete inputs},
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  year    = {1996},
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  pages   = {625--628},
  number  = {3}
}

@inproceedings{BarWil97,
  author    = {D. Barber and C.K.I. Williams},
  title     = {Gaussian Processes for {B}ayesian Classification via
               {Hybrid} {Monte} {Carlo}},
  booktitle = {Advances in Neural Information Processing Systems 9},
  editor    = {M.~C. Mozer and M.~I. Jordan and T. Petsche},
  publisher = {{MIT} Press},
  year      = {1997},
  pages     = {340-346}
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  author  = {J. M. Barzdin},
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  journal = {Dokl.\ Akad.\ Nauk.\ SSSR},
  year    = {1970},
  pages   = {1048--1051},
  vol     = {190}
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  title   = {Prognostication of automata and functions},
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@incollection{Barzdin74,
  author    = {J. M. Barzdin},
  title     = {{\"U}ber eine {E}igenschaft Limes berechenbarer
               {F}unktionale},
  booktitle = {Theory of Algorithms and Programs},
  publisher = {Latvian State University, Riga},
  year      = {1974},
  volume    = {1},
  pages     = {20--24}
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@article{Barzdin74b,
  author  = {J. M. Barzdin},
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  year    = {1974},
  volume  = {210},
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  author    = {J. M. Barzdin},
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  year      = {1974},
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  author    = {J. M. Barzdin},
  title     = {Two Theorems on the Limiting Synthesis of Functions},
  booktitle = {Theory of Algorithms and Programs},
  publisher = {Latvian State University, Riga},
  year      = {1974},
  volume    = {1},
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  title     = {Inductive Inference of Automata, Functions and Programs},
  booktitle = {Amer.\ Math.\ Soc.\ Transl.},
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  annote    = {Was also in Int. Math. Congress in 1974}
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@inproceedings{BatKumRub04,
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  booktitle    = {STOC'04},
  year         = {2004},
  pages        = {381-390},
  organization = {ACM}
}

@article{Battiti89,
  author  = {R. Battiti},
  title   = {Accelerated Backpropagation Learning: Two Optimization
             Methods},
  journal = {Complex Systems},
  year    = {1989},
  volume  = {3},
  pages   = {331--342},
  number  = {4},
  month   = aug,
  comment = {Gets one order of magnitude improvement over ordinary
             backpropagation using variable step size and a version of
             the conjugate gradient method.}
}

@article{BauAno00,
  author  = {G. Baudat and F. Anouar},
  title   = {Generalized Discriminant Analysis Using a Kernel
             Approach},
  journal = {Neural Computation},
  year    = {2000},
  volume  = {12},
  pages   = {2385--2404}
}

@inproceedings{BauBonGar95,
  author    = {Eric B. Baum and Dan Boneh and Charles Garrett},
  title     = {On genetic algorithms},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {230--239},
  publisher = {ACM Press, New York, NY}
}

@inbook{BauBor01,
  chapter   = {Joint and Separate Convexity of the {B}regman Distance},
  pages     = {23-36},
  title     = {Inherently Paraller Algorithms in Feasibility and
               Optimization and Their Applications},
  publisher = {Elsevier},
  year      = {2001},
  author    = {H. H. Bauschke and J. M. Borwein},
  number    = {8},
  series    = {Studiesn in Computational Mathematics},
  note      = {D. Butnariu and Y. Censor and S. Reich editors}
}

@article{BauBor97,
  author  = {H. H. Bauschke and J. M. Borwein},
  title   = {Legendre Functions and the Method of Random {B}regman
             Projections},
  journal = {Journal of Convex Analysis},
  year    = {1997},
  volume  = {4},
  pages   = {27-67},
  number  = {1}
}

@article{BauBorCom01,
  author  = {H. H. Bauschke and J. M. Borwein and P. L. Combettes},
  title   = {Essential Smoothness and Essential Strict Convexity and
             {L}egendre Functions in {B}anach Spaces},
  journal = {Communications in Contemporary Mathematics},
  year    = {2001},
  volume  = {3},
  pages   = {615-647},
  number  = {4}
}

@book{BauCom11,
  title     = {Convex Analysis and Monotone Operator Theory in Hilbert
               Spaces},
  publisher = {Springer},
  year      = {2011},
  author    = {H. H. Bauschke and P. L. Combettes}
}

@inproceedings{BauHau88,
  author    = {E.~B. Baum and D. Haussler},
  title     = {What Size Net Gives Valid Generalization?},
  booktitle = {IEEE Intl.\ Symp.\ Inform.\ Theory},
  year      = {1988},
  address   = {Kobe, Japan},
  month     = jun
}

@inproceedings{BauHau89,
  author    = {E.~B. Baum and D. Haussler},
  title     = {What Size Net Gives Valid Generalization?},
  booktitle = {Advances in Neural Information Processing Systems 2},
  year      = {1989},
  editor    = {D.~S. Touretzky},
  pages     = {81-90},
  publisher = {Morgan Kaufmann Publishers}
}

@article{BauHau89b,
  author  = {E.~B. Baum and D. Haussler},
  title   = {What Size Net Gives Valid Generalization?},
  journal = {Neural Computation},
  year    = {1989},
  volume  = {1},
  pages   = {151--160}
}

@article{BauKoh97,
  author  = {E. Bauer and R. Kohavi},
  title   = {An empirical comparison of voting classification
             algorithms: Bagging, boosting, and variants},
  journal = {Machine Learning},
  year    = {1997},
  note    = {To Appear}
}

@article{BauKoh99,
  author  = {E. Bauer and R. Kohavi},
  title   = {An empirical comparison of voting classification
             algorithms: Bagging, boosting, and variants},
  journal = {Machine Learning},
  year    = {1999},
  volume  = {36},
  pages   = {105--139},
  number  = {1}
}

@article{Baum72,
  author  = {L. E. Baum},
  title   = {An inequality and associated maximization technique in
             statistical estimation of probabilistic functions of a
             {M}arkov process},
  journal = {Inequalities},
  year    = {1972},
  volume  = {3},
  pages   = {1--8}
}

@article{Baum88,
  author  = {E.~B. Baum},
  title   = {On the Capabilities of Multilayer Perceptrons},
  journal = {J. Complexity},
  year    = {1988},
  volume  = {4},
  pages   = {193--215}
}

@article{Baum90,
  author  = {E.~B. Baum},
  title   = {The Perceptron Algorithm is Fast for Non-malicious
             Distributions},
  journal = {Neural Computation},
  year    = {1990},
  volume  = {2},
  pages   = {248--260}
}

@article{Baum90b,
  author  = {E. B. Baum},
  title   = {The Perceptron Algorithm is Fast for Nonmalicious
             Distributions},
  journal = {Neural Computation},
  year    = {1990},
  volume  = {2},
  pages   = {248--260}
}

@inproceedings{Baum90c,
  author    = {E. B. Baum},
  title     = {Polynomial time algorithms for learning neural nets},
  booktitle = {Proc. 3rd Annu. Workshop on Comput. Learning Theory},
  year      = {1990},
  pages     = {258--272},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{Baum90d,
  author  = {E. Baum},
  title   = {A polynomial time algorithm that learns two hidden net
             units},
  journal = {Neural Computation},
  year    = {1990},
  volume  = {2},
  pages   = {510--522}
}

@article{Baum91,
  author  = {E.~B. Baum},
  title   = {Neural Networks which learn in polynomial time from
             examples and queries},
  journal = {IEEE Transactions on Neural Networks},
  year    = {1991},
  volume  = {2},
  pages   = {5--19},
  number  = {1}
}

@inbook{Baum94,
  chapter   = {14},
  pages     = {415--442},
  title     = {When are k-nearest neighbor and backpropagation accurate
               for feasible-sized sets of examples?},
  publisher = {{MIT} Press},
  year      = {1994},
  author    = {Eric. B. Baum},
  volume    = {I: Constraints and Prospects},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@inproceedings{Baum96,
  author    = {Eric B. Baum},
  title     = {Toward a model of mind as a laissez-faire economy of
               idiots},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {28--36},
  publisher = {Morgan Kaufmann}
}

@article{Baum99,
  author    = {Eric B. Baum},
  title     = {Toward a Model of Intelligence as an Economy of Agents},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {35},
  pages     = {155--185},
  number    = {2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{BauMooWil88,
  author  = {E.~B. Baum and J. Moody and F. Wilczek},
  title   = {Internal representations for associative memory},
  journal = {Biological Cybernetics},
  year    = {1988},
  volume  = {59},
  pages   = {217--228}
}

@article{Bauschke03,
  author  = {H. H. Bauschke},
  journal = {Journal of Approximation Theory},
  pages   = {1--12},
  title   = {Duality for {Bregman} projections onto translated cones
             and affine subspaces},
  volume  = {121},
  number  = {1},
  year    = {2003}
}

@book{Bauschke_ConvexAnalysis_2011,
  address   = {New York, NY, USA},
  series    = {{CMS} books in mathematics},
  title     = {Convex analysis and monotone operator theory in {Hilbert}
               spaces},
  isbn      = {978-1-4419-9466-0},
  publisher = {Springer Science+Business Media},
  author    = {Bauschke, Heinz H. and Combettes, Patrick L.},
  year      = {2011},
  file      = {Bauschke, Combettes - Convex analysis and monotone
               operator theory in Hilbert spaces
               (2011).pdf:/Users/zcranko/Documents/Library/Book/Bauschke,
               Combettes - Convex analysis and monotone operator theory in
               Hilbert spaces (2011).pdf:application/pdf}
}

@article{Bauschke_EssentialSmoothness_2001,
  title    = {Essential smoothness, essential strict convexity and
              legendre functions in banach spaces},
  volume   = {03},
  issn     = {0219-1997},
  doi      = {10.1142/s0219199701000524},
  abstract = {The classical notions of essential smoothness, essential
              strict convexity, and Legendreness for convex functions are
              extended from Euclidean to Banach spaces. A pertinent
              duality theory is developed and several useful
              characterizations are given. The proofs rely on new results
              on the more subtle behavior of subdifferentials and
              directional derivatives at boundary points of the domain.
              In weak Asplund spaces, a new formula allows the recovery
              of the subdifferential from nearby gradients. Finally, it
              is shown that every Legendre function on a reflexive Banach
              space is zone consistent, a fundamental property in the
              analysis of optimization algorithms based on Bregman
              distances. Numerous illustrating examples are provided.},
  number   = {04},
  journal  = {Communications in Contemporary Mathematics},
  author   = {Bauschke, Heinz H. and Borwein, Jonathan M. and Combettes,
              Patrick L.},
  month    = nov,
  year     = {2001},
  pages    = {615--647},
  file     = {Bauschke et al - Essential smoothness, essential strict
              convexity and legendre functions in
              (2001).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Bauschke et al - Essential smoothness, essential
              strict convexity and legendre functions in
              (2001).pdf:application/pdf}
}

@book{BauschkeCombettes11,
  title     = {Convex Analysis and Monotone Operator Theory in Hilbert
               Spaces},
  publisher = {Springer},
  year      = {2011},
  author    = {Heinz H. Bauschke and Patrick L. Combettes}
}

@inproceedings{BauWil88,
  author    = {E. Baum and F. Wilczek},
  title     = {Supervised Learning of Probability Distributions by Neural
               Networks},
  booktitle = {Advances in Neural Information Processing Systems 1},
  year      = {1988},
  editor    = {D.~S.~Touretzky},
  pages     = {52-61},
  address   = {New York}
}

@inproceedings{BaxBar00,
  author    = {Jonathan Baxter and Peter L. Bartlett},
  title     = {Reinforcement Learning in {POMDP}'s via Direct Gradient
               Ascent},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {41--48},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{BaxBar01,
  author  = {Jonathan Baxter and Peter L. Bartlett},
  title   = {Infinite-Horizon Policy-Gradient Estimation},
  journal = {Journal of Artificial Intelligence Research},
  year    = {2001},
  volume  = {15},
  pages   = {319--350}
}

@inproceedings{BaxBar97,
  author    = {Jonathan Baxter and Peter L. Bartlett},
  title     = {A result relating convex $n$-widths to covering numbers
               with some applications to neural networks},
  booktitle = {the European Conference on Computational Learning Theory},
  year      = {1997},
  pages     = {251--259},
  address   = {Jerusalem, Israel}
}

@techreport{BaxBar99,
  author      = {Jonathan Baxter and Peter L. Bartlett},
  title       = {Direct Gradient-Based Reinforcement Learning: Gradient
                 Estimation Algorithms},
  institution = {Research School of Information, ANU Canberra},
  year        = {1999}
}

@article{BaxBarWea01,
  author  = {Jonathan Baxter and Peter L. Bartlett and Lex Weaver},
  title   = {Experiments with Infinite-Horizon, Policy-Gradient
             Estimation},
  journal = {Journal of Artificial Intelligence Research},
  year    = {2001},
  volume  = {15},
  pages   = {351--381}
}

@article{BaxCes99,
  author    = { Jonathan Baxter and N. Cesa-Bianchi},
  title     = {Guest Editors' Introduction},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {37},
  pages     = {239--240},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{BaxOli96,
  author    = {Rohan A. Baxter and Jonathan J. Oliver},
  title     = {The kindest cut: minimum message length segmentation},
  booktitle = alt-ws,
  year      = {1996},
  volume    = {1160},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {83--90},
  publisher = {Springer-Verlag}
}

@inproceedings{Baxter95,
  author    = {Jonathan Baxter},
  title     = {Learning internal representations},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {311--320},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{Baxter96,
  author    = {Jonathan Baxter},
  title     = {A {B}ayesian/information theoretic model of bias
               learning},
  booktitle = {Proc. 9th Annu. Conf. on Comput. Learning Theory},
  year      = {1996},
  pages     = {77--88},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{Baxter97,
  author    = {J. Baxter},
  title     = {The canonical distortion measure for vector quantization
               and function approximation},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997}
}

@article{Baxter97b,
  author  = {Jonathan Baxter},
  title   = {A {B}ayesian/Information Theoretic Model of Learning to
             Learn via Multiple Task Sampling},
  journal = {Machine Learning},
  year    = {1997},
  volume  = {28},
  pages   = {7--39}
}

@article{BaxTriWea00,
  author    = {Jonathan Baxter and Andrew Tridgell and Lex Weaver},
  title     = {Learning to Play Chess Using Temporal Differences},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {40},
  pages     = {243--263},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{BaxTriWea98,
  author    = {Jonathan Baxter and Andrew Trigdell and Lex Weaver},
  title     = {KnightCap: a chess program that learns by combining
               {TD}($\lambda$) with game-tree search},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {28--36},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{BaxYahMouSanetal98,
  author    = {I.~D. Baxter and A. Yahin and L. Moura and M. Sant'Anna
               and L. Bier},
  title     = {Clone Detection Using Abstract Syntax Trees},
  booktitle = {International Conference on Software Maintenance},
  year      = {1998},
  pages     = {368--377},
  address   = {Bethesda, Maryland, USA},
  month     = {November},
  publisher = {IEEE Press}
}

@inproceedings{Bay98,
  author    = {Stephen D. Bay},
  title     = {Combining nearest neighbor classifiers through multiple
               feature subsets},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {37--45},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{Bayes63,
  author  = {T. Bayes},
  title   = {An essay towards solving a problem in the doctrine of
             chances},
  journal = {Philiosophical Transactions of the Royal Socienty},
  year    = {1763},
  volume  = {53},
  pages   = {370--418}
}

@inproceedings{BayMaSri07,
  title        = {Scaling up all pairs similarity search},
  author       = {Bayardo, R.J. and Ma, Y. and Srikant, R.},
  booktitle    = {the 16th international conference on World Wide Web},
  pages        = {131--140},
  year         = {2007},
  organization = {ACM}
}

@inproceedings{BayPaz00,
  author    = {Stephen D. Bay and M. J. Pazzani},
  title     = {Characterizing Model Errors and Differences},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {49--56},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{BayPeaRadetal17,
  author  = {Baydin, Baydin G. and Pearlmutter, Barak A. and Radul,
             Alexey Andreyevich and Siskind, Jeffrey Mark},
  title   = {Automatic Differentiation in Machine Learning: A Survey},
  journal = jmlr,
  volume  = {18},
  number  = {1},
  year    = {2017},
  pages   = {5595--5637}
}

@book{BazSheShe93,
  title     = {Nonlinear Programming: Theory and Algorithms},
  publisher = {Wiley},
  year      = {1993},
  author    = {M.S. Bazaraa and H.D. Sherali and C.M. Shetty},
  edition   = {2nd}
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@article{BeaCooEbn87,
  author  = {M. F. Bear and L. N. Cooper and F. F. Ebner},
  title   = {A Physiological Basis for a Theory of Synapse
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  journal = {Science},
  year    = {1987},
  volume  = {237},
  pages   = {42--48},
  month   = jul # { 3},
  comment = {Proposes that change in weight $w_{ij}$ from $i$ to $j$
             goes as $d(w_ij)/dt = \phi(a_i,avg(a_i)) a_j$ where $\phi$
             is negative below (say) $avg(a_i)*2$, and positive above
             it.}
}

@techreport{Beaver87,
  author      = {D. Beaver},
  title       = {Polynomially Sized Boolean Circuits are not Learnable},
  institution = {Aiken Computation Laboratory, Harvard University},
  year        = {1987},
  number      = {TR-13-87},
  month       = dec
}

@article{BeaZetWriSaletal05,
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             Rosenwald},
  title   = {Diffuse large B-cell lymphoma subgroups have distinct
             genetic profiles that influence tumor biology and improve
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@article{BecBobCan09,
  author  = {Stephen Becker and J\'{e}r\^{o}me Bobin and Emmanuel J.
             Cand\`{e}s},
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             Recovery},
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  author    = {Luca Becchetti and Paolo Boldi and Carlos Castillo and
               Aristides Gionis},
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  year      = 2008,
  pages     = {16--24},
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}

@book{BecChaWil88,
  title     = {The new {S} language},
  publisher = {Wadsworth \& Brooks/Cole Advanced Books and Software},
  year      = {1988},
  author    = {R.~A. Becker and J.~M. Chambers and A.~R. Wilks},
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  address   = {Pacific Grove, CA}
}

@incollection{becker98,
  author    = {Becker, Suzanna},
  title     = {Unsupervised learning with global objective functions},
  booktitle = {The handbook of brain theory and neural networks},
  publisher = {MIT Press},
  year      = {1998},
  pages     = {997--1000}
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@article{beckerhinton92,
  author  = {Becker, Suzanna and Hinton, Geoffrey E},
  title   = {A self-organizing neural network that discovers surfaces
             in random-dot stereograms},
  journal = {Nature},
  year    = {1992},
  volume  = {355},
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@article{BeckTeboulle03,
  author  = {Amir Beck and Marc Teboulle},
  title   = {Mirror Descent and Nonlinear Projected Subgradient Methods
             for Convex Optimization},
  journal = {Operations Research Letters},
  volume  = {31},
  pages   = {167--175},
  year    = {2003}
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@article{BeckTeboulle04,
  author  = {Amir Beck and Marc Teboulle},
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  year    = {2004},
  volume  = {59},
  pages   = {235--247}
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@book{BecSch95,
  author    = {C. Beck and F. Schl\"{o}gl},
  publisher = {Cambridge University Press},
  title     = {Thermodynamics of Chaotic Systems: An Introduction},
  year      = {1995}
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@book{BecSch95b,
  author    = {C. Beck and F. Schl\"{o}gl},
  publisher = {Cambridge University Press},
  title     = {Thermodynamics of Chaotic Systems: An Introduction},
  year      = {1995}
}

@article{BecSht16,
  author  = {Beck, Amir and Shtern, Shimrit},
  title   = {Linearly convergent away-step conditional gradient for
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  journal = {Mathematical Programming},
  year    = {2016},
  pages   = {1--27}
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@article{BecTeb03,
  title   = {Mirror Descent and Nonlinear Projected Subgradient Methods
             for Convex Optimization},
  year    = {2003},
  author  = {Amir Beck and Marc Teboulle},
  journal = {Operations Research Letters},
  volume  = 31,
  number  = 3,
  pages   = {167--175}
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@article{BecTeb04,
  author  = {Beck, Amir and Teboulle, Marc},
  title   = {A conditional gradient method with linear rate of
             convergence for solving convex linear systems},
  journal = {Mathematical Methods of Operations Research},
  year    = {2004},
  volume  = {59},
  number  = {2},
  pages   = {235--247}
}

@article{BecTeb09,
  title   = {A Fast Iterative Shrinkage-Thresholding Algorithm for
             Linear Inverse Problems},
  author  = {Amir Beck and Marc Teboulle},
  journal = {SIAM Journal on Imaging Sciences},
  volume  = {2},
  number  = {1},
  pages   = {183--202},
  year    = {2009}
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@inproceedings{BedSanKow06,
  author    = {J. Bedo and C. Sanderson and A. Kowalczyk},
  title     = {An Efficient Alternative to {SVM} Based Recursive Feature
               Elimination with Applications in Natural Language
               Processing and Bioinformatics},
  booktitle = {Artificial Intelligence},
  year      = {2006}
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@article{BeeBerLaf99,
  author    = {Doug Beeferman and Adam Berger and J.~D. Lafferty},
  title     = {Statistical Models for Text Segmentation},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {34},
  pages     = {177--210},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{BeeKarHuaetal02,
  author  = {D. G. Beer and S. L. Kardia and S. L. Huang and others},
  title   = {Gene-expression profiles predict survival of patients with
             lung adenocarcinoma},
  journal = {Nat. Med.},
  volume  = {8},
  pages   = {816--824},
  year    = {2002}
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@inproceedings{behpour2015minimax,
  title     = {A Minimax Robust Approach for Learning to Assist Users
               with Pointing Tasks.},
  author    = {Behpour, Sima and Ziebart, Brian D},
  booktitle = {AAAI Workshop: Artificial Intelligence Applied to
               Assistive Technologies and Smart Environments},
  year      = {2015}
}

@misc{behpour2016adversarial,
  title  = {Adversarial Methods Improve Object Localization},
  author = {Behpour, Sima and Ziebart, Brian D},
  year   = {2016}
}

@article{behpour2017adversarially,
  title   = {Adversarially Optimizing Intersection over Union for
             Object Localization Tasks},
  author  = {Behpour, Sima and Kitani, Kris M and Ziebart, Brian D},
  journal = {arXiv preprint arXiv:1710.07735},
  year    = {2017}
}

@inproceedings{behpour2019ada,
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@book{Bellman61,
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@inproceedings{BelMalPuz01,
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@article{BelNiy03,
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  year      = {2003},
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@article{BelNiySin06,
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@article{Belongie02,
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@article{BelRomSmi93,
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@article{BelWan00,
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@inproceedings{BelYanMac17,
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@article{Ben-David95,
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  title     = {Monotonicity maintenance in information-theoretic machine
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  journal   = {Machine Learning},
  year      = {1995},
  volume    = {19},
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@article{Ben-David98,
  author  = {Shai Ben-David},
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  journal = {Algorithmica},
  year    = {1998},
  volume  = {22},
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@unpublished{Ben-David99,
  author = {S. Ben-David},
  title  = {Lecture given at the {Dagstuhl Seminar on Unsupervised
            Learning}},
  note   = {personal communication},
  year   = {March 1999}
}

@article{ben2013robust,
  title     = {Robust solutions of optimization problems affected by
               uncertain probabilities},
  author    = {Ben-Tal, Aharon and Den Hertog, Dick and De Waegenaere,
               Anja and Melenberg, Bertrand and Rennen, Gijs},
  journal   = {Management Science},
  volume    = {59},
  number    = {2},
  pages     = {341--357},
  year      = {2013},
  publisher = {INFORMS}
}

@techreport{BenBen91,
  author      = {S. Ben-David and G. M. Benedek},
  title       = {Measurability Constraints on {PAC} Learnability},
  institution = {Technion, Haifa},
  year        = {1991},
  comment     = {The year listed here may be incorrect.}
}

@inproceedings{BenBenMan89,
  author    = {S. Ben-David and G. M. Benedek and Y. Mansour},
  title     = {A parametrization scheme for classifying models of
               learnability},
  booktitle = {Proc. 2nd Annu. Workshop on Comput. Learning Theory},
  year      = {1989},
  pages     = {285--302},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@inproceedings{BenBlu97,
  author    = {K.~P. Bennett and J.~A. Blue},
  title     = {A Support Vector Machine Approach to Decision Trees},
  booktitle = {the International Joint Conference on Neural Networks},
  year      = {1997},
  pages     = {2396--2401},
  address   = {Anchorage, Alaska}
}

@inproceedings{BenBre00,
  author    = {K.~P. Bennett and E.~J. Bredensteiner},
  title     = {Duality and geometry in {SVM} classifiers},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  editor    = {P. Langley},
  pages     = {57--64},
  address   = {San Francisco, California},
  publisher = {Morgan Kaufmann Publishers}
}

@article{BenBre97,
  author  = {K.~P. Bennett and E.~J. Bredensteiner},
  title   = {A Parametric Optimization Method for Machine Learning},
  journal = {INFORMS Journal on Computing},
  year    = {1997},
  volume  = {9},
  pages   = {311--318},
  number  = {3}
}

@inproceedings{BenBre98,
  author    = {K.~P. Bennett and E.~J. Bredensteiner},
  title     = {Geometry in Learning},
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  year      = {1998},
  editor    = {C. Gorini and E. Hart and W. Meyer and T. Phillips},
  address   = {Washington, D.C.},
  publisher = {Mathematical Association of America},
  note      = {Available
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}

@inproceedings{BenBru03,
  author    = {A. Ben-Hur and D. Brutlag},
  title     = {Remote Homology Detection: A Motif Based Approach},
  booktitle = {{ISMB} 2003},
  year      = {2003}
}

@article{BenByeFasMehetal00,
  author  = {Peter Benner and Ralph Byers and Heike Fassbender and
             Volker Mehrmann and David Watkins},
  title   = {{C}holesky-Like Factorizations of Skew-Symmetric
             Matrices},
  pages   = {85--93},
  journal = {Electronic Transactions on Numerical Analysis},
  volume  = {11},
  year    = {2000}
}

@article{BenCesHauLon95,
  author  = {Shai Ben-David and N. Cesa-Bianchi and David Haussler and
             P. M. Long},
  title   = {Characterizations of learnability for classes of
             \{0,\dots, n\}-valued functions},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1995},
  volume  = {50},
  pages   = {74--86},
  number  = {1}
}

@inproceedings{BenCesLon92,
  author    = {S. Ben-David and N. Cesa-Bianchi and P. M. Long},
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               0,\dots ,n \}$-valued functions},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {333--340},
  publisher = {ACM Press, New York, NY}
}

@article{BenCouVin13,
  author  = {Y. {Bengio} and A. {Courville} and P. {Vincent}},
  journal = pami,
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  year    = {2013},
  volume  = {35},
  number  = {8},
  pages   = {1798-1828}
}

@article{BenCriShaWu00,
  author    = {K. P. Bennett and N. Cristianini and J. {Shawe-{T}aylor}
               and D. Wu},
  title     = {Enlarging the Margins in Perceptron Decision Trees},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {41},
  pages     = {295--313},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@techreport{BenCurMorSar04,
  author      = {S.J. Benson and L. Curfman-McInnes and J. Mor\'{e} and J.
                 Sarich},
  title       = {{TAO} User Manual (Revision 1.8)},
  institution = {Mathematics and Computer Science Division, Argonne
                 National Laboratory},
  year        = {2004},
  number      = {ANL/MCS-TM-242},
  note        = {http://www.mcs.anl.gov/tao}
}

@article{BenDavidetal10,
  author  = {Ben-David, Shai and Blitzer, John and Crammer, Koby and
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             Jennifer},
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  journal = {Machine Learning Journal},
  volume  = {72},
  number  = {1-2},
  pages   = {151--175},
  year    = 2010
}

@article{BenDeJ96,
  author    = {S. W. Bennett and G. F. DeJong},
  title     = {Real-World Robotics: Learning to Plan for Robust
               Execution},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {23},
  pages     = {121--161},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{BenDem98,
  author    = {K. Bennet and A. Demiriz},
  title     = {Semi-Supervised Support Vector Machines},
  booktitle = {Advances in Neural Information Processing Systems 11},
  year      = {1998},
  editor    = {M.~S. Kearns and S.~A. Solla and D.~A. Cohn},
  pages     = {368--374},
  publisher = {{MIT} Press}
}

@inproceedings{BenDemSha00,
  author    = {K.~P. Bennett and A. Demiriz and J. Shawe-Taylor},
  title     = {A Column Generation Algorithm for Boosting},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {65--72},
  editor    = {P. Langley},
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}

@article{BendenDeWetal13,
  author  = {Ben-Tal, Aharon and den Hertog, Dick and De Waegenaere,
             Anja and Melenberg, Bertrand and Rennen, Gijs},
  title   = {Robust Solutions of Optimization Problems Affected by
             Uncertain Probabilities},
  journal = {Management Science},
  volume  = {59},
  number  = {2},
  pages   = {341-357},
  year    = {2013}
}

@inproceedings{BenDic93,
  author    = {S. Ben-David and E. Dichterman},
  title     = {Learning with restricted focus of attention},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {287--296},
  publisher = {ACM Press, New York, NY}
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@inproceedings{BenDic94,
  author    = {S. Ben-David and E. Dichterman},
  title     = {Learnability with restricted focus of attention guarantees
               noise-tolerance},
  booktitle = alt-ws,
  year      = {1994},
  pages     = {248--259},
  publisher = {Springer-Verlag}
}

@inproceedings{BenEirKus95,
  author    = {Shai Ben-David and Nadav Eiron and Eyal Kushilevitz},
  title     = {On self-directed learning},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {136--143},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BenEirLon00,
  author    = {Shai Ben-David and Nadav Eiron and P. M. Long},
  title     = {On the Difficulty of Approximately Maximizing Agreements},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {266--274},
  publisher = {Morgan Kaufmann, San Francisco}
}

@article{BenEirLon03,
  author  = {S. Ben-David and N. Eiron and P.M. Long},
  title   = {On the difficulty of approximately maximizing agreements},
  journal = {J. Comput. System Sci.},
  year    = {2003},
  volume  = {66},
  pages   = {496-514},
  number  = {3}
}

@inproceedings{BenEirSim00,
  author    = {S. Ben-David and N. Eiron and H.~U. Simon},
  title     = {The computational complexity of densest region detection},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2000},
  editor    = {N. Cesa-Bianchi and S. Goldman},
  pages     = {255--265},
  address   = {San Francisco},
  publisher = {Morgan Kaufmann Publishers}
}

@article{BenEirSim02,
  author  = {S. Ben-David and N. Eiron and H. U. Simon},
  title   = {Limitations of Learning via Embeddings in {E}uclidean
             Half-Spaces},
  journal = {Journal of Machine Learning Research},
  year    = {2002},
  volume  = {3},
  pages   = {441-461},
  month   = {November},
  annote  = {(Preliminary version appeared in COLT 2001)}
}

@book{BenElgNem09,
  author    = {Ben-Tal, A. and El Ghaoui, L. and Nemirovski, A.S.},
  month     = {October},
  publisher = {Princeton University Press},
  series    = {Princeton Series in Applied Mathematics},
  title     = {Robust Optimization},
  year      = {2009}
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@inproceedings{Bengioetal05,
  author    = {Yoshua Bengio and Nicolas Le Roux and Pascal Vincent and
               Olivier Delalleau and Patrice Marcotte},
  title     = {Convex Neural Networks},
  booktitle = nips,
  year      = {2005}
}

@book{BenGre74,
  title     = {Generalized inverses: theory and applications},
  publisher = {John Wiley and Sons},
  year      = {1974},
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  address   = {New York}
}

@inproceedings{BenGur95,
  author    = {Shai Ben-David and Leonid Gurvits},
  title     = {A note on {VC}-dimension and measure of sets of reals},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {454--462},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BenHorSieVap01,
  author    = {A. Ben-Hur and D. Horn and H. Siegelmann and V. Vapnik},
  title     = {A Support Vector Method for Hierarchical Clustering},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {367--373},
  publisher = {{MIT} Press}
}

@techreport{BenHuiAus98,
  author      = {K.~P. Bennett and D. Hui and L. Auslender},
  title       = {On Support Vector Decision Trees for Database Marketing},
  institution = {Rensselaer Polytechnic Institute},
  year        = {1998},
  type        = {Department of Mathematical Sciences},
  number      = {Math Report No.~98 - 100},
  address     = {Troy, NY 12180},
  month       = mar,
  note        = {http://www.math.rpi.edu/\~{}bennek/}
}

@inproceedings{BenIta88,
  author    = {G. M. Benedek and A. Itai},
  title     = {Learnability by fixed distributions},
  booktitle = {Proc. 1st Annu. Workshop on Comput. Learning Theory},
  year      = {1988},
  pages     = {80--90},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@inproceedings{BenIta88b,
  author    = {G. M. Benedek and A. Itai},
  title     = {Nonuniform Learnability},
  booktitle = {ICALP},
  year      = {1988},
  pages     = {82--92},
  month     = jul,
  annote    = {Was TR 474 from Technion-Israel Institute of Technology in
               1987}
}

@article{BenIta91,
  author  = {G. Benedek and A. Itai},
  title   = {Learnability with respect to Fixed Distributions},
  journal = {Theoret.\ Comput.\ Sci.},
  year    = {1991},
  volume  = {86},
  pages   = {377--389},
  number  = {2},
  annote  = {Was in COLT88 and was Technion-Israel Institute of
             Technology TR 468 in 1987}
}

@inproceedings{BenIta92,
  author    = {G. M. Benedek and A. Itai},
  title     = {Dominating distributions and learnability},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {253--264},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BenItaKus90,
  author    = {S. Ben-David and A. Itai and E. Kushilevitz},
  title     = {Learning by distances},
  booktitle = {Proc. 3rd Annu. Workshop on Comput. Learning Theory},
  year      = {1990},
  pages     = {232--245},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@inproceedings{BenJac93,
  author    = {S. Ben-David and M. Jacovi},
  title     = {On learning in the limit and non-uniform $(\epsilon ,
               \delta )$- learning},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {209--217},
  publisher = {ACM Press, New York, NY}
}

@article{BenKusMan97,
  author    = {S. Ben-David and E. Kushilevitz and Y. Mansour},
  title     = {Online Learning versus Offline Learning},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {29},
  pages     = {45--63},
  publisher = {Kluwer Academic}
}

@inproceedings{BenLeCHen94,
  author    = {Y. Bengio and Y. LeCun and D. Henderson},
  title     = {Globally Trained Handwritten Word Recognizer using Spatial
               Representation, Convolutional Neural Networks and Hidden
               {Markov} Models},
  booktitle = {Advances in Neural Information Processing Systems 5},
  year      = {1994},
  editor    = {S.~J. Hanson and J.~D. Cowan and C.~L. Giles},
  pages     = {937--944},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{BenLeRVinetal05,
  author    = {Yoshua Bengio and Nicolas Le Roux and Pascal Vincent and
               Olivier Delalleau and Patrice Marcotte},
  title     = {Convex Neural Networks},
  booktitle = nips,
  year      = {2005}
}

@inproceedings{BenLin93,
  author    = {S. Ben-David and M. Lindenbaum},
  title     = {Localization vs. identification of semi-algebraic sets},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {327--336},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BenLin95,
  author    = {S. Ben-David and M. Lindenbaum},
  title     = {Learning distributions by their density levels\,---\,a
               paradigm for learning without a teacher},
  booktitle = {the European Conference on Computational Learning Theory},
  year      = {1995},
  pages     = {53--68}
}

@article{BenLin97,
  author  = {S. Ben-David and M. Lindenbaum},
  title   = {Learning Distributions by Their Density Levels: A Paradigm
             for Learning without a Teacher},
  journal = {Journal of Computer and System Sciences},
  year    = {1997},
  volume  = {55},
  pages   = {171--182}
}

@inproceedings{BenLuxPal06,
  author    = {S.~Ben-David and U.~von~Luxburg and D.~Pal},
  title     = {A sober look on clustering stability},
  booktitle = {the 19th Annual Conference on Learning Theory},
  year      = {2006},
  publisher = {Springer}
}

@article{BenMan92,
  author  = {K.~P. Bennett and O.~L. Mangasarian},
  title   = {Robust Linear Programming Discrimination of Two Linearly
             Inseparable Sets},
  journal = {Optim. Methods Softw.},
  year    = {1992},
  volume  = {1},
  pages   = {23--34}
}

@article{BenMan93,
  author  = {K.~P. Bennett and O.~L. Mangasarian},
  title   = {Multicategory Separation via Linear Programming},
  journal = {Optimization Methods and Software},
  year    = {1993},
  volume  = {3},
  pages   = {27--39}
}

@article{BenMan94,
  author  = {K.~P. Bennett and O.~L. Mangasarian},
  title   = {Serial and Parallel Multicategory Discrimination},
  journal = {SIAM Journal on Optimization},
  year    = {1994},
  volume  = {4},
  pages   = {722--734},
  number  = {4}
}

@article{BenMan95,
  author    = {Arie Ben-David and Janice Mandel},
  title     = {(Research Note) Classification Accuracy: Machine Learning
               vs. Explicit Knowledge Acquisition},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {18},
  pages     = {109--114},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{BenMarNem01,
  author  = {Ahron Ben-Tal and Tamar Margalit and Arkadi Nemirovski},
  title   = {The Ordered Subsets Mirror Descent Optimization Method
             with Applications to Tomography},
  journal = siamopt,
  year    = 2001,
  volume  = 12,
  number  = 1,
  pages   = {79--108},
  month   = {July}
}

@article{Bennett62,
  author  = {G. Bennett},
  title   = {Probability Inequalities for the Sum of Independent Random
             Variables},
  journal = {Journal of the American Statistical Association},
  year    = {1962},
  volume  = {57},
  pages   = {33--45}
}

@article{Bennett65,
  author  = {J.~M. Bennett},
  title   = {Triangular factors of modified matrices},
  journal = {Numerical Mathematics},
  year    = {1965},
  volume  = {7},
  pages   = {217--221}
}

@inproceedings{Bennett92,
  author    = {K.~P. Bennett},
  title     = {Decision Tree Construction Via Linear Programming},
  booktitle = {the 4th Midwest Artificial Intelligence and Cognitive
               Science Society Conference},
  year      = {1992},
  editor    = {M. Evans},
  pages     = {97--101},
  address   = {Utica, Illinois}
}

@incollection{Bennett98,
  author    = {Kristin Bennett},
  title     = {Combining Support Vector and Mathematical Programming
               Methods for Classification},
  booktitle = {Advances in Kernel Methods\,---\,Support Vector Learning},
  publisher = {{MIT} Press},
  year      = {1998},
  pages     = {307--326}
}

@inproceedings{Bennett99,
  author    = {K. Bennett},
  title     = {Combining Support Vector and Mathematical Programming
               Methods for Induction},
  booktitle = {Advances in Kernel Methods\,---\,SV Learning},
  year      = {1999},
  editor    = {B. Sch{\"o}lkopf and C.~J.~C. Burges and A. J. Smola},
  pages     = {307--326},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{BenNob05,
  author  = {A. Ben-Hur and W. Noble},
  title   = {Kernel methods for predicting protein-protein
             interactions},
  journal = {Bioinformatics},
  year    = {2005},
  volume  = {21 Suppl 1},
  pages   = {i38-i46}
}

@article{Benoist_WhatSubdifferential_1996,
  title    = {What is the subdifferential of the closed convex hull of a
              function?},
  volume   = {27},
  issn     = {0036-1410, 1095-7154},
  doi      = {10.1137/S0036141094265936},
  language = {en},
  number   = {6},
  journal  = {SIAM Journal on Mathematical Analysis},
  author   = {Benoist, J. and Hiriart-Urruty, J.-B.},
  month    = nov,
  year     = {1996},
  pages    = {1661--1679},
  file     = {What is the subdifferential of the closed convex hull of a
              function?:/Users/zcranko/Documents/Zotero/storage/UXTXMPFF/benoist1996.html:text/html}
}

@inproceedings{BenPaiVinDeletal03,
  author    = {Y. Bengio and J.~F. Paiement and P. Vincent and O.
               Delalleau and N.~L. Roux and M. Ouimet},
  title     = {Out of Sample Extensions for LLE, Isomap, MDS, Eigenmaps,
               and Spectral Clustering},
  booktitle = {Advances in Neural Information Processing Systems 16},
  year      = {2003},
  editor    = {S. Thrun and L. Saul and B. Sch\"{o}lkopf},
  volume    = {16},
  pages     = {177-184},
  publisher = {{MIT} Press}
}

@article{BenPar06,
  author  = {K.~P.~Bennett and E.~Parrado-Hernandez},
  journal = {J. Mach. Learn. Res.},
  month   = {October},
  pages   = {1705--1749},
  title   = {The Interplay of Optimization and Machine Learning
             Research},
  volume  = {7},
  year    = {2006}
}

@article{BenRao93,
  author  = {J. Ben-Arie and R. Rao},
  title   = {A Novel Approach for Template Matching by Nonorthogonal
             Image Expansion},
  journal = {IEEE Trans.\ Circuit and Systems},
  year    = {1993},
  volume  = {3},
  pages   = {71--84}
}

@inproceedings{BenSha76,
  author    = {J.~L. Bentley and M.~I. Shamos},
  title     = {Divide-And-Conquer In Multidimensional Space},
  booktitle = {the $8^\mathrm{th}$ annual ACM symposium on Theory of
               computing},
  year      = {1976},
  pages     = {220--230},
  location  = {Hershey, Pennsylvania, United States}
}

@inproceedings{BenSim01,
  author    = {S. Ben-David and H. Simon},
  title     = {Efficient Learning of Linear Perceptrons},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {189--195},
  publisher = {{MIT} Press}
}

@inproceedings{BenSlo94,
  author    = {M. A. Bender and Donna K. Slonim},
  title     = {The power of team exploration: two robots can learn
               unlabeled directed graphs},
  booktitle = {Proc. of the 35rd Annual Symposium on Foundations of
               Computer Science},
  year      = {1994},
  pages     = {75--85},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@inproceedings{Benson95,
  author    = {Scott Benson},
  title     = {Inductive learning of reactive action models},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {47--54},
  publisher = {Morgan Kaufmann}
}

@article{Bentley75,
  author  = {J.~L. Bentley},
  title   = {Multidimensional binary search trees used for associative
             searching},
  journal = {Communications of the {ACM}},
  year    = {1975},
  volume  = {18},
  pages   = {509--517},
  number  = {9}
}

@article{Bentley79,
  author  = {J.~L. Bentley},
  title   = {Multidimensional binary search trees in database
             applications},
  journal = {{IEEE Trasaction on Software Engineering}},
  year    = {1979},
  volume  = {5},
  pages   = {333--340},
  number  = {4}
}

@incollection{BenTom91,
  author    = {M. Benaim and L. Tomasini},
  title     = {Competitive and self-organizing algorithms based on the
               minimization of an information criterion},
  booktitle = {Artificial Neural Networks},
  publisher = {Elsevier Science Publishers},
  year      = {1991},
  editor    = {T. Kohonen and K. Makisara and O. Simula and J. Kangas},
  pages     = {391--396}
}

@inproceedings{Bentonetal19,
  title     = {Deep Generalized Canonical Correlation Analysis},
  author    = {Adrian Benton and Huda Khayrallah and Biman Gujral and Dee
               Ann Reisinger and Sheng Zhang and Raman Arora},
  booktitle = {Workshop on Representation Learning for NLP},
  year      = {2019}
}

@article{Benveniste81,
  author  = {R Benveniste},
  title   = {One way to solve the parametric quadratic programming
             problem},
  journal = {Mathematical Programming},
  year    = 1981,
  volume  = 21,
  number  = 1,
  pages   = {224--228}
}

@techreport{BenWuAus98,
  author      = {K.~P. Bennett and D.~H. Wu and L. Auslender},
  title       = {On Support Vector Decision Trees for Database Marketing},
  institution = {Rensselaer Polytechnic Institute},
  year        = {1998},
  type        = {R.{P}.{I}.~{M}ath {R}eport {N}o.~98 - 100},
  address     = {Troy, NY}
}

@article{BerAnaHanHin92,
  author  = {J.~R. Bergen and P. Anandan and K.~J. Hanna and R.
             Hingorani},
  title   = {Hierarchical Model-Based Motion Estimation},
  journal = eccv,
  year    = {1992},
  pages   = {237--252}
}

@inproceedings{BerBio95,
  author    = {Jan van den Berg and Jan C. Bioch},
  title     = {Some theorems concerning the free energy of
               (un)constrained stochastic {H}opfield neural networks},
  booktitle = {Computational Learning Theory: EuroColt '95},
  year      = {1995},
  pages     = {298--312},
  publisher = {Springer-Verlag}
}

@inproceedings{BerBouDeNKle10,
  title     = {Painless Unsupervised Learning with Features},
  author    = {Taylor Berg-Kirkpatrick and Alexandre Bouchard-Cote and
               John DeNero and Dan Klein},
  booktitle = {The 2010 Annual Conference of the North American Chapter
               of the Association for Computational Linguistics},
  year      = {2010}
}

@inproceedings{BerBshTamVar97,
  author    = {Francesco Bergadano and Nader H. Bshouty and Christino
               Tamon and Stefano Varricchio},
  title     = {On learning branching programs and small depth circuits},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  pages     = {150--161},
  publisher = {Springer-Verlag}
}

@article{BerCamGanPos97,
  author  = {A. Bertoni and P. Campadelli and G. Gangai and R.
             Posenato},
  title   = {Approximability of the Ground State Problem for {I}sing
             Spin Glasses},
  pages   = {326--339},
  journal = {Journal of Complexity},
  volume  = 13,
  year    = 1997
}

@inproceedings{BerCamMorPan92,
  author    = {A. Bertoni and P. Campadelli and A. Morpurgo and S.
               Panizza},
  title     = {Polynomial uniform convergence and polynomial-sample
               learnability},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {265--271},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BerCamPar97,
  author    = {A. Bertoni and P. Campadelli and M. Parodi},
  title     = {{A} Boosting Algorithm for Regression},
  booktitle = {Proc.\ Intl. Conf.\ Artificial Neural Networks (ICANN)},
  year      = {1997},
  editor    = {W. Gerstner and A. Germond and M. Hasler and J.-D.
               Nicoud},
  volume    = {V},
  series    = {LNCS},
  pages     = {343--348},
  address   = {Berlin},
  publisher = {Springer}
}

@book{BerChrRes84,
  title     = {Harmonic Analysis on Semigroups},
  publisher = {Springer},
  year      = {1984},
  author    = {C. Berg and J.~P.~R. Christensen and P. Ressel},
  address   = {New York}
}

@article{BerCop18,
  title   = {Characterization of the equivalence of robustification and
             regularization in linear and matrix regression},
  journal = {European Journal of Operational Research},
  author  = {Dimitris Bertsimas and Martin S. Copenhaver},
  volume  = {270},
  number  = {3},
  pages   = {931--942},
  year    = {2018}
}

@inproceedings{BerCorIndStr09,
  title     = {Space-optimal heavy hitters with strong error bounds},
  author    = {R. Berinde and G. Cormode and P. Indyk and M.J. Strauss},
  booktitle = {the Twenty-Eigth {ACM} {SIGMOD}-{SIGACT}-{SIGART}
               Symposium on Principles of Database Systems, {PODS}},
  publisher = {ACM},
  year      = {2009},
  editor    = {J. Paredaens and J. Su},
  pages     = {157--166},
  url       = {http://doi.acm.org/10.1145/1559795.1559819}
}

@book{BerFor75,
  title     = {Potential Theory on Locally Compact Abelian Groups},
  publisher = {Springer-Verlag},
  year      = {1975},
  author    = {C. Berg and G. Forst},
  address   = {Berlin}
}

@article{BerGamTsi99,
  author    = {Dimitris Bertsimas and David Gamarnik and John N.
               Tsitsiklis},
  title     = {Estimation of Time-Varying Parameters in Statistical
               Models: {A}n Optimization Approach},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {35},
  pages     = {225--245},
  number    = {3},
  comment   = {first in 10th COLT '97},
  publisher = {Kluwer Academic Publishers, Boston}
}

@book{Berger85,
  title     = {Statistical Decision theory and {B}ayesian Analysis},
  publisher = {Springer},
  year      = {1985},
  author    = {J.~O. Berger},
  address   = {New York}
}

@inproceedings{Berger85b,
  author    = {James Berger},
  title     = {The Frequentist Viewpoint and Conditioning},
  booktitle = {the Berkley Symposium},
  year      = {1985},
  pages     = {15--44}
}

@book{Berger_StatisticalDecision_1993,
  address   = {New York, NY, USA},
  edition   = {2},
  series    = {Springer series in statistics},
  title     = {Statistical decision theory and {Bayesian} analysis},
  isbn      = {0-387-96098-8},
  publisher = {Springer-Verlag},
  author    = {Berger, James O.},
  year      = {1993},
  note      = {tex.mrclass: 62-02 (62A15 62Cxx) tex.mrnumber: 1234489},
  file      = {Berger - Statistical decision theory and Bayesian analysis
               (1993).pdf:/Users/zcranko/Documents/Library/Book/Berger -
               Statistical decision theory and Bayesian analysis
               (1993).pdf:application/pdf}
}

@inproceedings{Berggren93,
  author    = {U. Berggren},
  title     = {Linear time deterministic learning of $k$-term {DNF}},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {37--40},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BerGivImmZil00,
  author    = {Daniel S. Bernstein and Robert Givan and Neil Immerman and
               Shlomo Zilberstein},
  title     = {The Complexity of Decentralized Control of Markov Decision
               Processes},
  booktitle = {UAI 16},
  year      = {2000}
}

@inproceedings{BerGun93,
  author    = {Francesco Bergadano and Daniele Gunetti},
  title     = {Funtional Inductive Logic Programming with Queries to the
               User},
  booktitle = {Machine Learning: ECML-93, European Conference on Machine
               Learning, Proceedings},
  year      = {1993},
  volume    = {667},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {323--328},
  publisher = {Springer-Verlag}
}

@inproceedings{BerHenToretal19,
  title     = {Meta-learning with differentiable closed-form solvers},
  author    = {Luca Bertinetto and Joao F. Henriques and Philip H.S. Torr
               and Andrea Vedaldi},
  booktitle = iclr,
  year      = {2019}
}

@article{BerIveLan05,
  author  = {A. Berchuck and E. Iversen and J. Lancaster},
  title   = {Patterns of gene expression that characterize long-term
             survival in advanced stage serous ovarian cancers},
  journal = {Clin. Cancer Res.},
  year    = {2005},
  volume  = {11},
  pages   = {3686--3696}
}

@inproceedings{BerKipWel18,
  title     = {Graph convolutional matrix completion},
  author    = {Berg, Rianne van den and Kipf, Thomas N and Welling, Max},
  booktitle = {Knowledge Discovery and Data Mining},
  year      = {2018}
}

@article{BerMatMicZha92,
  author    = {F. Bergadano and S. Matwin and R. S. Michalski and J.
               Zhang},
  title     = {Learning Two-Tiered Descriptions of Flexible Concepts: The
               {POSEIDON} System},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {8},
  pages     = {5--43},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{BerMolPik85,
  author  = {M. Bertero and C. De Mol and E.~R. Pike},
  title   = {Linear inverse problems with discrete data.~{I}: general
             formulation and singular system analysis},
  journal = {Inverse Problems},
  year    = {1985},
  volume  = {1},
  pages   = {301--330}
}

@inproceedings{BerMot01,
  author    = {Jacob Berlin and Amihai Motro},
  title     = {Autoplex: Automated Discovery of Content for Virtual
               Databases.},
  booktitle = {CoopIS},
  year      = {2001},
  pages     = {108-122}
}

@inproceedings{BerMot02,
  author    = {Jacob Berlin and Amihai Motro},
  title     = {Database Schema Matching Using Machine Learning with
               Feature Selection.},
  booktitle = {CAiSE},
  year      = {2002},
  pages     = {452-466}
}

@inproceedings{BerMot02b,
  author    = {J. Berlin and A. Motro},
  title     = {Database Schema Matching Using Machine Learning with
               Feature Selection.},
  booktitle = {CAiSE},
  year      = {2002},
  pages     = {452-466}
}

@inproceedings{Bernhardt83,
  author    = {L. Bernhardt},
  title     = {Zur {K}lassifizierung vieler {M}usterklassen mit wenigen
               {M}erkmalen},
  booktitle = {5.~DAGM Symposium: Mustererkennung 1983},
  year      = {1983},
  editor    = {H. Kazmierczak},
  pages     = {255--260},
  address   = {Berlin},
  publisher = {VDE-Verlag}
}

@book{Bernstein05,
  title     = {Matrix Mathematics},
  publisher = {Princeton University Press},
  year      = {2005},
  author    = {Dennis S. Bernstein}
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@article{Bernstein29,
  author  = {S. Bernstein},
  title   = {Sur les fonctions absolument monotones},
  journal = {Acta Mathematica},
  year    = {1929},
  volume  = {52},
  pages   = {1--66}
}

@book{Bernstein46,
  title     = {The Theory of Probabilities},
  publisher = {Gastehizdat Publishing House},
  year      = {1946},
  author    = {S. Bernstein},
  address   = {Moscow}
}

@inproceedings{Bernstein92,
  author    = {E. J. Bernstein},
  title     = {Absolute error bounds for learning linear functions
               online},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {160--163},
  publisher = {ACM Press, New York, NY}
}

@article{BerPai78,
  author  = {J. Berger and C. Pair},
  title   = {Inference for Regular Bilanguages},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1978},
  volume  = {16},
  pages   = {100--122}
}

@article{BerPen97,
  author  = {J. Bernu\'{e}s and A.~{Pe\~{n}a}},
  title   = {On the Shape of $p$-Convex Hulls, $0<p<1$},
  journal = {Acta Mathematica Hungarica},
  year    = {1997},
  volume  = {74},
  pages   = {345--353},
  number  = {4}
}

@article{BerPil87,
  author    = {Robert C. Berwick and Sam Pilato},
  title     = {Learning Syntax by Automata Induction},
  journal   = {Machine Learning},
  year      = {1987},
  volume    = {2},
  pages     = {9--38},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{BerPogTor88,
  author  = {M. Bertero and T. Poggio and V. Torre},
  title   = {Ill-Posed Problems in Early Vision},
  journal = {the {IEEE}},
  year    = {1988},
  volume  = {76},
  pages   = {869--889}
}

@inproceedings{BerRomSho89,
  author    = {B. Berger and J. Rompel and P. W. Shor},
  title     = {Efficient {NC} algorithms for set cover with applications
               to learning and geometry},
  booktitle = {Proc. 30th Annu. IEEE Sympos. Found. Comput. Sci.},
  year      = {1989},
  pages     = {54--59},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@inproceedings{BerRoo87,
  author    = {P. Berman and R. Roos},
  title     = {Learning one-counter languages in polynomial time},
  booktitle = {Proc. 28th Annu. IEEE Sympos. Found. Comput. Sci.},
  year      = {1987},
  pages     = {61--67},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@article{Berry81,
  author  = {M.~V. Berry},
  title   = {Quantizing a classically ergodic system: Sinai's billiard
             and the {KKR} method},
  journal = {Annals of Physics},
  year    = {1981},
  volume  = {131},
  pages   = {163--216}
}

@inproceedings{BerSai89,
  author    = {F. Bergadano and L. Saitta},
  title     = {On the error probabilty of boolean concept descriptions},
  booktitle = {Proc. of the 1989 European Working Session on Learning},
  year      = {1989},
  pages     = {25--35}
}

@book{BerSha03,
  title     = {Completely Positive Matrices},
  author    = {Abraham Berman and Naomi Shaked-Monderer},
  year      = {2003},
  publisher = {World Scientific}
}

@book{BerSmi94,
  title     = {Bayesian Theory},
  publisher = {John Wiley and Sons},
  year      = {1994},
  author    = {J.~M. Bernardo and A.~F.M. Smith},
  address   = {Chichester}
}

@book{Berstel79,
  author    = {Jean Berstel},
  title     = {Transductions and Context-Free Languages},
  publisher = {Teubner},
  year      = 1979
}

@incollection{Bertero86,
  author    = {M. Bertero},
  title     = {Regularization methods for linear inverse problems},
  booktitle = {Inverse Problems},
  publisher = {Springer},
  year      = {1986},
  editor    = {C.~G. Talenti},
  address   = {Berlin}
}

@article{BerThi04,
  author  = {Bernard, Fr{\'e}d{\'e}ric and Thibault, Lionel},
  title   = {Prox-Regularity of Functions and Sets in Banach Spaces},
  journal = {Set-Valued Analysis},
  year    = {2004},
  month   = {Mar},
  day     = {01},
  volume  = {12},
  number  = {1},
  pages   = {25--47}
}

@book{BerTho03,
  author    = {A. Berlinet and C. {Thomas-Agnan}},
  title     = {Reproducing Kernel Hilbert Spaces in Probability and
               Statistics},
  publisher = {Springer-Verlag},
  year      = {2003},
  address   = {Berlin}
}

@book{bertsekas1978stochastic,
  title     = {Stochastic optimal control: The discrete time case},
  author    = {Dimitri P Bertsekas and Steven E Shreve},
  year      = {1978},
  publisher = {Academic Press}
}

@article{Bertsekas76,
  author  = {Dimitri Bertsekas},
  title   = {On the {G}oldstein-{L}evitin-{P}olyak gradient projection
             method},
  journal = {IEEE Transactions on Automatic Control},
  year    = {1976},
  volume  = {21},
  number  = {2},
  pages   = {174--184}
}

@book{Bertsekas95,
  title     = {Nonlinear Programming},
  publisher = {Athena Scientific},
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  month   = {Sep},
  year    = {2002}
}

@inproceedings{BisSpiWin02,
  author    = {C. Bishop and D. Spiegelhalter and J. Winn },
  booktitle = {NIPS 15},
  title     = {VIBES: A variational inference engine for Bayesian
               networks},
  year      = {2002}
}

@techreport{BisSveWil96,
  author      = {C.~M. Bishop and M. Svens\'en and C.~K.~I. Williams},
  title       = {{GTM}: The Generative Topographic Mapping},
  institution = {Neural Computing Research Group},
  year        = {1996},
  number      = {NCRG/96/015},
  address     = {Aston University, UK}
}

@article{BisSveWil98,
  author  = {C.~M. Bishop and M. Svens\'en and C.~K.~I. Williams},
  title   = {{GTM}: The Generative Topographic Mapping},
  journal = {Neural Computation},
  year    = {1998},
  volume  = {10},
  pages   = {215--234},
  number  = {1}
}

@article{BisSveWil98b,
  author  = {C.~M. Bishop and M. Svens\'en and C.~K.~I. Williams},
  title   = {Developments of the Generative Topographic Mapping},
  journal = {Neurocomputing},
  year    = {1998},
  volume  = {21},
  pages   = {203--224}
}

@inproceedings{BisTip00,
  author    = {C.~M. Bishop and M.~E. Tipping},
  title     = {Variational Relevance Vector Machines},
  booktitle = {16th Conference on Uncertainty in Artificial Intelligence
               {UAI'2000}},
  year      = {2000},
  pages     = {46--53}
}

@inproceedings{BiZha04,
  author    = {J. Bi and T. Zhang},
  title     = {Support Vector Classification with Input Data
               Uncertainty},
  booktitle = {Advances in Neural Information Processing Systems 17},
  year      = { 2004},
  editor    = {L. K. Saul and Y. Weiss and L. Bottou}
}

@misc{BizIvaGis90,
  author = {E. Bizzi and F. Mussa Ivaldi and S. Giszter},
  title  = {The Organization of Limb Motor Space in the Spinal Cord},
  year   = {1990},
  note   = {in preparation}
}

@article{BlaBouMas08,
  author  = {G. Blanchard and O. Bousquet and P. Massart},
  title   = {Statistical Performance of Support Vector Machines},
  journal = as,
  year    = 2008,
  volume  = 36,
  number  = 2,
  pages   = {489--531}
}

@article{BlaCurZis93,
  author  = {A. Blake and R. Curwen and A. Zisserman},
  title   = {A Framework for Spatiotemporal Control in the Tracking of
             Visual Contours},
  journal = {International Journal of Computer Vision},
  year    = {1993},
  volume  = {11},
  pages   = {127--145},
  number  = {2}
}

@inproceedings{BlaDea98,
  author    = {Jock.~A. Blackard and Denis J. Dean},
  title     = {Comparative Accuracies of Neural Networks and Discriminant
               Analysis in Predicting Forest Cover Types from Cartographic
               Variables},
  booktitle = {Second Southern Forestry GIS Conference},
  year      = {1998}
}

@inproceedings{BlaEccForSbo00,
  author    = {Enrico Blanzieri and C. Eccher and S. Forti and A.
               Sboner},
  title     = {Exploiting Classifier Combination for Early Melanoma
               Diagnosis Support},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {55--62},
  publisher = {Springer, Berlin}
}

@book{BlaGir54,
  title     = {Theory of Games and Statistical Decisions},
  publisher = {Wiley, New York},
  year      = {1954},
  author    = {Blackwell, D. and Girshick, M. A.}
}

@inproceedings{BlaGre09,
  author    = {M. Blaschko and A. Gretton},
  title     = {Learning Taxonomies by Dependence Maximization},
  booktitle = {Advances in Neural Information Processing Systems 21},
  pages     = {153--160},
  year      = {2009},
  publisher = {MIT Press}
}

@article{BlaKanMur16,
  title   = {Robust {Wasserstein} profile inference and applications to
             machine learning},
  author  = {Blanchet, Jose and Kang, Yang and Murthy, Karthyek},
  journal = {arXiv:1610.05627},
  year    = {2016}
}

@article{BlaKanZhaetal17,
  title   = {Data-driven Optimal Transport Cost Selection for
             Distributionally Robust Optimization},
  author  = {Blanchet, Jose and Kang, Yang and Zhang, Fan and Murthy,
             Karthyek},
  journal = {arXiv:1705.07152},
  year    = {2017}
}

@inproceedings{BlaKat96,
  author    = {E. Blanzieri and P. Katenkamp},
  title     = {Learning radial basis function networks on-line},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {37--45},
  publisher = {Morgan Kaufmann}
}

@incollection{Blake85,
  author    = {A. Blake},
  title     = {On lightness computation in Mondrian world},
  booktitle = {Central and peripheral mechanisms of colour vision},
  publisher = {Macmillan},
  year      = {1985},
  editor    = {S.~{Ottoson, T.~and Zeki}},
  pages     = {45--49}
}

@article{Blake89,
  author  = {A. Blake},
  title   = {Comparison of the efficiency of deterministic and
             stochastic algorithms for visual recostruction},
  journal = {IEEE Transactions on Pattern Analysis and Machine
             Intelligence},
  year    = {1989},
  volume  = {11},
  pages   = {2--12},
  number  = {1}
}

@article{BlaMac73,
  author  = {D. Blackwell and J. MacQueen},
  journal = {The Annals of Statistics},
  title   = {Ferguson distributions via Polya urn schemes},
  year    = {1973},
  volume  = {1},
  number  = {2},
  pages   = {353--355}
}

@misc{BlaMer98,
  author      = {C.~L. Blake and C.~J. Merz},
  title       = {{UCI} Repository of machine learning databases},
  year        = {1998},
  institution = {University of California, Irvine, Dept.~of Information and
                 Computer Sciences},
  url         = {http://www.ics.uci.edu/~mlearn/MLRepository.html}
}

@inproceedings{BlaMii95,
  author    = {Justine Blackmore and Risto Miikkulainen},
  title     = {Visualizing high-dimensional structure with the
               incremental grid-growing neural network},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {55--63},
  publisher = {Morgan Kaufmann}
}

@article{BlaMur19,
  author  = {Blanchet, Jose and Murthy, Karthyek},
  title   = {Quantifying Distributional Model Risk via Optimal
             Transport},
  journal = {Mathematics of Operations Research},
  year    = {2019},
  doi     = {10.1287/moor.2018.0936}
}

@article{Blanchet_QuantifyingDistributional_2019,
  title    = {Quantifying distributional model risk via optimal
              transport},
  volume   = {44},
  issn     = {0364-765X, 1526-5471},
  doi      = {10.1287/moor.2018.0936},
  abstract = {This paper deals with the problem of quantifying the
              impact of model misspeciﬁcation when computing general
              expected values of interest. The methodology that we
              propose is applicable in great generality; in particular,
              we provide examples involving path-dependent expectations
              of stochastic processes. Our approach consists of computing
              bounds for the expectation of interest regardless of the
              probability measure used, as long as the measure lies
              within a prescribed tolerance measured in terms of a
              ﬂexible class of distances from a suitable baseline
              model. These distances, based on optimal transportation
              between probability measures, include Wasserstein’s
              distances as particular cases. The proposed methodology is
              well suited for risk analysis and distributionally robust
              optimization, as we demonstrate with applications. We also
              discuss how to estimate the tolerance region
              nonparametrically using Skorokhod-type embeddings in some
              of these applications.},
  language = {en},
  number   = {2},
  journal  = {Mathematics of Operations Research},
  author   = {Blanchet, Jose and Murthy, Karthyek},
  month    = may,
  year     = {2019},
  pages    = {565--600},
  file     = {Blanchet, Murthy - Quantifying distributional model risk
              via optimal transport
              (2019).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Blanchet, Murthy - Quantifying distributional model
              risk via optimal transport (2019).pdf:application/pdf}
}

@article{Blanchet_RobustWasserstein_2019,
  title    = {Robust {Wasserstein} profile inference and applications to
              machine learning},
  volume   = {56},
  issn     = {0021-9002},
  doi      = {10.1017/jpr.2019.49},
  abstract = {We show that several machine learning estimators,
              including square-root least absolute shrinkage and
              selection and regularized logistic regression, can be
              represented as solutions to distributionally robust
              optimization problems. The associated uncertainty regions
              are based on suitably defined Wasserstein distances. Hence,
              our representations allow us to view regularization as a
              result of introducing an artificial adversary that perturbs
              the empirical distribution to account for out-of-sample
              effects in loss estimation. In addition, we introduce RWPI
              (robust Wasserstein profile inference), a novel inference
              methodology which extends the use of methods inspired by
              empirical likelihood to the setting of optimal transport
              costs (of which Wasserstein distances are a particular
              case). We use RWPI to show how to optimally select the size
              of uncertainty regions, and as a consequence we are able to
              choose regularization parameters for these machine learning
              estimators without the use of cross validation. Numerical
              experiments are also given to validate our theoretical
              findings.},
  number   = {3},
  journal  = {Journal of Applied Probability},
  author   = {Blanchet, Jose and Kang, Yang and Murthy, Karthyek},
  year     = {2019},
  keywords = {Distributionally robust optimization, empirical
              likelihood, limit characterization of optimal Wasserstein
              ball radius and regularization parameter, logistic
              regression, regularization, square-root LASSO, support
              vector machine, Wasserstein distance},
  pages    = {830--857},
  file     = {Blanchet et al - Robust Wasserstein profile inference and
              applications to machine learning
              (2019).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Blanchet et al - Robust Wasserstein profile
              inference and applications to machine learning
              (2019).pdf:application/pdf}
}

@article{BlaRan96,
  author  = {Michael Black and Anand Rangarajan},
  title   = {On the unification of line processes, outlier rejection,
             and robust statistics with applications in early vision},
  journal = {International Journal of Computer Vision},
  year    = {1996},
  volume  = {19},
  pages   = {57--91},
  number  = {1}
}

@inproceedings{BlaRic99,
  author    = {Enrico Blanzieri and Francesco Ricci},
  title     = {A minimum risk metric for nearest neighbor
               classification},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {22--31},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{BlaRotBroPeretal04,
  author    = {A. Blake and C. Rother and M. Brown and P. Perez and P.
               Torr},
  title     = {Interactive Image Segmentation Using an Adaptive {GMMRF}
               Model},
  booktitle = {Proc. European Conf. on Computer Vision},
  year      = {2004}
}

@inproceedings{BlaSchBulBuretal96,
  author    = {V. Blanz and B.~{Sch\"olkopf} and H.~{B\"ulthoff} and C.
               Burges and V. Vapnik and T. Vetter},
  title     = {Comparison of view-based object recognition algorithms
               using realistic {3D} models},
  booktitle = {Artificial Neural Networks {ICANN'96}},
  year      = {1996},
  editor    = {C.~von der Malsburg and W.~von Seelen and J.~C.
               Vorbr{\"u}ggen and B. Sendhoff},
  volume    = {1112},
  series    = {Lecture Notes in Comput. Sci.},
  pages     = {251--256},
  address   = {Berlin},
  publisher = {Springer-Verlag}
}

@inproceedings{Blasig96,
  author    = {Reinhard Blasig},
  title     = {Discrete sequence prediction with commented {M}arkov
               models},
  booktitle = {Grammatical Inference: {L}earning Syntax from Sentences,
               3rd International Colloquium, ICGI-96, Montpellier, France,
               September 25--27, 1996, Proceedings},
  year      = {1996},
  volume    = {1147},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {191--202},
  publisher = {Springer, Berlin}
}

@unpublished{BlaVapBur95,
  author = {V. Blanz and V. Vapnik and C. Burges},
  title  = {Multiclass Discrimination with an extended Support Vector
            Machine},
  note   = {Talk given at AT\&T Bell Labs},
  year   = {1995}
}

@inproceedings{BlaVet99,
  author    = {V. Blanz and T. Vetter},
  title     = {A Morphable Model for the Synthesis of {3D} Faces},
  booktitle = {SIGGRAPH'99 Conference Proceedings},
  year      = {1999},
  pages     = {187--194},
  address   = {Los Angeles},
  publisher = {ACM Press}
}

@article{BlaWilMar97,
  author  = {K.~L. Blackmore and R.~C. Williamson and I.~M.~Y.
             Mareels},
  title   = {Decision Region Approximation by Polynomials or Neural
             Networks},
  journal = {IEEE Transactions on Information Theory},
  year    = {1997},
  volume  = {43},
  pages   = {903--907}
}

@inproceedings{BlaWilMarSet95,
  author    = {K.~L. Blackmore and R.~C. Williamson and I.~M. Mareels and
               W.~A. Sethares},
  title     = {Online Learning via Congregational Gradient Descent},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1995},
  pages     = {265--272},
  address   = {New York},
  publisher = {ACM Press}
}

@book{BlaZis87,
  title     = {Visual Reconstruction},
  publisher = {{MIT} Press},
  year      = {1987},
  author    = {A. Blake and A. Zisserman},
  address   = {Cambridge, MA}
}

@inproceedings{BleBagMcC02,
  author    = {D. Blei and J. Bagnell and A. McCallum},
  title     = {Learning with Scope, with Application to Information
               Extraction and Classification},
  booktitle = {Uncertainty in Artificial Intelligence: the Eighteenth
               Conference (UAI-2002)},
  year      = {2002},
  pages     = {53-60},
  address   = {San Francisco, CA},
  month     = june,
  publisher = {Morgan Kaufmann Publishers},
  url       = {http://www.cs.berkeley.edu/~blei/papers/BleiBagnellMcCallum_LearningWithScope.ps.gz}
}

@inproceedings{BleFra10,
  title     = {Distance dependent Chinese restaurant processes},
  author    = {D.M. Blei and P. Frazier},
  booktitle = {27th International Conference on Machine Learning {ICML}},
  publisher = {Omnipress},
  year      = {2010},
  editor    = {J. F{\"u}rnkranz and T. Joachims},
  pages     = {87--94},
  url       = {http://www.icml2010.org/papers/551.pdf}
}

@incollection{BleGriJorTen04,
  author    = {D. Blei and T. Griffiths and M. Jordan and J. Tenenbaum},
  title     = {Hierarchical Topic Models and the Nested {C}hinese
               Restaurant Process},
  booktitle = {Advances in Neural Information Processing Systems 16},
  publisher = {{MIT} Press},
  year      = {2004},
  editor    = {S. Thrun and L. Saul and B. Sch\"{o}lkopf},
  address   = {Cambridge, MA}
}

@inproceedings{BleJor03,
  author    = {Blei, D. and Jordan, M.},
  title     = {Modeling Annotated Data},
  booktitle = {the 26th annual international {ACM} {SIGIR} conference on
               Research and development in information retrieval},
  year      = {2003},
  pages     = {127-134},
  month     = august,
  publisher = {ACM Press}
}

@inproceedings{BleJor05,
  author    = {D. Blei and M. Jordan},
  booktitle = {Bayesian Analysis},
  title     = {Variational inference for Dirichlet process mixtures},
  year      = {2005},
  volume    = {1},
  number    = {1},
  pages     = {121--144}
}

@inproceedings{BleKae99,
  author    = {D. Blei and L. Kaelbling },
  title     = {Shortest paths in a dynamic uncertain domain},
  booktitle = { {IJCAI} Workshop on Adaptive Spatial Representations of
               Dynamic Environments},
  year      = {1999},
  url       = {http://www.cs.berkeley.edu/~blei/papers/BleiKaelbling_ShortestPathsInDynamicUncertainDomains.ps.gz}
}

@inproceedings{BleLaf06,
  title     = {Dynamic topic models},
  author    = {D. M. Blei and J. D. Lafferty},
  booktitle = {ICML},
  publisher = {ACM},
  year      = {2006},
  volume    = {148},
  editor    = {W. W. Cohen and A. Moore},
  pages     = {113--120},
  url       = {http://doi.acm.org/10.1145/1143844.1143859}
}

@article{BleLaf07,
  author  = {D. Blei and J. Lafferty},
  title   = {A correlated topic model of Science},
  journal = {Annals of Applied Statistics},
  volume  = {1},
  number  = {1},
  year    = {2007},
  pages   = {17--35}
}

@inproceedings{Blelloch87,
  author    = {G.~E. Blelloch},
  title     = {Scans and other primitives for parallel computation},
  booktitle = {Proc.\ International Conference on Parallel Processing},
  year      = {1987},
  pages     = {355--362},
  month     = aug
}

@inproceedings{BleMcA07,
  author    = {David Blei and Jon D. McAuliffe},
  title     = {Supervised topic models},
  booktitle = nips,
  year      = {2007}
}

@inproceedings{BleMor01,
  author    = {D. Blei and P. Moreno},
  title     = {Topic segmentation with an aspect hidden {M}arkov model},
  booktitle = {the 24th annual international ACM SIGIR conference on
               Research and development in information retrieval},
  year      = {2001},
  pages     = {343-348},
  publisher = {ACM Press},
  location  = {New Orleans, Louisiana, United States},
  url       = {http://www.cs.berkeley.edu/~blei/papers/BleiMoreno_TopicSegmentation.ps.gz}
}

@inproceedings{BleNgJor02,
  author    = {D. Blei and A. Ng and M. Jordan},
  title     = {Latent Dirichlet Allocation},
  booktitle = {Advances in Neural Information Processing Systems 14},
  year      = {2002},
  editor    = {T.~G. Dietterich and S. Becker and Z. Ghahramani},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press},
  url       = {http://www.cs.berkeley.edu/~blei/papers/BleiNgJordan_LatentDirichletAllocation.ps.gz}
}

@article{BleNgJor03,
  author  = {Blei, D. and Ng, A. and Jordan, M.},
  title   = {Latent {D}irichlet Allocation},
  journal = {Journal of Machine Learning Research},
  year    = {2003},
  volume  = {3},
  pages   = {993-1022},
  month   = jan
}

@techreport{BleVert11,
  author = {K. Bleakley and J.-P. Vert},
  title  = {The group fused Lasso for multiple change-point
            detection},
  year   = {2011},
  number = {HAL 00602121}
}

@inproceedings{BliDrePer07,
  author    = {Blitzer, John and Dredze, Mark and Pereira, Fernando},
  title     = {Biographies, Bollywood, Boom-boxes and Blenders: Domain
               Adaptation for Sentiment Classification},
  booktitle = acl,
  year      = {2007}
}

@inproceedings{BliMcDPer06,
  title        = {Domain adaptation with structural correspondence
                  learning},
  author       = {Blitzer, John and McDonald, Ryan and Pereira, Fernando},
  booktitle    = emnlp,
  pages        = {120--128},
  year         = {2006},
  organization = {Association for Computational Linguistics}
}

@article{Block62,
  author  = {H.~D. Block},
  title   = {The perceptron: A model for brain functioning},
  journal = {Reviews of Modern Physics},
  year    = {1962},
  volume  = {34},
  pages   = {123--135},
  note    = {Reprinted in {\it Neurocomputing} by Anderson and
             Rosenfeld}
}

@inproceedings{BloRaeRam98,
  author    = {Hendrik Blockeel and Luc De Raedt and Jan Ramon},
  title     = {Top-down induction of clustering trees},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {55--63},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{BloSho91,
  author    = {J. Bloomenthal and K. Shoemake},
  title     = {Convolution Surfaces},
  booktitle = {Proc.~SIGGRAPH 91},
  year      = {1991},
  pages     = {251--256}
}

@book{BloSte83,
  title     = {Least absolute deviations: theory, applications and
               algorithms},
  publisher = {Birkh\"auser},
  year      = {1983},
  author    = {P. Bloomfield and W.L. Steiger},
  address   = {Boston}
}

@article{BluBlu75,
  author  = {L. Blum and M. Blum},
  title   = {Toward a Mathematical Theory of Inductive Inference},
  journal = {Inform. Control},
  year    = {1975},
  volume  = {28},
  pages   = {125--155},
  number  = {2},
  month   = jun
}

@article{BluBur00,
  author    = {A. Blum and C. Burch},
  title     = {On-line Learning and the Metrical Task System Problem},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {39},
  pages     = {35--58},
  number    = {1},
  comment   = {first in 10th COLT '97},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{BluCha01,
  author    = {A. Blum and S. Chawla},
  booktitle = icml,
  title     = {Learning from Labeled and Unlabeled Data Using Graph
               Mincuts},
  year      = {2001}
}

@inproceedings{BluCha92,
  author    = {A. Blum and P. Chalasani},
  title     = {Learning switching concepts},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {231--242},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BluCha93,
  author    = {A. Blum and P. Chalasani},
  title     = {An on-line algorithm for improving performance in
               navigation.},
  booktitle = {Proc. 34th Annu. IEEE Sympos. Found. Comput. Sci.},
  year      = {1993},
  pages     = {2--11},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@inproceedings{BluChaGolSlo95,
  author    = {A. Blum and Prasad Chalasani and Sally A. Goldman and
               Donna K. Slonim},
  title     = {Learning with unreliable boundary queries},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {98--107},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BluChaJac93,
  author    = {A. Blum and P. Chalasani and J. Jackson},
  title     = {On learning embedded symmetric concepts},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {337--346},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BluDun02,
  author    = {Avrim Blum and John Dunagan},
  title     = {Smoothed analysis of the perceptron algorithm for linear
               programming},
  booktitle = {SODA '02: the thirteenth annual ACM-SIAM symposium on
               Discrete algorithms},
  year      = 2002,
  pages     = {905--914},
  location  = {San Francisco, California},
  publisher = {Society for Industrial and Applied Mathematics},
  address   = {Philadelphia, PA, USA}
}

@phdthesis{Blue98,
  author = {J.~A. Blue},
  title  = {A Hybrid of Tabu Search and Local Descent Algorithms with
            Applications in Artificial Intelligence},
  school = {Rensselaer Polytechnic Institute},
  year   = {1998}
}

@article{BluEhrHauWar87,
  author  = {A. Blumer and A. Ehrenfeucht and D. Haussler and M. K.
             Warmuth},
  title   = {Occam's Razor},
  journal = {Inform. Proc. Lett.},
  year    = {1987},
  volume  = {24},
  pages   = {377--380},
  month   = apr,
  annote  = {Was UCSC Computer Science Laboratory CRL-86-2},
  comment = {Defines `Occam-algorithm' which may produce a hypothesis
             of complexity $n^c m^\alpha$ for fixed $c$ and $\alpha <
             1$, and shows that Occam-algorithms need only polynomially
             many samples.}
}

@article{BluEhrHauWar89,
  author  = {Anselm Blumer and Andrzej Ehrenfeucht and David Haussler
             and M. K. Warmuth},
  title   = {Learnability and the {V}apnik-{C}hervonenkis {D}imension},
  journal = {Journal of the ACM},
  year    = {1989},
  volume  = {36},
  pages   = {929--965},
  number  = {4}
}

@article{BluFloPraRivetal73,
  author  = {M. Blum and R.W. Floyd and V. Pratt and R. Rivest and R.
             Tarjan},
  title   = {Time bounds for selection},
  journal = {Journal of Computer System Sciences},
  year    = 1973,
  volume  = 7,
  number  = 4,
  pages   = {448--461},
  month   = {August}
}

@article{BluFriKanVem98,
  author  = {A. Blum and A.M. Frieze and R. Kannan and Santosh
             Vempala},
  title   = {A Polynomial-Time Algorithm for Learning Noisy Linear
             Threshold Functions},
  journal = {Algorithmica},
  year    = {1998},
  volume  = {22},
  pages   = {35--52},
  number  = {1/2}
}

@inproceedings{BluFurJacKeaetal94,
  author    = {A. Blum and M. Furst and J. Jackson and M. Kearns and Y.
               Mansour and S. Rudich},
  title     = {Weakly Learning {DNF} and Characterizing Statistical Query
               Learning Using Fourier Analysis},
  booktitle = {Proc. of Twenty-sixth ACM Symposium on Theory of
               Computing},
  year      = {1994}
}

@article{BluHelLit95,
  author  = {A. Blum and Lisa Hellerstein and Nick Littlestone},
  title   = {Learning in the presence of finitely or infinitely many
             irrelevant attributes},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1995},
  volume  = {50},
  pages   = {32--40},
  number  = {1},
  note    = {Earlier version in 4th COLT, 1991}
}

@article{BluKal98,
  author    = {A. Blum and A. Kalai},
  title     = {A note on learning from multiple-instance examples},
  journal   = {Machine Learning},
  year      = {1998},
  volume    = {30},
  pages     = {23--29},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{BluKal99,
  author    = {A. Blum and A. Kalai},
  title     = {Universal Portfolios With and Without Transaction Costs},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {35},
  pages     = {193--205},
  number    = {3},
  comment   = {first in 10th COLT '97},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{BluKalLan99,
  author    = {A. Blum and A. Kalai and J. Langford},
  title     = {Beating the hold-out: bounds for k-fold and progressive
               cross-validation},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {203--208},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{BluKalWas00,
  author    = {A. Blum and A. Kalai and Hal Wasserman},
  title     = {Noise-tolerant learning, the parity problem, and the
               statistical query model},
  booktitle = {the Thirty-Second Annual ACM Symposium on Theory of
               Computing, May 21--23, 2000, Portland, OR, USA},
  year      = {2000},
  pages     = {435--440},
  publisher = {ACM Press}
}

@article{BluKan97,
  author  = {A. Blum and Ravidran Kannan},
  title   = {Learning an Intersection of a Constant Number of
             Halfspaces over a Uniform Distribution},
  journal = {J. Comput. Syst. Sci.},
  year    = {1997},
  volume  = {54},
  pages   = {371--380},
  number  = {2},
  note    = {First appeared in FOCS '93}
}

@inproceedings{BluKhaKusPitetal94,
  author    = {A. Blum and R. Khardon and E. Kushilevitz and L. Pitt and
               D. Roth},
  title     = {On learning read-{\it k}-satisfy-{\it j} {DNF}},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {110--117},
  publisher = {ACM Press, New York, NY}
}

@article{BluLit89,
  author  = {A. Blumer and N. Littlestone},
  title   = {Learning Faster than Promised by the
             {V}apnik-{C}hervonenkis Dimension},
  journal = {Discrete Applied Mathematics},
  year    = {1989},
  volume  = {24},
  pages   = {47--53},
  annote  = {Was also in First Montreal Conference on Combinatorics and
             Computer Science, University of Montreal, 1987}
}

@article{Blum54,
  author  = {Julius R. Blum},
  title   = {Multidimensional Stochastic Approximation Methods},
  journal = amas,
  year    = 1954,
  volume  = 25,
  pages   = {737--744}
}

@article{Blum89,
  author  = {E. K. Blum},
  title   = {Approximation of Boolean functions by sigmoidal networks:
             Part {I}: {XOR} and other two-variable functions},
  journal = {Neural Computation},
  year    = {1989},
  volume  = {1},
  pages   = {532--540},
  month   = feb
}

@mastersthesis{Blum89b,
  author = {A. Blum},
  title  = {On the Computational Complexity of Training Simple Neural
            Networks},
  school = {MIT Department of Electrical Engineering and Computer
            Science},
  year   = {1989},
  month  = may,
  note   = {(Published as Laboratory for Computer Science Technical
            Report MIT/LCS/TR-445 (May, 1989))}
}

@inproceedings{Blum90,
  author    = {A. Blum},
  title     = {Separating distribution-free and mistake-bound learning
               models over the {B}oolean domain},
  booktitle = {Proc. of the 31st Symposium on the Foundations of Comp.
               Sci.},
  year      = {1990},
  pages     = {211--218},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@article{Blum90b,
  author  = {A. Blum},
  title   = {Separating {PAC} and Mistake-Bound Learning Models over
             the Boolean Domain},
  journal = {Proc. 31th Annu. IEEE Sympos. Found. Comput. Sci.},
  year    = {1990}
}

@article{Blum92,
  author    = {A. Blum},
  title     = {Learning Boolean Functions in an Infinite Attribute
               Space},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {9},
  pages     = {373--386},
  number    = {4},
  month     = oct,
  annote    = {Was in STOC90},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Blum95,
  author    = {A. Blum},
  title     = {Empirical support for {W}innow and weighted-majority based
               algorithms: results on a calendar scheduling domain},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {64--72},
  address   = {San Francisco, CA.},
  publisher = {Morgan Kaufmann}
}

@article{Blum97,
  author    = {A. Blum},
  title     = {Empirical Support for Winnow and Weighted-Majority
               Algorithms: Results on a Calendar Scheduling Domain},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {26},
  pages     = {5--23},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Blum97b,
  author    = {Manuel Blum},
  title     = {Program Error Detection/Correction: {T}urning {PAC}
               Learning into {PERFECT} Learning},
  booktitle = alt-ws,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {1--1},
  publisher = {Springer-Verlag}
}

@inproceedings{BluMit98,
  author    = {A. Blum and T. Mitchell},
  title     = {Combining labeled and unlabeled data with co-training},
  booktitle = {Proc. 11th Annu. Conf. on Comput. Learning Theory},
  year      = {1998},
  pages     = {92--100},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{blummitchell98,
  author    = {A. Blum and T. Mitchell},
  title     = {Combining labeled and unlabeled data with co-training},
  booktitle = {Conference on Learning Theory (COLT)},
  year      = {1998},
  pages     = {92--100}
}

@inproceedings{BluRagSch91,
  author    = {A. Blum and P. Raghavan and B. Schieber},
  title     = {Navigating in Unfamiliar Geometric Terrain},
  booktitle = {Proc. 23th Annu. ACM Sympos. Theory Comput.},
  year      = {1991},
  pages     = {494--504},
  publisher = {ACM}
}

@inproceedings{BluRiv88,
  author    = {A. Blum and R.~L. Rivest},
  title     = {Training a three-neuron neural net is {NP-complete}},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1988},
  pages     = {9--18},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@incollection{BluRiv89,
  author    = {A. Blum and R. L. Rivest},
  title     = {Training a 3-node neural net is {NP-Complete}},
  booktitle = {Advances in Neural Information Processing Systems 1},
  publisher = {Morgan Kaufmann},
  year      = {1989},
  editor    = {D.~S.~Touretzky},
  pages     = {494-501},
  annote    = {Was in COLT88}
}

@inproceedings{BluRud92,
  author    = {A. Blum and S. Rudich},
  title     = {Fast Learning of $k$-term {DNF} Formulas with Queries},
  booktitle = {Proc. of Twenty-Fourth ACM Symposium on Theory of
               Computing},
  year      = {1992},
  pages     = {382--389},
  publisher = {ACM}
}

@article{BluRud95,
  author  = {A. Blum and Stephen Rudich},
  title   = {Fast learning of k-term {DNF} formulas with queries},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1995},
  volume  = {51},
  pages   = {367--373},
  number  = {3}
}

@inproceedings{BluSin90,
  author    = {A. Blum and M. Singh},
  title     = {Learning functions of $k$ terms},
  booktitle = {Proc. 3rd Annu. Workshop on Comput. Learning Theory},
  year      = {1990},
  pages     = {144--153},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{BoaPit89,
  author    = {R. A. Board and L. Pitt},
  title     = {Semi-Supervised Learning},
  journal   = {Machine Learning},
  year      = {1989},
  volume    = {4},
  pages     = {41--65},
  number    = {1},
  annote    = {Univ. of Illinois TR-87-1372 in 1987},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{BoaPit92,
  author  = {R. Board and L. Pitt},
  title   = {On the Necessity of {O}ccam Algorithms},
  journal = {Theoret. Comput. Sci.},
  year    = {1992},
  volume  = {100},
  pages   = {157--184},
  annote  = {Was Univ of Illinois at Urbana TR-89-1544 in 1989 and in
             STOC90}
}

@inproceedings{Bochner32,
  author    = {S. Bochner},
  title     = {{Vorlesungen ueber Fouriersche Integrale}},
  booktitle = {Akademische Verlagsgesellschaft},
  year      = {1932},
  address   = {Leipzig}
}

@article{Bochner33,
  author  = {S. Bochner},
  title   = {Monotone {F}unktionen, {S}tieltjessche {I}ntegrale und
             harmonische {A}nalyse},
  journal = {Math. Ann.},
  year    = {1933},
  volume  = {108},
  pages   = {378-410}
}

@book{Bochner59,
  title     = {Lectures on {Fourier} integral},
  publisher = {Princeton Univ.\ Press},
  year      = {1959},
  author    = {S. Bochner},
  address   = {Princeton, New Jersey}
}

@book{Bogachev_TopologicalVector_2017,
  address   = {Cham, Switzerland},
  series    = {Springer {Monographs} in {Mathematics}},
  title     = {Topological vector spaces and their applications},
  isbn      = {978-3-319-57116-4 978-3-319-57117-1},
  language  = {en},
  publisher = {Springer International Publishing},
  author    = {Bogachev, V.I. and Smolyanov, O.G.},
  year      = {2017},
  doi       = {10.1007/978-3-319-57117-1},
  keywords  = {topological vector spaces},
  file      = {Bogachev, Smolyanov - Topological vector spaces and their
               applications
               (2017).pdf:/Users/zcranko/Documents/Library/Book/Bogachev,
               Smolyanov - Topological vector spaces and their
               applications (2017).pdf:application/pdf}
}

@article{BohBra94,
  author    = {Marko Bohanec and Ivan Bratko},
  title     = {Trading accuracy for simplicity in decision trees},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {15},
  pages     = {223--250},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@book{Bois61,
  title     = {Tables of Indefinite Integrals},
  publisher = {Dover Publications},
  year      = {1961},
  author    = {G. Petit Bois}
}

@inproceedings{BoiTecMarBowetal00,
  author    = {Mihai Boicu and Gheorghe Tecuci and Dorin Marcu and M.
               Bowman and Ping Shyr and Florin Ciucu and Cristian
               Levcovici},
  title     = {Disciple-{COA}: {F}rom Agent Programming to Agent
               Teaching},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {73--80},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{BojBreHooSwe95,
  author  = {A.~W. Bojanczyk and R.~P. Brent and F.~R.~de Hoog and D.
             R. Sweet},
  title   = {On the Stability of the Bareiss and related Toeplitz
             Factorization Algorithms},
  journal = {{SIAM} J. Matrix Anal.\ Appl.},
  year    = {1995},
  volume  = {16},
  pages   = {40--57},
  number  = {1},
  month   = jan
}

@inproceedings{BojGun19,
  title     = {Certifiable Robustness to Graph Perturbations},
  author    = {Aleksandar Bojchevski and G{\"u}nnemann, Stephan},
  booktitle = nips,
  year      = {2019}
}

@inproceedings{BolGelMatMauetal01,
  author    = {Igor A. Bolshakov and Alexander Gelbukh and V\'aclav
               Matousek and Pavel Mautner and Roman Moucek and Karel
               Tauser},
  title     = {Text segmentation into paragraphs based on local text
               cohesion},
  booktitle = {In Proc.~TSD 2001 : text, speech and dialogue},
  pages     = {158--166},
  year      = {2001}
}

@inproceedings{BolGho98,
  author    = {Kurt D. Bollacker and Joydeep Ghosh},
  title     = {A supra-classifier architecture for scalable knowledge
               reuse},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {64--72},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{Bollen84,
  author  = {Jo A. M. Bollen},
  title   = {Numerical Stability of Descent Methods for Solving Linear
             Equations},
  journal = {Numerische Mathematik},
  year    = {1984}
}

@techreport{BolPer96,
  author      = {Michel de Bollivier and Domenico Perrotta},
  title       = {Using {B}ayesian Methods for Avoiding Overfitting and for
                 Ranking Networks in Multilayer Perceptrons Learning},
  institution = {Royal Holloway, University of London},
  year        = {1996},
  note        = {NC2-TR-1996-012}
}

@article{BolRag93,
  author  = {Peter Bollmann-Sdorra and Vijay V. Raghavan},
  title   = {On the Delusiveness of Adopting a Common Space for
             Modeling {IR} Objects: Are Queries Documents?},
  journal = {Journal of the American Society for Information Science},
  year    = {1993},
  volume  = {44},
  pages   = {579--587},
  number  = {10}
}

@inproceedings{BolWon87,
  author    = {Peter Bollmann and S.~K.~M. Wong},
  title     = {Adaptive Linear Information Retrieval Models},
  booktitle = {the 10th Annual {ACM} {SIGIR} Conference an Research and
               Development in Information Retrieval},
  year      = {1987},
  pages     = {157--163}
}

@techreport{BonBieConCoretal05,
  author      = {P. Bonami and L.T. Biegler and A.R. Conn and G. Cornuejols
                 and I.E. Grossmann and C.D. Laird and J. Lee and A. Lodi
                 and F. Margot and N.Sawaya and A. Waechter},
  title       = {An Algorithmic Framework for Convex Mixed Integer
                 Nonlinear Programs},
  institution = {IBM},
  year        = {2005},
  type        = {Research Report},
  number      = {RC23771},
  month       = {October}
}

@inproceedings{BonBirBer99,
  author    = {Gianluca Bontempi and Mauro Birattari and Hugues Bersini},
  title     = {Local learning for iterated time series prediction},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {32--38},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{Boneh95,
  author    = {Dan Boneh},
  title     = {Learning using group representations},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {418--426},
  publisher = {ACM Press, New York, NY}
}

@article{Boneskyetal07,
  author  = {Thomas Bonesky and Kristian Bredies and Dirk A. Lorenz and
             Peter Maass},
  title   = {A Generalized Conditional Gradient Method for Nonlinear
             Operator Equations with Sparsity Constraints},
  journal = {Inverse Problems},
  year    = {2007},
  volume  = {23},
  pages   = {2041--2058}
}

@inproceedings{BonGef98,
  author    = {Blai Bonet and H{\'e}ctor Geffner},
  title     = {Learning sorting and decision trees with {POMDP}s},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {73--81},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@book{BonGilLemSag00,
  author    = {J. Fr\'{e}d\'{e}ric Bonnans and J. Charles Gilbert and
               Claude Lemar\'{e}chal and Claudia A. Sagastiz\'{a}bal},
  title     = {Numerical Optimization},
  publisher = {Springer},
  year      = 2000,
  edition   = {Second}
}

@inproceedings{BonLip93,
  author    = {D. Boneh and R. Lipton},
  title     = {Amplification of weak learning under the uniform
               distribution},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {347--351},
  publisher = {ACM Press, New York, NY}
}

@incollection{Bonnans_GeneralizedConvexity_2019,
  address      = {Cham, Switzerland},
  title        = {Generalized convexity and transportation theory},
  isbn         = {978-3-030-14976-5 978-3-030-14977-2},
  language     = {en},
  booktitle    = {Convex and {Stochastic} {Optimization}},
  publisher    = {Springer International Publishing},
  author       = {Bonnans, J. Frédéric},
  collaborator = {Bonnans, J. Frédéric},
  year         = {2019},
  doi          = {10.1007/978-3-030-14977-2_9},
  pages        = {283--301},
  file         = {Bonnans - Generalized Convexity and Transportation Theory
                  (2019).pdf:/Users/zcranko/Documents/Library/Book
                  Section/Bonnans - Generalized Convexity and Transportation
                  Theory (2019).pdf:application/pdf}
}

@book{BonRou91,
  editor    = {D.~Bonchev and D. H. Rouvray},
  title     = {Chemical Graph Theory: Introduction and Fundamentals},
  publisher = {Gordon and Breach Science Publishers},
  year      = {1991},
  volume    = {1},
  address   = {London, UK}
}

@article{BooGai89,
  author    = {John H. Boose and Brian R. Gaines},
  title     = {Knowledge Acquisition for Knowledge-Based Systems: Notes
               on the State-of-the-Art},
  journal   = {Machine Learning},
  year      = {1989},
  volume    = {4},
  pages     = {377--394},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{BooGreGeo13,
  title     = {Hilbert Space Embeddings of Predictive State
               Representations},
  author    = {Boots, Byron and Gretton, Arthur and Gordon, Geoffrey J},
  booktitle = uai,
  year      = 2013
}

@article{Booker88,
  author    = {Lashon B. Booker},
  title     = {Classifier Systems that Learn Internal World Models},
  journal   = {Machine Learning},
  year      = {1988},
  volume    = {3},
  pages     = {161--192},
  publisher = {Kluwer Academic Publishers, Boston}
}

@incollection{Boor69,
  author    = {C.~de Boor},
  title     = {On the approximation by {$\gamma$-Polynomials}},
  booktitle = {Approximation with special emphasis on spline functions},
  publisher = {Academic Press},
  year      = {1969},
  editor    = {I.~J. Schoenberg},
  pages     = {157--183},
  address   = {New York}
}

@incollection{Boor73,
  author    = {C.~de Boor},
  title     = {Good approximation by splines with variable knots},
  booktitle = {Spline functions and approximation theory},
  publisher = {Birkhauser-Verlag},
  year      = {1973},
  editor    = {A. Meir and A. Sharma},
  volume    = {21},
  pages     = {57--72},
  address   = {Basel}
}

@article{Boor78,
  author  = {C.~de Boor},
  title   = {The approximation of functions and linear functionals:
             best vs.~good approximation},
  journal = {Symposia in Applied Mathematics},
  year    = {1978},
  volume  = {22},
  pages   = {53--70}
}

@book{Boor78b,
  title     = {A Practical Guide to Splines},
  publisher = {Springer},
  year      = {1978},
  author    = {C.~de Boor},
  address   = {New York}
}

@incollection{Boor87,
  author    = {C.~de Boor},
  title     = {Multivariate approximation},
  booktitle = {The state of the art in numerical analysis},
  publisher = {Clarendon Press},
  year      = {1987},
  editor    = {A. Iserles and M.~J.~D. Powell},
  pages     = {87--109},
  address   = {Oxford}
}

@incollection{Boor90,
  author    = {C.~de Boor},
  title     = {Quasi-interpolants and approximation power of multivariate
               splines},
  booktitle = {Computation of Curves and Surfaces},
  publisher = {Kluwer Academic},
  year      = {1990},
  editor    = {M. Gasca and C.~A. Micchelli},
  pages     = {313--345},
  address   = {Dordrecht, Netherlands}
}

@book{Boothby86,
  title     = {An introduction to differentiable manifolds and Riemannian
               geometry},
  publisher = {Academic Press},
  year      = {1986},
  author    = {W.~M. Boothby},
  edition   = {2nd}
}

@article{BorBotGal09,
  author  = {Antoine Bordes and L\'{e}on Bottou and Patrick Gallinari},
  title   = {{SGD-QN}: Careful Quasi-{N}ewton Stochastic Gradient
             Descent},
  journal = {Journal of Machine Learning Research},
  year    = {2009},
  volume  = {10},
  pages   = {1737--1754},
  month   = {July},
  url     = {http://leon.bottou.org/papers/bordes-bottou-gallinari-2009}
}

@inproceedings{BorBotGalWes07,
  author    = {Antoine Bordes and L\'{e}on Bottou and Patrick Gallinari
               and Jason Weston},
  title     = {Solving multiclass support vector machines with {LaRank}},
  booktitle = icml,
  year      = 2007,
  pages     = {89--96},
  publisher = {ACM},
  address   = {New York, NY, USA}
}

@article{BorBurDeh01,
  author  = {S. Borovkova and R. Burton and H. Dehling},
  title   = {Limit Theorems for Functionals of Mixing Processes with
             Applications to Dimension Estimation},
  journal = {Transactions of the American Mathematical Society},
  year    = 2001,
  volume  = 353,
  number  = 11,
  pages   = {4261--4318}
}

@article{BorBurDeh99,
  author  = {S. Borovkova and R. Burton and H. Dehling},
  title   = {Consistency of the Takens Estimator for the Correlation
             Dimension},
  journal = {Annals of Applied Probability},
  year    = {1999},
  volume  = {9},
  number  = {2},
  pages   = {376--390}
}

@article{BorCraHam90,
  author  = {E. Boros and Y. Crama and P. L. Hammer},
  title   = {Polynomial-time inference of all valid implications for
             {H}orn and related formulae},
  journal = {Annals of Mathematics and Artificial Intelligence},
  year    = {1990},
  volume  = {1},
  pages   = {21--32}
}

@article{BorErtWesBot05,
  author  = {Antoine Bordes and Seyda Ertekin and Jason Weston and
             L\'eon Bottou},
  title   = {Fast Kernel Classifiers with Online and Active Learning},
  journal = {Journal of Machine Learning Research},
  year    = {2005},
  volume  = {6},
  pages   = {1579--1619},
  month   = {September}
}

@techreport{BorGha09,
  author = {Karsten Borgwardt and Zoubin Ghahramani},
  title  = {Bayesian Two-sample Tests},
  year   = {2009},
  number = {04-104},
  note   = {http://arxiv.org/abs/0906.4032}
}

@article{BorGreRasKrietal06,
  author  = {K. M. Borgwardt and A. Gretton and M. J. Rasch and H.-P.
             Kriegel and B. Sch{\"o}lkopf and A. J. Smola},
  title   = {Integrating structured biological data by Kernel Maximum
             Mean Discrepancy},
  journal = {Bioinformatics (ISMB)},
  year    = {2006},
  volume  = {22},
  number  = {14},
  pages   = {e49--e57}
}

@inproceedings{BorGreRasKrietal06b,
  author    = {K. M. Borgwardt and A. Gretton and M.J. Rasch and H.P.
               Kriegel and B. Sch{\"o}lkopf and A.J. Smola},
  title     = {Integrating structured biological data by Kernel Maximum
               Mean Discrepancy},
  booktitle = {ISMB},
  year      = {2006}
}

@inproceedings{BorGutVisSmo05,
  author    = {K. M. Borgwardt and O. Guttman and S.~V.~N. Vishwanathan
               and A. J. Smola},
  title     = {Joint Regularization},
  booktitle = {the European Symposium on Artificial Neural Networks
               (ESANN 2005)},
  year      = {2005},
  address   = {Brugge, Belgium}
}

@phdthesis{Borgwardt07,
  author  = {Karsten M. Borgwardt},
  title   = {Graph Kernels},
  school  = {Ludwig-Maximilians-University},
  year    = {2007},
  address = {Munich, Germany}
}

@inproceedings{BorIbaMak97,
  author    = {Endre Boros and Toshihide Ibaraki and Kazuhisa Makino},
  title     = {Monotone extensions of {B}oolean data sets},
  booktitle = alt-ws,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {161--175},
  publisher = {Springer-Verlag}
}

@book{Borisovich85,
  title     = {Introduction to topology},
  publisher = {{MIR} Publishers},
  year      = {1985},
  author    = {Yu. Borisovich},
  address   = {Moscow}
}

@book{Borkar_ProbabilityTheory_1995,
  address    = {New York, NY, USA},
  series     = {Universitext},
  title      = {Probability theory: an advanced course},
  isbn       = {978-0-387-94558-3},
  shorttitle = {Probability theory},
  language   = {en},
  publisher  = {Springer-Science+Business Media},
  author     = {Borkar, Vivek S.},
  year       = {1995},
  file       = {Borkar - Probability theory
                (1995).pdf:/Users/zcranko/Documents/Library/Book/Borkar -
                Probability theory (1995).pdf:application/pdf}
}

@inproceedings{BorKri05,
  author    = {Karsten M. Borgwardt and Hans-Peter Kriegel},
  title     = {Shortest-Path Kernels on Graphs.},
  booktitle = {Proc.\ Intl.\ Conf.\ Data Mining},
  year      = {2005},
  pages     = {74--81}
}

@inproceedings{BorKriVisSch07,
  author    = {Karsten M. Borgwardt and H.-P. Kriegel and S.~V.~N.
               Vishwanathan and N. Schraudolph},
  title     = {Graph kernels for disease outcome prediction from
               protein-protein interaction networks},
  booktitle = {the Pacific Symposium of Biocomputing 2007},
  year      = {2007},
  editor    = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and
               Tiffany Murray and Teri E Klein},
  address   = {Maui Hawaii},
  month     = {January},
  publisher = {World Scientific}
}

@inproceedings{BorKriWac06,
  author    = {K. M. Borgwardt and H.-P. Kriegel and P. Wackersreuther},
  title     = {Pattern Mining in Frequent Dynamic Subgraphs.},
  booktitle = {ICDM},
  year      = {2006},
  pages     = {818-822}
}

@book{BorLew00,
  title     = {Convex Analysis and Nonlinear Optimization: Theory and
               Examples},
  publisher = {Canadian Mathematical Society},
  year      = {2000},
  author    = {J. M. Borwein and A. S. Lewis},
  series    = {CMS books in Mathematics}
}

@inproceedings{BorOngSchVisetal05,
  author    = {K. M. Borgwardt and C. S. Ong and S. Sch\"{o}nauer and
               S.~V.~N. Vishwanathan and A. J. Smola and H.-P. Kriegel},
  title     = {Protein Function Prediction via Graph Kernels},
  booktitle = {Intelligent Systems in Molecular Biology (ISMB)},
  year      = {2005},
  address   = {Detroit, USA},
  note      = {\url{http://www.stat.purdue.edu/~vishy/papers/BorOngSchVisetal05.pdf}}
}

@article{BorOngSchVisetal05b,
  author  = {K. M. Borgwardt and C. S. Ong and S. Schonauer and
             S.~V.~N. Vishwanathan and A. J. Smola and H. P. Kriegel},
  title   = {Protein function prediction via graph kernels},
  journal = {Bioinformatics},
  year    = {2005},
  volume  = {21},
  pages   = {i47-i56},
  number  = {Suppl 1},
  month   = {Jun}
}

@inproceedings{BorShaUll04,
  author    = {E. Borenstein and E. Sharon and S. Ullman},
  title     = {Combining Top-Down and Bottom-Up Segmentation},
  booktitle = {CVPRW '04: the 2004 Conference on Computer Vision and
               Pattern Recognition Workshop (CVPRW'04) Volume 4},
  year      = {2004},
  isbn      = {0-7695-2158-4},
  pages     = {46},
  publisher = {IEEE Computer Society},
  address   = {Washington, DC, USA}
}

@inproceedings{BorUll04,
  author    = {E. Borenstein and S. Ullman},
  title     = {Learning to Segment},
  booktitle = {ECCV},
  year      = {2004}
}

@inproceedings{BorVisKri06,
  author    = {Karsten M. Borgwardt and S.~V.~N. Vishwanathan and H.-P.
               Kriegel},
  title     = {Class prediction from time series gene expression profiles
               using dynamical systems kernels},
  booktitle = {the Pacific Symposium of Biocomputing 2006},
  year      = {2006},
  editor    = {Russ B. Altman and A. Keith Dunker and Lawrence Hunter and
               Tiffany Murray and Teri E Klein},
  pages     = {547--558},
  address   = {Maui Hawaii},
  month     = {January},
  publisher = {World Scientific}
}

@book{borwein2006convex,
  title     = {Convex analysis and nonlinear optimization: theory and
               examples},
  publisher = {Springer Verlag},
  year      = {2006},
  author    = {Borwein, J.M. and Lewis, A.S.},
  volume    = {3}
}

@book{Borwein_ConvexFunctions_2010,
  address    = {Cambridge, UK},
  series     = {Encyclopedia of mathematics and its applications},
  title      = {Convex functions: constructions, characterizations and
                counterexamples},
  isbn       = {978-0-521-85005-6},
  shorttitle = {Convex functions},
  number     = {109},
  publisher  = {Cambridge University Press},
  author     = {Borwein, Jonathan M. and Vanderwerff, Jon D.},
  year       = {2010},
  note       = {OCLC: ocn434562716},
  file       = {Borwein, Vanderwerff - Convex functions
                (2010).pdf:/Users/zcranko/Documents/Library/Book/Borwein,
                Vanderwerff - Convex functions (2010).pdf:application/pdf}
}

@article{Borwein_ExistencePareto_1983,
  title    = {On the existence of pareto efficient points},
  volume   = {8},
  issn     = {0364765X, 15265471},
  doi      = {10.1287/moor.8.1.64},
  abstract = {We prove some general theorems on the existence of minimal
              points of compact sets in linear spaces with respect to
              closed convex orderings. These results are compared to the
              axiom of choice.},
  number   = {1},
  journal  = {Mathematics of Operations Research},
  author   = {Borwein, Jonathan M.},
  year     = {1983},
  pages    = {64--73},
  file     = {Borwein - On the existence of pareto efficient points
              (1983).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Borwein - On the existence of pareto efficient
              points (1983).pdf:application/pdf}
}

@article{Borwein_VerifiableNecessary_1988,
  title    = {Verifiable necessary and sufficient conditions for
              openness and regularity of set-valued and single-valued
              maps},
  volume   = {134},
  issn     = {0022247X},
  doi      = {10.1016/0022-247x(88)90034-0},
  language = {en},
  number   = {2},
  journal  = {Journal of Mathematical Analysis and Applications},
  author   = {Borwein, J.M and Zhuang, D.M},
  month    = sep,
  year     = {1988},
  pages    = {441--459},
  file     = {Borwein, Zhuang - Verifiable necessary and sufficient
              conditions for openness and regularity of
              (1988).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Borwein, Zhuang - Verifiable necessary and
              sufficient conditions for openness and regularity of
              (1988).pdf:application/pdf}
}

@book{borweinlewis00,
  title     = {Convex Analysis and Nonlinear Optimization},
  publisher = {Springer},
  year      = {2000},
  author    = {Borwein, J. and Lewis, A.}
}

@book{BorweinLewis06,
  title     = {Convex Analysis and Nonlinear Optimization: Theory and
               Examples},
  publisher = {Springer},
  year      = {2005},
  author    = {Jonathan M. Borwein and Adrian S. Lewis},
  edition   = {2nd}
}

@book{BorweinVanderwerff10,
  title     = {Convex Functions: Constructions, Characterizations and
               Counterexamples},
  publisher = {Cambridge University Press},
  year      = {2010},
  author    = {Jonathan M. Borwein and John D. Vanderwerff},
  edition   = {1st}
}

@inproceedings{BorWesUsu14,
  author    = {Bordes, Antoine and Weston, Jason and Usunier, Nicolas},
  title     = {Open Question Answering with Weakly Supervised Embedding
               Models},
  booktitle = ecml,
  year      = {2014}
}

@book{BorZhu05,
  title     = {Techniques of Variational Analysis},
  publisher = {Canadian Mathematical Society},
  year      = {2005},
  author    = {J. M. Borwein and Q. J. Zhu},
  series    = {CMS books in Mathematics}
}

@inproceedings{Bosch99,
  author    = {Antal van den Bosch},
  title     = {Instance-family abstraction in memory-based language
               learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {39--48},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{bose2001business,
  title     = {Business data mining—a machine learning perspective},
  author    = {Bose, Indranil and Mahapatra, Radha K},
  journal   = {Information \& management},
  volume    = {39},
  number    = {3},
  pages     = {211--225},
  year      = {2001},
  publisher = {Elsevier}
}

@inproceedings{BosGuyVap92,
  author    = {B. Boser and I. Guyon and V. Vapnik},
  title     = {A Training Algorithm for Optimal Margin Classifiers},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1992},
  editor    = {D. Haussler},
  pages     = {144--152},
  address   = {Pittsburgh, PA},
  month     = jul,
  publisher = {ACM Press}
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@inproceedings{Bostrom93,
  author    = {Henrik Bostr{\"o}m},
  title     = {Improving Example-Guided Unfolding},
  booktitle = {Machine Learning: ECML-93, European Conference on Machine
               Learning, Proceedings},
  year      = {1993},
  volume    = {667},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {124--135},
  publisher = {Springer-Verlag}
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@inproceedings{Bostrom96,
  author    = {Henrik Bostr{\"o}m},
  title     = {Theory-guided induction of logic programs by inference of
               regular languages},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {46--53},
  publisher = {Morgan Kaufmann}
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@article{BosYeWoo95,
  author  = {R.J. Bosch and Y.Ye and G.G.Woodworth},
  title   = {A convergent algorithm for quantile regression with
             smoothing splines},
  journal = {Computational Statistics and Data Analysis},
  year    = {1995},
  volume  = {19},
  pages   = {613-630}
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@inproceedings{BosZav00,
  author    = {Antal van den Bosch and Jakub Zavrel},
  title     = {Unpacking Multi-valued Symbolic Features and Classes in
               Memory-based Language Learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {1055--1062},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{Bot_WeakerRegularity_2006,
  title   = {A weaker regularity condition for subdifferential calculus
             and {Fenchel} duality in infinite dimensional spaces},
  volume  = {64},
  issn    = {0362-546X},
  doi     = {10.1016/j.na.2005.09.017},
  number  = {12},
  journal = {Nonlinear Analysis. Theory, Methods \& Applications},
  author  = {Boţ, Radu Ioan and Wanka, Gert},
  year    = {2006},
  note    = {tex.fjournal: Nonlinear Analysis. Theory, Methods \&
             Applications. An International Multidisciplinary Journal
             tex.mrclass: 49J52 (49N15 90C25 90C46) tex.mrnumber:
             2218547 tex.mrreviewer: Juan-Enrique Martínez-Legaz},
  pages   = {2787--2804},
  file    = {Boţ, Wanka - A weaker regularity condition for
             subdifferential calculus and Fenchel duality
             (2006).pdf:/Users/zcranko/Documents/Library/Journal
             Article/Boţ, Wanka - A weaker regularity condition for
             subdifferential calculus and Fenchel duality
             (2006).pdf:application/pdf}
}

@inproceedings{BotAtk91,
  author    = {S. Botros and C. Atkeson},
  title     = {Generalization properties of {Radial Basis Function}},
  booktitle = {Advances in Neural Information Processing Systems 3},
  year      = {1991},
  editor    = {R. Lippmann and J.~E. Moody and D.~S. Touretzky},
  pages     = {707--713},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{BotBen95,
  author    = {Leon Bottou and Yoshua Bengio},
  title     = {Convergence Properties of the {$K$}-Means Algorithms},
  booktitle = {Advances in Neural Information Processing Systems 7},
  year      = {1995},
  editor    = {G. Tesauro and D.~S. Touretzky and T.~K. Leen},
  pages     = {585--592},
  publisher = {{MIT} Press}
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@inproceedings{BotBou07,
  title     = {The Tradeoffs of Large Scale Learning},
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  editor    = {J. C. Platt and D. Koller and Y. Singer and S.T. Roweis},
  note      = {{\small\url{http://books.nips.cc/papers/files/nips20/NIPS2007\_0726.pdf}}}
}

@inproceedings{BotCorDenDruetal94,
  author    = {L. Bottou and Corinna Cortes and J.~S. Denker and H.
               Drucker and I. Guyon and L.~D. Jackel and Y. LeCun and
               U.~A. M{\"u}ller and E.~S{\"a}ckinger and P. Simard and V.
               Vapnik},
  title     = {Comparison of classifier methods: a case study in
               handwritten digit recognition},
  booktitle = {the 12th International Conference on Pattern Recognition
               and Neural Networks, Jerusalem},
  year      = {1994},
  pages     = {77--87},
  publisher = {IEEE Computer Society Press}
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@inproceedings{BotGioPio97,
  author    = {Marco Botta and Attilo Giordana and Roberto Piola},
  title     = {{FONN}: Combining first order logic with connectionist
               learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {48--56},
  publisher = {Morgan Kaufmann}
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@inproceedings{BotLeC04,
  author    = {Bottou, L\'eon and {LeCun}, Yann},
  title     = {Large Scale Online Learning},
  pages     = {217--224},
  booktitle = {Advances in Neural Information Processing Systems 16},
  editor    = {S. Thrun and L. Saul and B. Sch\"{o}lkopf},
  publisher = {MIT Press},
  address   = {Cambridge, MA},
  year      = {2004}
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@article{BotLeC05,
  author  = {Bottou, L\'eon and {LeCun}, Yann},
  title   = {On-line Learning for Very Large Datasets},
  pages   = {137--151},
  journal = {Applied Stochastic Models in Business and Industry},
  volume  = {21},
  number  = {2},
  year    = {2005}
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@incollection{BotMur02,
  author    = {Bottou, L\'{e}on and Murata, Noboru},
  title     = {Stochastic Approximations and Efficient Learning},
  booktitle = {The Handbook of Brain Theory and Neural Networks, Second
               edition,},
  editor    = {Arbib, M. A.},
  publisher = {The MIT Press},
  address   = {Cambridge, MA},
  year      = {2002},
  url       = {http://leon.bottou.org/papers/bottou-murata-2002}
}

@article{BotPio00,
  author    = {Marco Botta and Roberto Piola},
  title     = {Refining Numerical Constants in First Order Logic
               Theories},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {38},
  pages     = {109--131},
  number    = {1/2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@unpublished{Bottou08,
  author = {Bottou, L\'eon},
  title  = {Stochastic Gradient {SVMs}},
  note   = {{\small\url{http://leon.bottou.org/projects/sgd}}},
  year   = 2008
}

@phdthesis{Bottou91,
  title   = {Une Approche th\'eorique de l'Apprentissage
             Connexionniste: Applications \`a la Reconnaissance de la
             Parole},
  author  = {Bottou, {L\'eon}},
  year    = {1991},
  school  = {Universit\'{e} de Paris XI},
  address = {Orsay, France},
  url     = {http://leon.bottou.org/papers/bottou-91a}
}

@incollection{Bottou98,
  author    = {Bottou, L\'{e}on},
  title     = {Online Algorithms and Stochastic Approximations},
  booktitle = {Online Learning and Neural Networks},
  editor    = {Saad, David},
  publisher = {Cambridge University Press},
  address   = {Cambridge, UK},
  year      = {1998}
}

@incollection{Bottou99,
  author   = {Bottou, L\'eon},
  title    = {Online Learning and Stochastic Approximations},
  chapter  = {2},
  pages    = {9--42},
  crossref = {Saad99}
}

@article{BotVap92,
  author  = {L. Bottou and V.~N. Vapnik},
  title   = {Local Learning Algorithms},
  journal = {Neural Computation},
  year    = {1992},
  volume  = {4},
  pages   = {888--900},
  number  = {6}
}

@article{BouBouLug05,
  author  = {O. Bousquet and S. Boucheron and G. Lugosi},
  title   = {Theory of classification: a survey of recent advances},
  journal = {{ESAIM}: Probab. Stat.},
  year    = {2005},
  volume  = {9},
  pages   = {323-- 375}
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@book{Boucheron_ConcentrationInequalities_2013,
  address    = {Oxford, UK},
  edition    = {1},
  title      = {Concentration inequalities: a nonasymptotic theory of
                independence},
  isbn       = {978-0-19-953525-5},
  shorttitle = {Concentration inequalities},
  language   = {eng},
  publisher  = {Oxford University Press},
  author     = {Boucheron, Stéphane and Lugosi, Gábor and Massart,
                Pascal},
  year       = {2013},
  note       = {OCLC: 837517674},
  file       = {Boucheron et al - Concentration inequalities
                (2013).pdf:/Users/zcranko/Documents/Library/Book/Boucheron
                et al - Concentration inequalities
                (2013).pdf:application/pdf}
}

@inproceedings{BouDea96,
  author    = {Craig Boutilier and R. Dearden},
  title     = {Approximating value trees in structured dynamic
               programming},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {54--62},
  publisher = {Morgan Kaufmann}
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@techreport{BouEli00,
  author      = {Olivier Bousquet and Andre Elisseeff},
  title       = {Stability and Generalization},
  institution = {Centre de Mathematiques Appliquees},
  year        = {2000}
}

@inproceedings{BouEli01,
  author    = {O. Bousquet and A. Elisseeff},
  title     = {Algorithmic Stability and Generalization Performance},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {196--202},
  publisher = {{MIT} Press}
}

@inproceedings{BouHer02,
  author    = {O. Bousquet and D. Herrmann},
  title     = {On the Complexity of Learning the Kernel Matrix},
  booktitle = {Advances in Neural Information Processing Systems 15},
  year      = {2002},
  editor    = {S. Becker and S. Thrun and K. Obermayer}
}

@article{BouKam88,
  author  = {H. Bourlard and Y. Kamp},
  title   = {Auto-Association by Multilayer Perceptrons and Singular
             Value Decomposition},
  journal = {Biological Cybernetics},
  year    = {1988},
  volume  = {59},
  pages   = {291--294},
  number  = {4/5}
}

@article{BouLugMas00,
  author  = {S. Boucheron and G. Lugosi and P. Massart},
  title   = {A sharp concentration inequality with applications},
  journal = {Random Structures and Algorithms},
  year    = {2000},
  volume  = {16},
  pages   = {277--292},
  number  = {3}
}

@inproceedings{BouMor90,
  author    = {H. Bourlard and N. Morgan},
  title     = {A Continuous Speech Recognition System Embedding {MLP}
               into {HMM}},
  booktitle = {Advances in Neural Information Processing Systems 2},
  year      = {1990},
  editor    = {D.~S. Touretzky},
  pages     = {186--193},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann Publishers}
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@inproceedings{BouPajSzaTom89,
  author    = {J. Bourgain and A. Pajor and S.~J. Szarek and N.
               Tomczak-Jaegermann},
  title     = {On the Duality Problem for Entropy Numbers of Operators},
  booktitle = {Geometric Aspects of Functional Analysis. Israel Seminar
               (GAFA) 1987-88},
  year      = {1989},
  volume    = {1376},
  series    = {Lecture Notes in Mathematics},
  pages     = {50--63},
  address   = {Berlin},
  publisher = {Springer}
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@inproceedings{BouSal88,
  author    = {S. Boucheron and J. Sallantin},
  title     = {Some remarks about space-complexity of learning, and
               circuit complexity of recognizing},
  booktitle = {Proc. 1st Annu. Workshop on Comput. Learning Theory},
  year      = {1988},
  pages     = {125--138},
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@misc{Bousquet00,
  author = {O. Bousquet},
  title  = {Personal Communication},
  year   = {2000}
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@inproceedings{Bousquet03,
  author    = {O. Bousquet and O. Chapelle and M. Hein},
  booktitle = nips,
  title     = {Measure Based Regularization},
  year      = {2003}
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@inproceedings{Boutilier96,
  author    = {Craig Boutilier},
  title     = {Planning, Learning and Coordination in Multiagent Decision
               Processes},
  year      = {1996},
  booktitle = {the 6th Conference on Theoretical Aspects of Rationality
               and Knowledge (TARK '96)},
  location  = {De Zeeuwse Stromen, The Netherlands}
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@inproceedings{Boutilier99,
  author    = {Craig Boutilier},
  title     = {Sequential Optimality and Coordination in Multiagent
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}

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}

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@article{BoyVekZab01b,
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}

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  author    = {David M. Bradley and James Andrew Bagnell},
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}

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  year      = {1998},
  pages     = {91--99},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@techreport{BraFayMan98,
  author      = {P.~S. Bradley and U.~M. Fayyad and O.~L. Mangasarian},
  title       = {Data Mining: Overview and Optimization Opportunities},
  institution = {University of Wisconsin, Computer Sciences Department},
  year        = {1998},
  number      = {98-01},
  address     = {Madison},
  month       = jan,
  note        = {{\em INFORMS Journal on Computing}, To Appear.}
}

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  pages   = {293--300}
}

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  author    = {A. Br{\= a}zma and I. Jonassen and J. Vilo and E. Ukkonen},
  title     = {Pattern discovery in biosequences},
  booktitle = {Proc. 4th International Colloquium on Grammatical
               Inference - ICGI '98},
  year      = {1998},
  volume    = {1433},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {257--270},
  publisher = {Springer-Verlag}
}

@article{BraKolMulSchetal05,
  author  = {Matthieu Bray and Esther Koller-Meier and Pascal M\"uller
             and Nicol N. Schraudolph and Luc {Van Gool}},
  title   = {Stochastic Optimization for High-Dimensional Tracking in
             Dense Range Maps},
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  year    = {2005},
  volume  = {152},
  pages   = {501--512},
  number  = {4}
}

@article{BraKolSchGoo07,
  author  = {Matthieu Bray and Esther Koller-Meier and Nicol N.
             Schraudolph and Luc Van~Gool},
  title   = {Fast Stochastic Optimization for Articulated Structure
             Tracking},
  pages   = {352--364},
  journal = {Image and Vision Computing},
  volume  = {25},
  number  = {3},
  month   = {March},
  year    = {2007}
}

@article{BraKolSchGoo07b,
  crossref = brakolschvan07
}

@inproceedings{BraMan98,
  author    = {P. S. Bradley and O. L. Mangasarian},
  title     = {Feature Selection via Concave Minimization and Support
               Vector Machines},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  editor    = {J. Shavlik},
  pages     = {82--90},
  address   = {San Francisco, California},
  publisher = {Morgan Kaufmann Publishers},
  note      = {ftp://ftp.cs.wisc.edu/math-prog/tech-reports/98-03.ps.Z }
}

@techreport{BraMan98b,
  author      = {P. Bradley and O. Mangasarian},
  title       = {Massive Data Discrimination via Linear Suppport Vector
                 Machines},
  institution = {University of Wisconsin Madison},
  year        = {1998},
  type        = {Mathematical Programming Technical Report},
  number      = {98-05}
}

@techreport{BraMan98c,
  author      = {P. Bradley and O. Mangasarian},
  title       = {$k$ - plane clustering},
  institution = {University of Wisconsin Madison},
  year        = {1998},
  type        = {Mathematical Programming Technical Report},
  number      = {98-08}
}

@techreport{BraManStr95,
  author      = {P.~S. Bradley and O.~L. Mangasarian and W.~N. Street},
  title       = {Feature Selection via Mathematical Programming},
  institution = {Computer Sciences Department, University of Wisconsin},
  year        = {1995},
  number      = {95-21},
  address     = {Madison, Wisconsin},
  note        = {To appear in {\it INFORMS Journal on Computing} 10, 1998.}
}

@inproceedings{BraManStr97,
  author    = {P.~S. Bradley and O.~L. Mangasarian and W.~N. Street},
  title     = {Clustering via Concave Minimization},
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  year      = {1997},
  editor    = {M.~C. Mozer and M.~I. Jordan and T. Petsche},
  pages     = {368--374},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{BraManStr98,
  author  = {P.~S. Bradley and O.~L. Mangasarian and W.~N. Street},
  title   = {Feature Selection via Mathematical Programming},
  journal = {INFORMS Journal on Computing},
  year    = {1998},
  volume  = {10},
  pages   = {209--217},
  number  = {2},
  note    = {ftp://ftp.cs.wisc.edu/math-prog/tech-reports/95-21.ps.Z }
}

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  author    = {Matthew Brand},
  title     = {Finding Variational Structure in Data by Cross-Entropy
               Optimization},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {95--101},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

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               Dean},
  title     = {Large Language Models in Machine Translation},
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               Processing and Computational Natural Language Learning
               {EMNLP}-{CoNLL}},
  pages     = {858-867},
  year      = 2007
}

@inproceedings{BraPopXuOchetal07,
  author    = {Thorsten Brants and Ashok C. Popat and Peng Xu and Franz
               J. Och and Jeffrey Dean},
  title     = {Large Language Models in Machine Translation},
  booktitle = {the 2007 Joint Conference on Empirical Methods in Natural
               Language Processing and Computational Natural Language
               Learning (EMNLP-CoNLL)},
  pages     = {858--867},
  year      = 2007
}

@article{BraRagSla89,
  author  = {M. L. Brady and R. Raghavan and J. Slawny},
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}

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  author = {B. Bradshaw and B.~{Sch\"olkopf} and J. Platt},
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  note   = {Submitted},
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}

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  author    = {Pavel Brazdil and Carlos Soares},
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               Algorithm Selection},
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               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {63--74},
  publisher = {Springer, Berlin}
}

@article{BraStrSch13,
  author  = {Phil\'{e}mon Brakel and Dirk Stroobandt and Benjamin
             Schrauwen},
  title   = {Training Energy-Based Models for Time-Series Imputation},
  journal = jmlr,
  year    = {2013},
  volume  = {14},
  pages   = {2771--2797}
}

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  author    = {A. Br{\=a}zma},
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               representative examples},
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  year      = {1993},
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}

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  author    = {Alvis Br{\=a}zma},
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  year      = {1995},
  pages     = {392--403},
  publisher = {Springer-Verlag}
}

@inbook{Brazma97,
  chapter   = {19},
  pages     = {337--352},
  title     = {Efficient learning of regular expressions from approximate
               examples},
  publisher = {{MIT} Press},
  year      = {1997},
  author    = {Alvis Br{\=a}zma},
  volume    = {IV: Making Learning Systems Practical},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@article{BreBen97,
  author  = {E.~J. Bredensteiner and K.~P. Bennett},
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}

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  author    = {M. R. Brent and Timothy A. Cartwright},
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               incremental {MDL} optimization},
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}

@phdthesis{Bredensteiner97,
  author  = {E.~J. Bredensteiner},
  title   = {Optimization Methods in Data Mining and Machine Learning},
  school  = {Department of Mathematical Sciences, Rensselaer
             Polytechnic Institute},
  year    = {1997},
  address = {Troy, NY}
}

@article{Brediesetal09,
  author  = {Kristian Bredies and Dirk A. Lorenz and Peter Maass},
  title   = {A Generalized Conditional Gradient Method and its
             Connection to an Iterative Shrinkage Method},
  journal = {Computational Optimization and Applications},
  year    = {2009},
  volume  = {42},
  pages   = {173--193}
}

@article{BrediesLorenz08,
  author  = {Kristian Bredies and Dirk A. Lorenz},
  title   = {Iterated Hard Shrinkage for Minimization Problems with
             Sparsity Constraints},
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  year    = {2008},
  volume  = {30},
  pages   = {657--683},
  number  = {2}
}

@inproceedings{brefeld2003support,
  title        = {Support vector machines with example dependent costs},
  author       = {Brefeld, Ulf and Geibel, Peter and Wysotzki, Fritz},
  booktitle    = {European Conference on Machine Learning},
  pages        = {23--34},
  year         = {2003},
  organization = {Springer}
}

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  author    = {Laurent Br{\'{e}}h{\'{e}}lin and Olivier Gascuel and
               Gilles Caraux},
  title     = {Hidden {M}arkov Models with Patterns and Their Application
               to Integrated Circuit Testing},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {75--87},
  publisher = {Springer, Berlin}
}

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  author  = {L.~M. Bregman},
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  pages   = {200--217}
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               collaborative filtering},
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               Intelligence},
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  year      = {1998}
}

@article{Brehmer_ProperizationConstructing_2019,
  title    = {Properization: constructing proper scoring rules via
              {Bayes} acts},
  issn     = {1572-9052},
  doi      = {10.1007/s10463-019-00705-7},
  abstract = {Scoring rules serve to quantify predictive performance. A
              scoring rule is proper if truth telling is an optimal
              strategy in expectation. Subject to customary regularity
              conditions, every scoring rule can be made proper, by
              applying a special case of the Bayes act construction
              studied by Grünwald and Dawid (Ann Stat 32:1367–1433,
              2004) and Dawid (Ann Inst Stat Math 59:77–93, 2007), to
              which we refer as properization. We discuss examples from
              the recent literature and apply the construction to create
              new types, and reinterpret existing forms, of proper
              scoring rules and consistent scoring functions. In an
              abstract setting, we formulate sufficient conditions under
              which Bayes acts exist and scoring rules can be made
              proper.},
  journal  = {Annals of the Institute of Statistical Mathematics},
  author   = {Brehmer, Jonas R. and Gneiting, Tilmann},
  month    = feb,
  year     = {2019},
  keywords = {\#nosource},
  file     = {Brehmer, Gneiting - Properization
              (2019).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Brehmer, Gneiting - Properization
              (2019).pdf:application/pdf}
}

@article{Breiman00,
  author    = {Leo Breiman},
  title     = {Randomizing Outputs to Increase Prediction Accuracy},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {40},
  pages     = {229--242},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Breiman01,
  author    = {Leo Breiman},
  title     = {Random Forests},
  journal   = {Machine Learning},
  year      = {2001},
  volume    = {45},
  pages     = {5--32},
  number    = {1},
  publisher = spring
}

@book{Breiman68,
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@techreport{Breiman93b,
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@techreport{Breiman93c,
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@techreport{Breiman94,
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@article{Breiman96b,
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  abstract  = {Set-valued analysis is an essential tool for the
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               work offers the first comprehensive treatment in book form
               of the fairly new subdiscipline of enlargements of maximal
               monotone operators, including several important new results
               in the field. In the last decades, with the development of
               nonsmooth optimization, effective algorithms have been
               developed to solve these kinds of problems, such as
               nonsmooth variational inequalities. Several of these
               methods, such as bundle methods for variational problems,
               are fully developed and analyzed in this book. The first
               chapters provide a self-contained review of the basic
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               Kakutani. Next, an in-depth introduction to monotone
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               them established during the last decade) on the concept of
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               variational inequalities, bundle-type methods, augmented
               Lagrangian methods, and proximal point algorithms},
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}

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  note        = {in preparation}
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  note   = {http://kodiak.cs.cornell.edu/kddcup/index.html}
}

@unpublished{CarKyrPaj97,
  author = {B. Carl and I. Kyrezi and A. Pajor},
  title  = {Metric entropy of convex hulls in {B}anach spaces},
  note   = {preprint},
  year   = {1997}
}

@article{CarKyrPaj99,
  author  = {B. Carl and I. Kyrezi and A. Pajor},
  title   = {Metric entropy of convex hulls in {B}anach spaces},
  journal = {the {L}ondon Mathematical Society},
  year    = {1999},
  note    = {To Appear}
}

@article{Carl81,
  author  = {B. Carl},
  title   = {Entropy Numbers of Diagonal Operators with an Application
             to Eigenvalue Problems},
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  year    = {1981},
  volume  = {32},
  pages   = {135--150}
}

@article{Carl85,
  author  = {B. Carl},
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  volume  = {35},
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  number  = {3}
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@article{Carl97,
  author  = {B. Carl},
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  volume  = {29},
  pages   = {452--458}
}

@inproceedings{carlini2017towards,
  title        = {Towards evaluating the robustness of neural networks},
  author       = {Carlini, Nicholas and Wagner, David},
  booktitle    = {2017 IEEE Symposium on Security and Privacy (SP)},
  pages        = {39--57},
  year         = {2017},
  organization = {IEEE}
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@inproceedings{Carlini_EvaluatingRobustness_2017,
  address   = {San Jose, CA, USA},
  title     = {Towards {Evaluating} the {Robustness} of {Neural}
               {Networks}},
  isbn      = {978-1-5090-5533-3},
  doi       = {10.1109/sp.2017.49},
  booktitle = {{IEEE} {Symposium} on {Security} and {Privacy}},
  publisher = {IEEE},
  author    = {Carlini, Nicholas and Wagner, David},
  month     = may,
  year      = {2017},
  pages     = {39--57},
  file      = {Full
               Text:/Users/zcranko/Documents/Zotero/storage/YQN9B9RT/Carlini
               and Wagner - 2017 - Towards Evaluating the Robustness of
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@article{Carlstein86,
  author  = {E. Carlstein},
  journal = {The Annals of Statistics},
  pages   = {1171-1179},
  title   = {The Use of Subseries Values for Estimating the Variance of
             a General Statistic From a Stationary Sequence},
  volume  = {14},
  year    = {1986}
}

@inproceedings{CarMarPie99,
  author    = {Claire Cardie and Scott Mardis and David Pierce},
  title     = {Combining error-driven pruning and classification for
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  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
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@book{Carmo76,
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  year      = {1976},
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}

@article{CarMoo99,
  author    = {Claire Cardie and Raymond J. Mooney},
  title     = {Guest Editors' Introduction: Machine Learning and Natural
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  journal   = {Machine Learning},
  year      = {1999},
  volume    = {34},
  pages     = {5--9},
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@unpublished{carnegie,
  title = {The {C}arnegie classification of institutions of higher
           education},
  year  = {2015},
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}

@inproceedings{CarOnc94,
  author    = {R. Carrasco and J. Oncina},
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  booktitle = {Proc. 2nd International Colloquium on Grammatical
               Inference - ICGI '94},
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  volume    = {862},
  series    = {Lecture Notes in Artificial Intelligence},
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@article{CarOnc99,
  author  = {R. C. Carrasco and J. Oncina},
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             samples in polynomial time},
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  year    = {1999},
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@article{CarOncCal01,
  author    = {Rafael C. Carrasco and Jose Oncina and Jorge
               Calera-Rubio},
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  volume    = {44},
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@inproceedings{CarOncCal98,
  author    = {R. C. Carrasco and J. Oncina and J. Calera},
  title     = {Stochastic inference of regular tree languages},
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               Inference - ICGI '98},
  year      = {1998},
  volume    = {1433},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {187--198},
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@article{CarPaj88,
  author  = {B. Carl and A. Pajor},
  title   = {{G}elfand Numbers of Operators with Values in a {H}ilbert
             Space},
  journal = {Inventiones Mathematicae},
  year    = {1988},
  volume  = {94},
  pages   = {479--504}
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@inproceedings{CarSle96,
  author    = {Leonardo Carbonara and Derek Sleeman},
  title     = {Improving the efficiency of knowledge base refinement},
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  year      = {1996},
  pages     = {78--86},
  publisher = {Morgan Kaufmann}
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@article{CarSle99,
  author    = {Leonardo Carbonara and Derek Sleeman},
  title     = {Effective and Efficient Knowledge Base Refinement},
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  year      = {1999},
  volume    = {37},
  pages     = {143--181},
  number    = {2},
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@book{CarSte90,
  title     = {Entropy, compactness, and the approximation of operators},
  publisher = {Cambridge University Press},
  year      = {1990},
  author    = {B. Carl and I. Stephani},
  address   = {Cambridge, UK}
}

@article{carter1987assessing,
  title     = {Assessing credit card applications using machine
               learning},
  author    = {Carter, Chris and Catlett, Jason},
  journal   = {IEEE expert},
  volume    = {2},
  number    = {3},
  pages     = {71--79},
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  publisher = {IEEE}
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@inproceedings{Caruana95,
  title     = {Learning many related tasks at the same time with
               backpropagation},
  author    = {Caruana, Rich},
  booktitle = nips,
  pages     = {657--664},
  year      = {1995}
}

@inproceedings{Caruana96,
  author    = {R. Caruana},
  title     = {Algorithms and applications for multitask learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {87--95},
  publisher = {Morgan Kaufmann}
}

@article{Caruana97,
  author    = {R. Caruana},
  title     = {Multitask learning},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {28},
  pages     = {41--75},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{CarWan14,
  title     = {Distributed Optimization of Deeply Nested Systems},
  booktitle = aistats,
  author    = {M.A. Carreira-Perpinnan and Weiran Wang},
  year      = {2014}
}

@article{CarYanCoh00,
  author    = {Jaime Carbonell and Yiming Yang and William Cohen},
  title     = {Special Issue of Machine Learning on Information Retrieval
               Introduction},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {39},
  pages     = {99--101},
  number    = {2/3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Casacuberta00,
  author    = {F. Casacuberta},
  title     = {Inference of Finite-State Transducers by Using Regular
               Grammars and Morphisms},
  booktitle = {Grammatical Inference: {A}lgorithms and {A}pplications,
               5th International Colloquium, ICGI 2000, Lisbon, Portugal,
               September 11--13, 2000 ; Proceedings},
  year      = {2000},
  volume    = {1891},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {1--14},
  publisher = {Springer, Berlin}
}

@inproceedings{Casacuberta96,
  author    = {F. Casacuberta},
  title     = {Maximum mutual information and conditional maximum
               likelihood estimation of stochastic regular syntax-directed
               translation schemes},
  booktitle = {Grammatical Inference: {L}earning Syntax from Sentences,
               3rd International Colloquium, ICGI-96, Montpellier, France,
               September 25--27, 1996, Proceedings},
  year      = {1996},
  volume    = {1147},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {282--291},
  publisher = {Springer, Berlin}
}

@inproceedings{CasAnt99,
  author    = {S. Castano and V.~D. Antonellis},
  title     = {A schema analysis and reconciliation tool environment},
  booktitle = {In the Int. Database Engineering and Applications
               Symposium (IDEAS)},
  year      = {1999}
}

@inproceedings{CasBal95,
  author    = {Jorge Castro and Jos{\'e} L. Balc{\'a}zar},
  title     = {Simple {PAC} learning of simple decision lists},
  booktitle = alt-ws,
  year      = {1995},
  pages     = {239--248},
  publisher = {Springer-Verlag}
}

@book{CasBer02,
  title     = {Statistical Inference},
  publisher = {Duxbury},
  year      = {2002},
  author    = {G. Casella and R. Berger},
  address   = {Pacific Grove, CA},
  edition   = {2nd}
}

@article{CasCheJai01,
  author  = {John Case and Keh-Jiann Chen and Sanjay Jain},
  title   = {Costs of general purpose learning},
  journal = {Theoret. Comput. Sci.},
  year    = {2001},
  volume  = {259},
  pages   = {455--473},
  number  = {1--2}
}

@article{CasCheJai92,
  author  = {J. Case and K. J. Chen and S. Jain},
  title   = {Strong Separation of Learning Classes},
  journal = {J. of Experimental and Theoret. Artif. Intell.},
  year    = {1992},
  volume  = {4},
  pages   = {281--293},
  annote  = {Was in {\sl Analogical and Inductive Inference,
             International Workshop AII'92}, Dagstuhl Castle, Germany.
             Lecture Note in AI 642, October 1992}
}

@inproceedings{CasCheJai99,
  author    = {John Case and Keh-Jiann Chen and Sanjay Jain},
  title     = {Costs of General Purpose Learning},
  booktitle = {Proc.~16th Annual Symposium on Theoretical Aspects of
               Computer Science - STACS '99},
  year      = {1999},
  volume    = {1563},
  series    = {Lecture Notes in Computer Science},
  pages     = {424--433},
  publisher = {Springer-Verlag}
}

@unpublished{CasChi86,
  author = {J. Case and H. Chi},
  title  = {Machine Learning of Nearly Minimal Size Grammars},
  note   = {Unpublished Manuscript},
  year   = {1986}
}

@article{CasCov95,
  author  = {Vittorio Castelli and T. M. Cover},
  title   = {On the exponential value of labeled examples},
  journal = {Pattern Recognition Letters},
  year    = {1995},
  volume  = {16},
  pages   = {105--111}
}

@article{Casdagli89,
  author  = {M. Casdagli},
  title   = {Nonlinear prediction of chaotic time-series},
  journal = {Physica {D}},
  year    = {1989},
  volume  = {35},
  pages   = {335--356}
}

@article{CasDav11,
  author     = {Castillo, Carlos and Davison, Brian D.},
  title      = {Adversarial Web Search},
  year       = {2011},
  issue_date = {May 2011},
  publisher  = {Now Publishers Inc.},
  address    = {Hanover, MA, USA},
  volume     = {4},
  number     = {5},
  issn       = {1554-0669},
  url        = {https://doi.org/10.1561/1500000021},
  doi        = {10.1561/1500000021},
  journal    = {Found. Trends Inf. Retr.},
  month      = may,
  pages      = {377–486},
  numpages   = {110}
}

@article{Case74,
  author  = {J. Case},
  title   = {Periodicity in generations of automata},
  journal = {Math. Syst. Theory},
  year    = {1974},
  volume  = {8},
  pages   = {15--32}
}

@article{Case82,
  author  = {J. Case},
  title   = {Problems in Inductive Inference},
  journal = {Bulletin of the European Association for Theoretical
             Computer Science},
  year    = {1982},
  volume  = {18},
  pages   = {117--118}
}

@incollection{Case86,
  author    = {J. Case},
  title     = {Learning Machines},
  booktitle = {Language Learning and Concept Acquisition},
  publisher = {Ablex Publishing Company},
  year      = {1986}
}

@inproceedings{Case88,
  author    = {J. Case},
  title     = {The power of vacillation},
  booktitle = {Proc. 1st Annu. Workshop on Comput. Learning Theory},
  year      = {1988},
  pages     = {196--205},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{Case94,
  author  = {J. Case},
  title   = {Infinitary Self-Reference in Learning Theory},
  journal = {J. of Experimental and Theoret. Artif. Intell.},
  year    = {1994},
  volume  = {6},
  pages   = {3--36},
  number  = {1},
  note    = {Special issue for AII '92},
  comment = {Supersedes University of Delaware TR-93-15}
}

@article{Case99,
  author  = {John Case},
  title   = {The Power of Vacillation in Language Learning},
  journal = {{SIAM} J. Comput.},
  year    = {1999},
  volume  = {28},
  pages   = {1941--1969},
  number  = {6}
}

@inproceedings{Casella88,
  author    = {George Casella},
  title     = {Conditionally Acceptable Frequentist Solutions},
  booktitle = {Statistical Decision Theory},
  year      = {1988},
  volume    = {1},
  pages     = {73--84}
}

@article{CasFul83,
  author  = {J. Case and M. Fulk},
  title   = {Solution to inductive inference problem {P3}},
  journal = {Bulletin of the European Association for Theoretical
             Computer Science},
  year    = {1983},
  volume  = {19},
  pages   = {8}
}

@article{CasFul99,
  author  = {J. Case and M. A. Fulk},
  title   = {Maximal machine learnable classes},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1999},
  volume  = {58},
  pages   = {211--214},
  number  = {1}
}

@article{CasGriWes07,
  author  = {A. I. Vila Casado and M. Griot and R.D. Wesel},
  title   = {Informed Dynamic Scheduling for Belief-Propagation
             Decoding of {LDPC} Codes},
  journal = {CoRR},
  volume  = {abs/cs/0702111},
  year    = {2007}
}

@article{CasGui00,
  author  = {Jorge Castro and David Guijarro},
  title   = {{PACS}, simple-{PAC} and query learning},
  journal = {Information Processing Letters},
  year    = {2000},
  volume  = {73},
  pages   = {11--16},
  number  = {1--2}
}

@inproceedings{CasGuiLav97,
  author    = {Jorge Castro and David Guijarro and Victor Lavin},
  title     = {Learning nearly monotone $k$-term {DNF}},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  pages     = {162--170},
  publisher = {Springer-Verlag}
}

@article{CasHadSol97,
  author    = {Enrque Castillo and Ali S. Hadi and Cristina Solares},
  title     = {Learning and Updating of Uncertainty in Dirichlet Models},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {26},
  pages     = {43--63},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{CasHig00,
  author    = {F. Casacuberta and Colin De La Higuera},
  title     = {Computational Complexity of Problems on Probabilistic
               Grammars and Transducers},
  booktitle = {Grammatical Inference: {A}lgorithms and {A}pplications,
               5th International Colloquium, ICGI 2000, Lisbon, Portugal,
               September 11--13, 2000 ; Proceedings},
  year      = {2000},
  volume    = {1891},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {15--24},
  publisher = {Springer, Berlin}
}

@inproceedings{CasJai98,
  author    = {J. Case and S. Jain},
  title     = {Synthesizing Learners Tolerating Computable Noisy Data},
  booktitle = alt,
  year      = {1998},
  volume    = {1501},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {205--219},
  publisher = {Springer-Verlag}
}

@inproceedings{CasJaiKauShaetal98,
  author    = {J. Case and S. Jain and S. Kaufmann and A. Sharma and F.
               Stephan},
  title     = {Predictive Learning Models for Concept Drift},
  booktitle = alt,
  year      = {1998},
  volume    = {1501},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {276--290},
  publisher = {Springer-Verlag}
}

@article{CasJaiLanZeu99,
  author  = {J. Case and S. Jain and S. Lange and T. Zeugmann},
  title   = {Incremental concept learning for bounded data mining},
  journal = {Inform. Comput.},
  year    = {1999},
  volume  = {152},
  pages   = {74--110},
  number  = {1}
}

@article{CasJaiMan94,
  author  = {J. Case and S. Jain and S. Ngo Manguelle},
  title   = {Refinements of Inductive Inference by Popperian and
             Reliable Machines},
  journal = {Kybernetika},
  year    = {1994},
  volume  = {30-1},
  pages   = {23--52}
}

@article{CasJaiNgo94,
  author  = {John Case and Sanjay Jain and Suzanne Ngo-Manguelle},
  title   = {Refinements of Inductive Inference by Popperian and
             Reliable Machines},
  journal = {Kybernetika},
  year    = {1994},
  volume  = {30},
  pages   = {23--52},
  number  = {1}
}

@article{CasJaiOttShaetal00,
  author  = {John Case and Sanjay Jain and Matthias Ott and Arun Sharma
             and Frank Stephan},
  title   = {Robust Learning Aided by Context},
  journal = {J. of Comput. Syst. Sci.},
  year    = {2000},
  volume  = {60},
  pages   = {234--257},
  number  = {2},
  note    = {Special Issue for COLT '98}
}

@article{CasJaiSha01,
  author  = {John Case and Sanjay Jain and Arun Sharma},
  title   = {Synthesizing noise-tolerant language learners},
  journal = {Theoret. Comput. Sci.},
  year    = {2001},
  volume  = {261},
  pages   = {31--56},
  number  = {1},
  note    = {Special issue for ALT '97}
}

@inproceedings{CasJaiSha90,
  author    = {J. Case and S. Jain and A. Sharma},
  title     = {Anomalous Learning Helps Succinctness},
  booktitle = alt-ws,
  year      = {1990},
  pages     = {282--288},
  month     = oct,
  publisher = {Japanese Society for Artificial Intelligence},
  annote    = {Was Tech Report 93-01 from Univ. of Deleware in 1993}
}

@techreport{CasJaiSha90b,
  author      = {J. Case and S. Jain and A. Sharma},
  title       = {Convergence to Nearly Minimal Size Grammars by Vacillating
                 Learning Machines},
  institution = {University of Delaware, Newark, Delaware},
  year        = {1990},
  number      = {90-10}
}

@article{CasJaiSha92,
  author  = {J. Case and S. Jain and A. Sharma},
  title   = {On Learning Limiting Programs},
  journal = {Internat. J. of Found. of Comput. Sci.},
  year    = {1992},
  volume  = {3},
  pages   = {93--115},
  number  = {1},
  annote  = {Was in COLT92 and was University of Deleware TR 92-05}
}

@article{CasJaiSha94,
  author  = {John Case and Sanjay Jain and Arun Sharma},
  title   = {Vacillatory learning of nearly-minimal size grammars},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1994},
  volume  = {49},
  pages   = {189--207},
  number  = {2}
}

@article{CasJaiSha95,
  author  = {J. Case and S. Jain and A. Sharma},
  title   = {Complexity Issues for Vacillatory Function
             Identification},
  journal = {Inform. Comput.},
  year    = {1995},
  volume  = {116},
  annote  = {Earlier version in {\it Proceedings, Foundations of
             Software Technology and Theoretical Computer Science,
             Eleventh Conference, New Delhi, India. Lecture Notes in
             Computer Science 560 } in 1991},
  comment = {Supersedes {U}niversity of {D} TR 92-10}
}

@inproceedings{CasJaiSha95b,
  author    = {John Case and Sanjay Jain and Arun Sharma},
  title     = {Machine induction without revolutionary paradigm shifts},
  booktitle = alt-ws,
  year      = {1995},
  pages     = {153--168},
  publisher = {Springer-Verlag}
}

@inproceedings{CasJaiSha97,
  author    = {John Case and Sanjay Jain and Arun Sharma},
  title     = {Synthesizing noise-tolerant language learners},
  booktitle = alt-ws,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {228--243},
  publisher = {Springer-Verlag}
}

@article{CasJaiSte00,
  author  = {John Case and Sanjay Jain and Frank Stephan},
  title   = {Vacillatory and {BC} learning on noisy data},
  journal = {Theoret. Comput. Sci.},
  year    = {2000},
  volume  = {241},
  pages   = {115--141},
  number  = {1--2},
  note    = {Special issue for ALT '96}
}

@inproceedings{CasJaiSur97,
  author    = {John Case and Sanjay Jain and Mandayam Suraj},
  title     = {Control structures in hypothesis spaces: the influence on
               learning},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  pages     = {286--300},
  publisher = {Springer-Verlag}
}

@article{CasKauKinKum97,
  author  = {John Case and Susanne Kaufmann and Efim B. Kinber and
             Martin Kummer},
  title   = {Learning Recursive Functions from Approximations},
  journal = {J. Comput. Syst. Sci.},
  year    = {1997},
  volume  = {55},
  pages   = {183--196},
  number  = {1},
  note    = {First appeared in EuroCOLT '95}
}

@inproceedings{CasKinShaSte97,
  author    = {John Case and Efim B. Kinber and Arun Sharma and Frank
               Stephan},
  title     = {On the Classification of Computable Languages},
  booktitle = {Proc.~14th Annual Symposium on Theoretical Aspects of
               Computer Science: STACS'97},
  year      = {1997},
  volume    = {1200},
  series    = {Lecture Notes in Computer Science},
  pages     = {225--236},
  publisher = {Springer-Verlag}
}

@inproceedings{CasLyn82,
  author    = {J. Case and C. Lynes},
  title     = {Machine Inductive Inference and Language Identification},
  booktitle = {Automata, Languages and Programming, 9th Colloquium,
               Proceedings},
  year      = {1982},
  volume    = {140},
  pages     = {107--115},
  publisher = {Springer-Verlag}
}

@inproceedings{CasManJai93,
  author    = {J. Case and S. Mandayam and S. Jain},
  title     = {Not-So-Nearly-Minimal-Size Program Inference},
  booktitle = alt-ws,
  address   = {Tokyo, Japan},
  year      = {1993},
  month     = oct,
  publisher = {Japanese Society for Artificial Intelligence},
  note      = {To Appear}
}

@proceedings{CasMye05,
  editor    = {Rita Casadio and Gene Myers},
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@inproceedings{CesGen05,
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  year      = {2005},
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  publisher = {{MIT} Press}
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@inproceedings{CesGen06,
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@article{CesGenZan06,
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}

@proceedings{CesGol00,
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  year      = {2000},
  editor    = {N. Cesa-Bianchi and S. Goldman},
  address   = {San Francisco, CA},
  publisher = {Morgan Kaufmann, Inc.},
  isbn      = {1-55860-703-X}
}

@article{CesHelPan98,
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  year    = {1998},
  volume  = {22},
  pages   = {112--137},
  number  = {1/2},
  comment = {first in 9th COLT '96}
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@article{CesKroWar94,
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  month   = jul
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  year      = {1993},
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@article{CesLug01,
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  year      = {2001},
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@inproceedings{CesLug98,
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  year      = {1998},
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  author    = {N. Cesa-Bianchi and G{\'a}bor Lugosi},
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               experts},
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  year      = {1999},
  pages     = {12--18},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{ChaBoyGerWanetal09,
  author    = {Jonathan Chang and Jordan Boyd-Graber and Sean Gerrish and
               Chong Wang and David M. Blei},
  title     = {Reading Tea Leaves: How Humans Intepret Topic Models},
  booktitle = {{NIPS}},
  year      = {2009}
}

@article{ChaCeoPalNaretal07,
  author  = {A. Chatr-aryamontri and A. Ceol and L. M. Palazzi and G.
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  month   = {Jan},
  year    = {2007}
}

@inproceedings{ChaCohMcC00,
  author    = {Huan Chang and David Cohn and Andrew K. McCallum},
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  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {127--134},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{ChaDeaGheHsietal06,
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  booktitle = {OSDI '06: the 7th symposium on Operating systems design
               and implementation},
  location  = {Seattle, Washington},
  pages     = {205--218},
  publisher = {USENIX Association},
  title     = {Bigtable: a distributed storage system for structured
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  year      = 2006
}

@inproceedings{ChaGas93,
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  booktitle = {Proc. of the 34rd Annual Symposium on Foundations of
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  year      = {1993},
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  year      = {1999},
  pages     = {1--10},
  publisher = {ACM Press, New York, NY}
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@article{ChaHafVap99,
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  year    = {1999},
  volume  = {10},
  number  = {5}
}

@inproceedings{ChaHanTanQi15,
  title        = {Heterogeneous network embedding via deep architectures},
  author       = {Chang, Shiyu and Han, Wei and Tang, Jiliang and Qi,
                  Guo-Jun and Aggarwal, Charu C and Huang, Thomas S},
  booktitle    = kdd,
  pages        = {119--128},
  year         = {2015},
  organization = {ACM}
}

@inproceedings{ChaHar05,
  author    = {O. Chapelle and Z. Harchaoui},
  title     = {A Machine Learning Approach to Conjoint Analysis},
  booktitle = {Advances in Neural Information Processing Systems 17},
  year      = {2005},
  pages     = {257--264},
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  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@book{ChaHas92,
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@article{ChaHsiLin08,
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  journal   = {Journal of Machine Learning Research},
  year      = {2008},
  volume    = {9},
  pages     = {1369--1398},
  publisher = {{MIT} Press}
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@inproceedings{ChaHsuLin99,
  author    = {C.-C. Chang and C.-W. Hsu and C.-J. Lin},
  title     = {The analysis of decomposition methods for support vector
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  booktitle = {Proceeding of {IJCAI99}, {SVM} workshop},
  year      = {1999}
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  volume  = {13},
  pages   = {547--569},
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  month   = oct
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@article{Chakrabarty_ContinuityProperties_2007,
  title    = {Continuity properties for the subdifferential and
              ε-subdifferential of a convex function and its conjugate},
  volume   = {14},
  issn     = {0944-6532},
  number   = {3},
  author   = {Chakrabarty, A. K. and Shunmugaraj, P. and Zălinescu,
              Constantin},
  year     = {2007},
  note     = {tex.fjournal: Journal of Convex Analysis tex.mrclass:
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  keywords = {⛔ No DOI found},
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  file     = {Chakrabarty et al - Continuity properties for the
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@article{ChaKraKumRabetal06,
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@inproceedings{ChaKri08,
  author    = {M. Chandraker and D. Kriegman},
  title     = {Globally Optimal Bilinear Programming for Computer Vision
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  booktitle = {{IEEE} Conference on Computer Vision and Pattern
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  year      = 2008
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@article{ChaLaw94,
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  volume  = {12},
  pages   = {327--344},
  number  = {4/5}
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@article{ChaLed09,
  title     = {An observation about submatrices},
  author    = {S. Chatterjee and M. Ledoux},
  journal   = {Electronic Communications in Probability},
  publisher = {Institute of Mathematical Statistics},
  year      = {2009},
  volume    = {14},
  url       = {http://www.math.washington.edu/~ejpecp/ECP/viewarticle.php?id=2145}
}

@article{ChaLin01,
  author  = {C.C. Chang and C.J. Lin},
  title   = {Training $\nu$-support vector classifiers: Theory and
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  year    = {2001},
  volume  = {13},
  pages   = {2119--2147},
  number  = {9}
}

@manual{ChaLin01b,
  title  = {{LIBSVM}: a library for support vector machines},
  author = {C.C. Chang and C.J. Lin},
  year   = {2001},
  note   = {Software available at
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}

@techreport{ChaLin99,
  author      = {C.-C. Chang and C.-J. Lin},
  title       = {Some analysis on $\nu$-support vector classification},
  institution = {National Taiwan University},
  year        = {1999},
  month       = {December},
  note        = {Submitted to \emph{Neural Computation}}
}

@article{ChaLinShiWan15,
  author  = {Kuan Y. Chang and Tung-Pei Lin and Ling-Yi Shih and
             Chien-Kuo Wang},
  title   = {Analysis and Prediction of the Critical Regions of
             Antimicrobial Peptides Based on Conditional Random Fields},
  journal = {{PLoS} ONE},
  year    = {2015}
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@inproceedings{ChaLip93,
  author    = {E.~I. Chang and R.~L. Lippmann},
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  year      = {1993},
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@inproceedings{ChaMetZhaGri09,
  author    = {O. Chapelle and D. Metlzer and Y. Zhang and P. Grinspan},
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  booktitle = {{CIKM} '09: the 18th ACM Conference on Information and
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@article{Champion2008Wasserstein,
  title        = {The {$\infty$}-{{Wasserstein}} Distance: Local Solutions
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  shorttitle   = {The \$\textbackslash{}infty\$-{{Wasserstein Distance}}},
  author       = {Champion, Thierry and De Pascale, Luigi and Juutinen,
                  Petri},
  date         = {2008-01},
  year         = {2008},
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  keywords     = {optimal transport},
  langid       = {english},
  number       = {1}
}

@article{Champion_WassersteinDistance_2008,
  title      = {The ∞-{Wasserstein} distance: local solutions and
                existence of optimal transport maps},
  volume     = {40},
  issn       = {0036-1410, 1095-7154},
  shorttitle = {The \${\textbackslash}infty\$-{Wasserstein} {Distance}},
  doi        = {10.1137/07069938x},
  language   = {en},
  number     = {1},
  journal    = {SIAM Journal on Mathematical Analysis},
  author     = {Champion, Thierry and De Pascale, Luigi and Juutinen,
                Petri},
  month      = jan,
  year       = {2008},
  keywords   = {optimal transport},
  pages      = {1--20},
  file       = {Champion et al - The ∞-Wasserstein distance
                (2008).pdf:/Users/zcranko/Documents/Library/Journal
                Article/Champion et al - The ∞-Wasserstein distance
                (2008).pdf:application/pdf}
}

@mastersthesis{Chan95,
  author  = {N.~T. Chan},
  title   = {A Priori Knowledge and the Complexity of Learning From
             Examples},
  school  = {Department of Electrical Engineering and Computer Science,
             {MIT}},
  year    = {1995},
  address = {Cambridge, MA},
  month   = may
}

@article{Chandrasekaran89,
  author    = {B. Chandrasekaran},
  title     = {Task-Structures, Knowledge Acquisition and Learning},
  journal   = {Machine Learning},
  year      = {1989},
  volume    = {4},
  pages     = {339--345},
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}

@article{Chandrasekaranetal12,
  author  = {Venkat Chandrasekaran and Benjamin Recht and Pablo A.
             Parrilo and Alan S.Willsky},
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  volume  = {12},
  pages   = {805--849},
  number  = {6}
}

@article{chang2006applying,
  title     = {Applying data mining to predict college admissions yield:
               A case study},
  author    = {Chang, Lin},
  journal   = {New Directions for Institutional Research},
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  number    = {131},
  pages     = {53--68},
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@inproceedings{Changetal10,
  author    = {Ming-Wei Chang and Dan Goldwasser and Dan Roth and Vivek
               Srikumar},
  title     = {Discriminative Learning over Constrained Latent
               Representations},
  booktitle = naacl,
  year      = 2010
}

@article{Chapelle06,
  title     = {Training a Support Vector Machine in the Primal},
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  journal   = {Neural Computation},
  volume    = {19},
  number    = {5},
  pages     = {1155--1178},
  year      = {2007},
  publisher = {MIT Press}
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@book{charalambos2006infinite,
  title     = {Infinite dimensional analysis: a hitchhiker's guide},
  author    = {Charalambos, D Aliprantis and Border, Kim C},
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  publisher = {Springer}
}

@inproceedings{Charikar02,
  title        = {Similarity estimation techniques from rounding
                  algorithms},
  author       = {Charikar, M.S.},
  booktitle    = {the thiry-fourth annual ACM symposium on Theory of
                  computing},
  pages        = {380--388},
  year         = {2002},
  organization = {ACM}
}

@inproceedings{Charniak00,
  author    = {E. Charniak},
  title     = {A maximum-entropy-inspired parser},
  booktitle = {the first conference on North American chapter of the
               Association for Computational Linguistics},
  year      = {2000},
  pages     = {132--139},
  location  = {Seattle, Washington},
  publisher = {Morgan Kaufmann Publishers Inc.},
  address   = {San Francisco, CA, USA}
}

@inproceedings{Charniak97,
  author    = {E. Charniak},
  title     = {Statistical parsing with a context-free grammar and word
               statistics},
  booktitle = {the 14th National Conference on Artificial Intelligence},
  year      = {1997},
  pages     = {598-603}
}

@article{Charniak97b,
  author  = {E. Charniak},
  title   = {Statistical techniques for natural language parsing},
  journal = {AI Magazine},
  year    = {1997}
}

@unpublished{ChaSch00,
  author = {O. Chapelle and B.~{Sch\"olkopf}},
  title  = {Incorporating invariances in nonlinear {SVMs}},
  note   = {Presented at the NIPS2000 workshop on Learning with
            Kernels},
  year   = {2000}
}

@inproceedings{ChaSchSmo00,
  author    = {A. Chalimourda and B. Sch\"olkopf and A. J. Smola},
  title     = {Choosing $\nu$ in support vector regression with different
               noise models\,---\,theory and experiments},
  booktitle = {Proceedings {IEEE}-{INNS}-{ENNS} International Joint
               Conference on Neural Networks ({IJCNN} 2000)},
  year      = {2000},
  address   = {Como, Italy}
}

@article{ChaSchSmo03,
  author  = {A. Chalimourda and B. Sch\"olkopf and A. J. Smola},
  title   = {Choosing $\nu$ in support vector regression with different
             noise models\,---\,theory and experiments},
  journal = {Neural Networks},
  year    = {2003},
  note    = {To Appear}
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@book{ChaSchZie06,
  editor    = {O. Chapelle and B. Sch{\"o}lkopf and A. Zien},
  title     = {Semi-Supervised Learning},
  publisher = {MIT Press},
  year      = {2006},
  url       = {http://www.kyb.tuebingen.mpg.de/ssl-book},
  address   = {Cambridge, MA}
}

@inproceedings{ChaSriGolRot10,
  author    = {Ming-Wei Chang and Vivek Srikumar and Dan Goldwasser and
               Dan Roth},
  title     = {Structured Output Learning with Indirect Supervision},
  booktitle = icml,
  year      = {2010}
}

@inproceedings{ChaSto95,
  author    = {Philip K. Chan and Salvatore J. Stolfo},
  title     = {A comparative evaluation of voting and meta-learning on
               partitioned data},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {90--98},
  publisher = {Morgan Kaufmann}
}

@article{ChaStoWol99,
  author    = {Philip K. Chan and Salvatore J. Stolfo and David Wolpert},
  title     = {Guest Editors' Introduction},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {36},
  pages     = {5--7},
  number    = {1/2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@phdthesis{Chatterjee05,
  author = {S. Chatterjee},
  title  = {Concentration Inequalities with Exchangeable Pairs},
  school = {Stanford University},
  year   = 2005
}

@inproceedings{chaudhurietal09,
  author    = {K. Chaudhuri and S. Kakade and K. Livescu and K.
               Sridharan},
  title     = {Multi-view clustering via canonical correlation analysis},
  booktitle = icml,
  year      = {2009}
}

@article{ChaVapBouMuk02,
  author  = {O. Chapelle and V. Vapnik and O. Bousquet and S.
             Mukherjee},
  title   = {Choosing Multiple Parameters for Support Vector Machines},
  journal = {Machine Learning},
  year    = {2002},
  volume  = {46},
  pages   = {131--159},
  number  = {1}
}

@inproceedings{ChaVapWes00,
  author    = {O. Chapelle and V.~N. Vapnik and J. Weston},
  title     = {Transductive Inference for Estimating Values of
               Functions},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A. Solla and T.~K. Leen and K.-R. MÂÃ¼ller},
  pages     = {421--427},
  publisher = {{MIT} Press}
}

@book{Chavel_RiemannianGeometry_2006,
  address    = {New York, NY, USA},
  edition    = {2},
  series     = {Cambridge studies in advanced mathematics},
  title      = {Riemannian geometry: a modern introduction},
  isbn       = {978-0-521-85368-2 978-0-521-61954-7},
  shorttitle = {Riemannian geometry},
  number     = {98},
  publisher  = {Cambridge University Press},
  author     = {Chavel, Isaac},
  year       = {2006},
  note       = {OCLC: ocm62089870},
  file       = {Chavel - Riemannian geometry
                (2006).pdf:/Users/zcranko/Documents/Library/Book/Chavel -
                Riemannian geometry (2006).pdf:application/pdf}
}

@article{Chazelle00,
  author   = {B. Chazelle},
  title    = {A Minimum Spanning Tree Algorithm with Inverse-Ackermann
              Type Complexity},
  journal  = {Journal of the ACM},
  year     = {2000},
  volume   = {47},
  optpages = {1028--1047}
}

@inproceedings{ChaZha09,
  title     = {A dynamic bayesian network click model for web search
               ranking},
  author    = {O. Chapelle and Y. Zhang},
  booktitle = {the 18th International Conference on World Wide Web,
               {WWW}},
  publisher = {ACM},
  year      = {2009},
  editor    = {J. Quemada and G. Le{\'o}n and Y.S. Maarek and W. Nejdl},
  pages     = {1--10},
  url       = {http://doi.acm.org/10.1145/1526709.1526711}
}

@incollection{ChaZhuWanBaietal08,
  title     = {Parallelizing Support Vector Machines on Distributed
               Computers},
  author    = {E. Chang and K. Zhu and H. Wang and H. Bai and J. Li and
               Z. Qiu and H. Cui},
  booktitle = {NIPS},
  pages     = {257--264},
  year      = {2008}
}

@inproceedings{ChaZie05,
  author    = {O. Chapelle and A. Zien},
  title     = {Semi-Supervised Classification by Low Density Separation},
  booktitle = {Tenth International Workshop on Artificial Intelligence
               and Statistics},
  year      = {2005}
}

@article{CheAme99,
  author  = {Z. Chen and F. Ameur},
  title   = {The learnability of unions of two rectangles in the
             two-dimensional discretized space},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1999},
  volume  = {59},
  pages   = {70--83},
  number  = {1}
}

@article{CheBic05,
  author  = {A. Chen and P. Bickel},
  title   = {Consistent independent component analysis and
             prewhitening},
  journal = {IEEE Trans. Signal Process.},
  year    = {2005},
  volume  = {53},
  number  = {10},
  pages   = {3625--3632}
}

@article{CheCha95,
  author  = {Liang-Hwe Chen and Shyang Chang},
  title   = {An adaptive Learning Algorithm for Principal Component
             Analysis},
  journal = {IEEE Transaction on Neural Networks},
  year    = {1995},
  volume  = {6},
  pages   = {1255--1263},
  number  = {5}
}

@inproceedings{CheCheRamMus06,
  author    = {B.C. Chen and L. Chen and R. Ramakrishnan and D.R.
               Musicant},
  title     = {Learning from Aggregate Views},
  booktitle = {the 22nd International Conference on Data Engineering
               (ICDE)},
  pages     = {3-12},
  year      = {2006},
  editor    = {L. Liu and A. Reuter and K.Y. Whang and J. Zhang},
  address   = {Atlanta, GA}
}

@article{CheCheTeo08,
  author  = {Michael Chertkov and Vladimir Y. Chernyak and Razvan
             Teodorescu},
  title   = {Belief Propagation and Loop Series on Planar Graphs},
  pages   = {P05003},
  journal = {Journal of Statistical Mechanics},
  month   = may,
  year    = 2008
}

@inproceedings{CheChuLuaBaietal09,
  title     = {Collaborative filtering for orkut communities: discovery
               of user latent behavior},
  author    = {W.Y. Chen and J.C. Chu and J. Luan and H. Bai and Y. Wang
               and E.Y. Chang},
  booktitle = {World Wide Web {WWW}},
  publisher = {ACM},
  year      = {2009},
  editor    = {J. Quemada and G. Le{\'o}n and Y.S. Maarek and W. Nejdl},
  pages     = {681--690},
  url       = {http://doi.acm.org/10.1145/1526709.1526801}
}

@techreport{CheDonSau95,
  author      = {S. Chen and D. Donoho and M. Saunders},
  title       = {Atomic Decomposition by Basis Pursuit},
  institution = {Department of Statistics, Stanford University},
  year        = {1995},
  type        = {Technical Report},
  number      = {479},
  month       = {May}
}

@techreport{CheDonSau96,
  author      = {S.~S. Chen and D.~L. Donoho and M.~A. Saunders},
  title       = {Atomic Decomposition by Basis Pursuit},
  institution = {Stanford University},
  year        = {1996},
  number      = {Dept.~of Statistics Technical Report}
}

@article{CheDonSau99,
  author  = {S. Chen and D. Donoho and M. Saunders},
  title   = {Atomic decomposition by basis pursuit},
  journal = {SIAM J. Sci. Comput.},
  year    = {1999},
  volume  = {20},
  pages   = {33--61},
  number  = {1}
}

@inproceedings{Cheeseman83,
  author    = {P. C. Cheeseman},
  title     = {A Method of Computing Generalized Bayesian Probability
               Values for Expert Systems},
  booktitle = {Proc.\ Eighth International Conference on Artificial
               Intelligence},
  year      = {1983},
  pages     = {198--202},
  month     = aug,
  comment   = {Describes iterative graph-oriented method for computing a
               maximum-entropy distribution subject to constraints.}
}

@inproceedings{Cheeseman84,
  author    = {P. C. Cheeseman},
  title     = {Learning of Expert Systems from Data},
  booktitle = {Proc. of the Workshop on Principles of Knowledge-Based
               Systems},
  year      = {1984},
  pages     = {115--122},
  month     = dec,
  comment   = {Describes a `message-length' approach to inferring
               significant contingencies in a contingency table.}
}

@inproceedings{Cheeseman85,
  author    = {P.~C. Cheeseman},
  title     = {In defense of probability},
  booktitle = {the International Joint Conference on Artificial
               Intelligence},
  year      = {1985},
  pages     = {1002--1009}
}

@article{Cheeseman88,
  author  = {P.~C. Cheeseman},
  title   = {An inquiry into computer understanding},
  journal = {Computational Intelligence},
  year    = {1988},
  volume  = {4},
  pages   = {58--66},
  number  = {1},
  month   = feb
}

@inproceedings{Cheeseman88b,
  author    = {P. Cheeseman},
  title     = {Bayesian classification},
  booktitle = {Proceedings {AAAI-88} 7th National Conference on
               Artificial Intelligence},
  year      = {1988},
  address   = {St.\ Paul, Minnesota},
  month     = aug # { 21--26}
}

@article{CheFanLin06,
  author  = {Pai-Hsuen Chen and Rong-En Fan and Chih-Jen Lin},
  title   = {A Study on SMO-type Decomposition Methods for Support
             Vector Machines},
  journal = {{IEEE} Transactions on Neural Networks},
  year    = {2006},
  volume  = {17},
  number  = {4},
  pages   = {893--908}
}

@inbook{CheGilSunGouetal95,
  chapter   = {6},
  pages     = {95--114},
  title     = {A method for constructive learning of recurrent neural
               networks},
  publisher = {{MIT} Press},
  year      = {1995},
  author    = {Dong Chen and C. Lee Giles and Gordon Sun and Mark W.
               Goudreau and Hsing-Hen Chen and Yee-Chun Lee},
  volume    = {III: Selecting Good Models},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@article{CheGol59,
  author  = {E. W. Cheney and A. A. Goldstein},
  title   = {Newton's method for convex programming and Tchebycheff
             approximation},
  journal = {Numerische Mathematik},
  year    = {1959},
  volume  = {1},
  number  = {1},
  pages   = {253--268}
}

@inproceedings{CheGue07,
  author    = {A. Chechetka and C. Guestrin},
  title     = {Efficient principled learning of thin junction trees},
  year      = {2007},
  booktitle = {NIPS}
}

@inproceedings{CheHar00,
  author    = {S. Chen and C.~J. Harris},
  title     = {Design of the optimal separating hyperplane for the
               decision feedback equalizer using support vector machines},
  booktitle = {IEEE International Conference on Acoustic, Speech, and
               Signal Processing},
  year      = {2000},
  address   = {Istanbul, Turkey}
}

@article{CheHom96,
  author  = {Z. Chen and S. Homer},
  title   = {The bounded injury priority method and the learnability of
             unions of rectangles},
  journal = {Ann. Pure and Applied Logic},
  year    = {1996},
  volume  = {77},
  pages   = {143--168},
  number  = {2}
}

@article{CheHom97,
  author  = {Zhixiang Chen and Steven Homer},
  title   = {Learning counting functions with queries},
  journal = {Theoret. Comput. Sci.},
  year    = {1997},
  volume  = {180},
  pages   = {155--168},
  number  = {1--2},
  note    = {first in COLT '94}
}

@article{CheHonHar04,
  author  = {S. Chen and X. Hong and C. J. Harris},
  title   = {Sparse kernel density construction using orthogonal
             forward regression with leave-one-out test score and local
             regularization},
  year    = {2004},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics},
  volume  = {34},
  number  = {4},
  pages   = {1708--1717}
}

@article{CheHutSch07,
  author   = {Alexey Chernov and Marcus Hutter and J{\"u}rgen
              Schmidhuber},
  title    = {Algorithmic Complexity Bounds on Future Prediction
              Errors},
  journal  = {Information and Computation},
  volume   = {205},
  number   = {2},
  pages    = {242--261},
  month    = feb,
  year     = {2007},
  bibtex   = {http://www.hutter1.net/official/bib.htm#postbndx},
  url      = {http://arxiv.org/abs/cs.LG/0701120},
  conf     = {http://www-alg.ist.hokudai.ac.jp/~thomas/ALT05/alt05.jhtml},
  pdf      = {http://www.hutter1.net/ai/postbndx.pdf},
  ps       = {http://www.hutter1.net/ai/postbndx.ps},
  latex    = {http://www.hutter1.net/ai/postbndx.tex},
  slides   = {http://www.hutter1.net/ai/spostbnd.pdf},
  project  = {http://www.hutter1.net/official/projects.htm#ait},
  doi      = {10.1016/j.ic.2006.10.004},
  issn     = {0890-5401},
  keywords = {Kolmogorov complexity, posterior bounds, online sequential
              prediction, Solomonoff prior, monotone conditional
              complexity, total error, future loss, randomness
              deficiency},
  abstract = {We bound the future loss when predicting any (computably)
              stochastic sequence online. Solomonoff finitely bounded the
              total deviation of his universal predictor $M$ from the
              true distribution $mu$ by the algorithmic complexity of
              $mu$. Here we assume we are at a time $t>1$ and already
              observed $x=x_1...x_t$. We bound the future prediction
              performance on $x_{t+1}x_{t+2}...$ by a new variant of
              algorithmic complexity of $mu$ given $x$, plus the
              complexity of the randomness deficiency of $x$. The new
              complexity is monotone in its condition in the sense that
              this complexity can only decrease if the condition is
              prolonged. We also briefly discuss potential
              generalizations to Bayesian model classes and to
              classification problems.},
  support  = {SNF grant 2000-61847}
}

@article{CheKanKan97,
  author  = {Jianer Chen and Saroja P. Kanchi and Arkady Kanevsky},
  title   = {A Note on Approximating Graph Genus},
  pages   = {317--322},
  journal = {Information Processing Letters},
  volume  = {61},
  number  = {6},
  year    = {1997}
}

@inproceedings{CheLin06,
  title     = {Soft syntactic constraints for word alignment through
               discriminative training},
  author    = {Colin Cherry and Dekang Lin},
  booktitle = {the COLING/ACL on Main conference poster sessions},
  year      = {2006}
}

@inproceedings{CheLiXiaetal17,
  title     = {Dual Path Networks},
  author    = {Yunpeng Chen and Jianan Li and Huaxin Xiao and Xiaojie Jin
               and Shuicheng Yan and Jiashi Feng},
  booktitle = nips,
  year      = {2017}
}

@inproceedings{CheMaa92,
  author    = {Z. Chen and W. Maass},
  title     = {On-line learning of rectangles},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {16--28},
  publisher = {ACM Press, New York, NY}
}

@article{CheMaa94,
  author    = {Zhixiang Chen and Wolfgang Maass},
  title     = {On-line learning of rectangles and unions of rectangles},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {17},
  pages     = {23--50},
  number    = {2/3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{CheMaXia18,
  title     = {Fastgcn: fast learning with graph convolutional networks
               via importance sampling},
  author    = {Chen, Jie and Ma, Tengfei and Xiao, Cao},
  booktitle = iclr,
  year      = {2018}
}

@article{CheMilSmi96,
  author    = {Peter C. Y. Chen and James K. Mills and Kenneth C. Smith},
  title     = {Performance Improvement of Robot Continuous-Path Operation
               through Iterative Learning Using Neural Networks},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {23},
  pages     = {191--220},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{CheMonForLiuetal16,
  title     = {Robust covariate shift regression},
  author    = {Chen, Xiangli and Monfort, Mathew and Liu, Anqi and
               Ziebart, Brian D},
  booktitle = aistats,
  pages     = {1270--1279},
  year      = {2016}
}

@inproceedings{CheMonZieCar16,
  title     = {Adversarial inverse optimal control for general imitation
               learning losses and embodiment transfer},
  booktitle = uai,
  year      = {2016},
  author    = {Chen, Xiangli and Monfort, Mathew and Ziebart, Brian D.
               and Carr, Peter}
}

@book{CheMul98,
  title     = {Learning from Data},
  publisher = {John Wiley and Sons},
  year      = {1998},
  author    = {V. Cherkassky and F. Mulier},
  address   = {New York}
}

@inproceedings{Chen06,
  author    = {A. Chen},
  title     = {Fast Kernel Density Independent Component Analysis},
  booktitle = {6th international conference on ICA and BSS },
  pages     = {24-- 31 },
  year      = {2006},
  volume    = {3889},
  series    = {Lecture Notes in Computer Science},
  address   = {Heidelberg},
  publisher = {Springer}
}

@inproceedings{chen2013discriminative,
  title     = {Discriminative System Identification via the Principle of
               Maximum Causal Entropy},
  author    = {Chen, Xiangli and Ziebart, Brian D},
  booktitle = {ICML Workshop on Machine Learning For System
               Identification},
  year      = {2013}
}

@inproceedings{chen2015imitation,
  title     = {Robust Covariate Shift Regression},
  author    = {Chen, Xiangli and Ziebart, Brian D and Carr, Peter},
  booktitle = { NIPS Workshop: Probabilistic Approaches for Robotics and
               Control},
  year      = {2015}
}

@inproceedings{chen2015predictive,
  title     = {Predictive Inverse Optimal Control for
               Linear-Quadratic-Gaussian Systems},
  author    = {Chen, Xiangli and Ziebart, Brian D},
  booktitle = {Artificial Intelligence and Statistics (AISTATS)},
  year      = {2015}
}

@inproceedings{chen2016adversarial,
  title     = {Adversarial Inverse Optimal Control for General Imitation
               Learning Losses and Embodiment Transfer},
  author    = {Chen, Xiangli and Monfort, Mathew and Ziebart, Brian D and
               Carr, Peter},
  booktitle = uai,
  year      = {2016}
}

@phdthesis{Chen81,
  author = {K. Chen},
  title  = {Tradeoffs in Machine Inductive Inference},
  school = {SUNY at Buffalo},
  year   = {1981},
  annote = {Also TR 178 from SUNY Buffalo Dept. of CS}
}

@article{Chen82,
  author  = {K. J. Chen},
  title   = {Tradeoffs in the inductive inference of nearly minimal
             size programs},
  journal = {Inform. Control},
  year    = {1982},
  volume  = {52},
  pages   = {68--86}
}

@inproceedings{Chen93,
  author    = {Z. Chen},
  title     = {Learning unions of two rectangles in the plane with
               equivalence queries},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {243--252},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{Chen94,
  author    = {Z. Chen},
  title     = {A note on learning {DNF} formulas using equivalence and
               incomplete membership queries},
  booktitle = alt-ws,
  year      = {1994},
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  year      = {2010},
  pages     = {361--369}
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@article{Chenetal12,
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@article{Chenetal14,
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                   Kokkinos and Kevin Murphy and Alan L. Yuille},
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@inproceedings{Chenetal15,
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               Raquel Urtasun},
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@inproceedings{Chenetal16,
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  abstract = {We introduce a new protocol for prediction with expert
              advice in which each expert evaluates the learner’s and
              his own performance using a loss function that may change
              over time and may be different from the loss functions used
              by the other experts. The learner’s goal is to perform
              better or not much worse than each expert, as evaluated by
              that expert, for all experts simultaneously. If the loss
              functions used by the experts are all proper scoring rules
              and all mixable, we show that the defensive forecasting
              algorithm enjoys the same performance guarantee as that
              attainable by the Aggregating Algorithm in the standard
              setting and known to be optimal. This result is also
              applied to the case of “specialist” experts. In this
              case, the defensive forecasting algorithm reduces to a
              simple modification of the Aggregating Algorithm.},
  journal  = {Algorithmic Learning Theory},
  author   = {Chernov, Alexey and Vovk, Vladimir},
  editor   = {Gavaldà, Ricard and Lugosi, Gábor and Zeugmann, Thomas
              and Zilles, Sandra},
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}

@inproceedings{CheVisSchWanetal06,
  author    = {Li Cheng and S.~V.~N. Vishwanathan and Dale Schuurmans and
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  year      = {2006},
  editor    = {B. {Sch\"olkopf} and J. Platt and T. Hofmann},
  address   = {Cambridge MA},
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@inproceedings{CheVisZha08,
  author    = {Li Cheng and S.~V.~N. Vishwanathan and Xinhua Zhang},
  title     = {Consistent Image Analogies using Semi-supervised
               Learning},
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  booktitle = cvpr,
  year      = 2008
}

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  author    = {Yizong Cheng and Zhangyong Wan},
  volume    = {III: Selecting Good Models},
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@inproceedings{CheYuZhaXinetal16,
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@inproceedings{CheZhaCha08,
  title     = {Combinational collaborative filtering for personalized
               community recommendation},
  author    = {W. Chen and D. Zhang and E.Y. Chang},
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               Knowledge Discovery and Data Mining},
  publisher = {ACM},
  year      = {2008},
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  pages     = {115--123},
  url       = {http://doi.acm.org/10.1145/1401890.1401909}
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@inproceedings{CheZhaSch13,
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  year      = {2013}
}

@inproceedings{CheZhuSon17,
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  year      = {2017}
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  address   = {Stockholm, Sweden},
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  author    = {Choquet, Gustave},
  year      = {1962},
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@article{ChoSau10,
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@inproceedings{ChoTanAnaWil10,
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@inproceedings{ChoThaLeaCapetal07,
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@inproceedings{ChoTul06,
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@article{chow:68,
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@inproceedings{ChoYoo00,
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  author    = {Cheng-Tao Chu and Sang Kyun Kim and Yi-An Lin and Yuanyuan
               Yu and Gary Bradski and Andrew Y. Ng and Kunle Olukotun},
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  booktitle = nips19,
  editor    = nips19e,
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}

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  location  = {Washington, DC, USA},
  pages     = {889--898},
  numpages  = {10},
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  publisher = {ACM},
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  number    = {261},
  publisher = {Springer Science+Business Media},
  author    = {Çınlar, E.},
  year      = {2011},
  note      = {OCLC: ocn253217106},
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  author    = {Cisse, Moustapha and Bojanowski, Piotr and Grave, Edouard
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  booktitle = icml,
  year      = {2017}
}

@inproceedings{Cisse_ParsevalNetworks_2017,
  address   = {Sydney, Australia},
  title     = {Parseval networks: {Improving} robustness to adversarial
               examples},
  volume    = {70},
  abstract  = {We introduce Parseval networks, a form of deep neural
               networks in which the Lipschitz constant of linear,
               convolutional and aggregation layers is constrained to be
               smaller than 1. Parseval networks are empirically and
               theoretically motivated by an analysis of the robustness of
               the predictions made by deep neural networks when their
               input is subject to an adversarial perturbation. The most
               important feature of Parseval networks is to maintain
               weight matrices of linear and convolutional layers to be
               (approximately) Parseval tight frames, which are extensions
               of orthogonal matrices to non-square matrices. We describe
               how these constraints can be maintained efficiently during
               SGD. We show that Parseval networks match the
               state-of-the-art regarding accuracy on CIFAR-10/100 and
               Street View House Numbers (SVHN), while being more robust
               than their vanilla counterpart against adversarial
               examples. Incidentally, Parseval networks also tend to
               train faster and make a better usage of the full capacity
               of the networks.},
  booktitle = {Proceedings of the 34th international conference on
               machine learning},
  publisher = {Proceedings of machine learning research},
  author    = {Cisse, Moustapha and Bojanowski, Piotr and Grave, Edouard
               and Dauphin, Yann and Usunier, Nicolas},
  editor    = {Precup, Doina and Teh, Yee Whye},
  month     = aug,
  year      = {2017},
  pages     = {854--863}
}

@inproceedings{ClaMat93,
  author    = {Peter Clark and Stan Matwin},
  title     = {Learning Domain Theories using Abstract Beckground
               Knowledge},
  booktitle = {Machine Learning: ECML-93, European Conference on Machine
               Learning, Proceedings},
  year      = {1993},
  volume    = {667},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {360--365},
  publisher = {Springer-Verlag}
}

@article{Clancey89,
  author    = {William J. Clancey},
  title     = {The Knowledge Level Reinterpreted: Modeling How Systems
               Interact},
  journal   = {Machine Learning},
  year      = {1989},
  volume    = {4},
  pages     = {285--291},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{ClaNib87,
  author    = {P. Clark and T. Niblett},
  title     = {Induction in Noisy Domains},
  booktitle = {Progress in Machine Learning - EWSL 87: 2nd European
               Working Session on Learning},
  year      = {1987},
  pages     = {11--30},
  address   = {Bled, Yogoslavia},
  month     = may
}

@article{ClaNib89,
  author    = {Peter Clark and Tim Niblett},
  title     = {The {CN2} Induction Algorithm},
  journal   = {Machine Learning},
  year      = {1989},
  volume    = {3},
  pages     = {261--283},
  annote    = {Was a 1987 Tech Report from The Turing Institute},
  comment   = {Experimental study of rule induction, with accomodation
               for noise.},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Clark90,
  author  = {A. Clark},
  title   = {Inference of haplotypes from PCR-amplified samples of
             diploid populations},
  journal = {Molecular Biology and Evolution},
  volume  = {7},
  number  = {2},
  pages   = {111-122},
  year    = {1990}
}

@article{Clark93,
  author  = {R. Clark},
  title   = {Genetic Algorithms for Language Learning},
  journal = {Language Acquisition},
  year    = {1993}
}

@book{Clarke90,
  title     = {Optimization and Nonsmooth Analysis},
  publisher = {SIAM},
  year      = {1990},
  author    = {Frank H. Clarke}
}

@book{Clarke_NonsmoothAnalysis_1998,
  address   = {New York, NY, USA},
  series    = {Graduate {Texts} in {Mathematics}},
  title     = {Nonsmooth {Analysis} and {Control} {Theory}},
  volume    = {178},
  isbn      = {978-0-387-98336-3 978-0-387-22625-5},
  language  = {en},
  publisher = {Springer-Verlag},
  author    = {Clarke, Francis H. and Ledyaev, Yuri S. and Stern, Ronald
               J. and Wolenski, Peter R.},
  year      = {1998},
  doi       = {10.1007/b97650}
}

@inproceedings{Clarkson08,
  author    = {Kenneth L. Clarkson},
  title     = {Coresets, sparse greedy approximation, and the Frank-Wolfe
               algorithm},
  publisher = {Society for Industrial and Applied Mathematics},
  location  = {San Francisco, California},
  pages     = {922--931},
  booktitle = {SODA '08: the nineteenth annual ACM-SIAM symposium on
               Discrete algorithms},
  year      = 2008
}

@article{Clarkson10,
  author  = {Kenneth L. Clarkson},
  title   = {Coresets, Sparse Greedy Approximation, and the
             {F}rank-{W}olfe Algorithm},
  journal = {ACM Transactions on Algorithms},
  year    = {2010},
  volume  = {6},
  pages   = {1--30},
  number  = {4}
}

@inproceedings{ClaSto15,
  title     = {Training Deep Convolutional Neural Networks to Play Go},
  booktitle = icml,
  year      = {2015},
  pages     = {1766--1774},
  author    = {Clark, Christopher and Storkey, Amos}
}

@article{ClaTho04,
  author    = {Alexander Clark and Franck Thollard},
  title     = {{PAC}-learnability of Probabilistic Deterministic Finite
               State Automata},
  journal   = {J. Mach. Learn. Res.},
  year      = {2004},
  volume    = {5},
  pages     = {473-497},
  issn      = {1533-7928},
  publisher = {{MIT} Press}
}

@inbook{CleLee94,
  chapter   = {6},
  pages     = {81--95},
  title     = {Sensitivity constraints in learning},
  publisher = {{MIT} Press},
  year      = {1994},
  author    = {Scott H. Clearwater and Yongwon Lee},
  volume    = {II: Intersections Between Theory and Experiment},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@article{CleSha03,
  author  = {R. Clement and D. Sharp},
  title   = {Ngram and {B}ayesian Classification of Documents for Topic
             and Authorship},
  journal = {Literary and Linguistic Computing},
  volume  = {18},
  number  = {4},
  year    = {2003},
  pages   = {423-447}
}

@inproceedings{CleTri95,
  author    = {John G. Cleary and Leonard E. Trigg},
  title     = {{K}*: an instance-based learner using an entropic distance
               measure},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {108--114},
  publisher = {Morgan Kaufmann}
}

@inproceedings{CliftonForlines_HeuristicsImproving_2012b,
  series   = {{AAAI} {Fall} {Symposium} {Series}},
  title    = {Heuristics for improving forecast aggregation},
  abstract = {The aggregate prediction from a group of forecasters often
              outperformsthe individual forecasts from experts. In this
              paper, wepresent an improvement on the traditional
              “Wisdom of Crowds” aggregation technique that uses
              extra information elicited fromforecasters along with their
              prediction. An analysis of 64 forecastingquestions answered
              by a group of 1000+ novice predictorsshows that applying
              heuristic weighting rules to forecasts resultsin a 28\%
              improvement in the accuracy of the group’s predictions.},
  author   = {{Clifton Forlines} and {Sarah Miller} and {Srinivasamurthy
              Prakash} and {John Irvine}},
  year     = {2012},
  keywords = {⛔ No DOI found, aggregation, forecasting, human
              judgement},
  file     = {Clifton Forlines et al - Heuristics for improving forecast
              aggregation
              (2012).pdf:/Users/zcranko/Documents/Library/Conference
              Paper/Clifton Forlines et al - Heuristics for improving
              forecast aggregation (2012).pdf:application/pdf}
}

@article{CliKanVaiLinetal02,
  author  = {C. Clifton and M. Kantarcioglu and J. Vaidya and X. Lin
             and and M. Zhu},
  title   = {{Tools for Privacy Preserving Distributed Data Mining}},
  journal = {{ACM SIGKDD Explorations}},
  year    = {2002},
  volume  = {4},
  number  = {2},
  month   = {December}
}

@article{Cobzas_LipschitzProperties_2017,
  title   = {Lipschitz properties of convex functions},
  volume  = {2},
  doi     = {10.22034/aot.1610.1022},
  number  = {1},
  journal = {Advances in Operator Theory},
  author  = {Cobzas, Stefan},
  month   = jan,
  year    = {2017},
  file    = {Cobzas - Lipschitz properties of convex functions
             (2017).pdf:/Users/zcranko/Documents/Library/Journal
             Article/Cobzas - Lipschitz properties of convex functions
             (2017).pdf:application/pdf}
}

@inproceedings{CocMoo00,
  author    = {K. De Cock and B. De Moor},
  title     = {Subspace angles between linear stochastic models},
  booktitle = {Systems and Control Letters},
  year      = {2000},
  volume    = {2},
  pages     = {1561--1566}
}

@article{CocMoo02,
  author  = {K. De Cock and B. De Moor},
  title   = {Subspace angles between {ARMA} models},
  journal = {Systems Control Lett.},
  year    = {2002},
  volume  = {46},
  pages   = {265--270}
}

@inproceedings{CoeGru00,
  author    = {Jefferson Coelho and Rod Grupen},
  title     = {Learning in Non-stationary Conditions: {A} Control
               Theoretic Approach},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {151--158},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{CohAtlLad94,
  author    = {David Cohn and Les Atlas and R. Ladner},
  title     = {Improving generalization with active learning},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {15},
  pages     = {201--221},
  number    = {2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{CohCha00,
  author    = {David Cohn and Huan Chang},
  title     = {Learning to Probabilistically Identify Authoritative
               Documents},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {167--174},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{CohDau96,
  author  = {A. Cohen and I. Daubechies},
  title   = {A new method to determine the regularity of refinable
             functions },
  journal = {Rev.\ Mat.\ Iberoamericana},
  year    = {1996},
  volume  = {12},
  pages   = {527--591}
}

@inproceedings{CohDev97,
  author    = {William W. Cohen and Prem Devanbu},
  title     = {A comparative study of inductive logic programming methods
               for software fault prediction},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {66--74},
  publisher = {Morgan Kaufmann}
}

@inproceedings{Cohen00,
  author    = {William W. Cohen},
  title     = {Automatically Extracting Features for Concept Learning
               from the Web},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {159--166},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{Cohen00b,
  author    = {William W. Cohen},
  title     = {Extracting Information from the Web for Concept Learning
               and Collaborative Filtering},
  address   = {Sydney, Australia},
  booktitle = alt,
  month     = dec,
  year      = {2000},
  volume    = {1968},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {1--12},
  publisher = {Springer, Berlin}
}

@article{cohen2017emnist,
  title   = {EMNIST: an extension of MNIST to handwritten letters},
  author  = {Cohen, Gregory and Afshar, Saeed and Tapson, Jonathan and
             van Schaik, Andr{\'e}},
  journal = {arXiv preprint arXiv:1702.05373},
  year    = {2017}
}

@article{Cohen92,
  author    = {William W. Cohen},
  title     = {Abductive Explanation-Based Learning: {A} Solution to the
               Multiple Inconsistent Explanation Problem},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {8},
  pages     = {167--219},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Cohen93,
  author    = {William Cohen},
  title     = {Cryptographic Limitations on Learning One-Clause Logic
               Programs},
  booktitle = {Proc. of the Tenth National Conference on Artificial
               Intelligence},
  year      = {1993},
  address   = {Washington, D.C.}
}

@article{Cohen93b,
  author    = {William W. Cohen},
  title     = {Creating a Memory of Casual Relationships},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {10},
  pages     = {179--183},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Cohen93c,
  author    = {William Cohen},
  title     = {Pac-Learning a Restricted Class of Recursive Logic
               Programs},
  booktitle = {Proc. of the Tenth National Conference on Artificial
               Intelligence},
  year      = {1993},
  address   = {Washington, D.C.}
}

@article{Cohen94,
  author    = {William W. Cohen},
  title     = {Incremental abductive {EBL}},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {15},
  pages     = {5--24},
  number    = {1},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Cohen95,
  author    = {W. Cohen},
  title     = {Fast effective rule induction},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995}
}

@inproceedings{Cohen95b,
  author    = {William W. Cohen},
  title     = {Text categorization and relational learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {124--132},
  publisher = {Morgan Kaufmann}
}

@inproceedings{Cohen96,
  author    = {William W. Cohen},
  title     = {Learning Rules that Classify e-mails},
  booktitle = {the AAAI Spring Symposium on Machine Learning in
               Information},
  year      = {1996}
}

@inproceedings{Cohen97,
  author    = {Edith Cohen},
  title     = {Learning noisy perceptrons by a perceptron in polynomial
               time},
  booktitle = {Proc. 38th Annu. IEEE Symposium on Foundations of Computer
               Science},
  year      = {1997},
  pages     = {514--523},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@article{Cohen98,
  author    = {William W. Cohen},
  title     = {Hardness results for learning first-order representations
               and programming by demonstration},
  journal   = {Machine Learning},
  year      = {1998},
  volume    = {30},
  pages     = {57--87},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Cohen99,
  author    = {William W. Cohen},
  title     = {What can we learn from the web?},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {515--521},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inbook{CohFei68,
  chapter   = {XIV: {\em Learning and Inductive Inference}},
  pages     = {324--511},
  title     = {The Handbook of Artificial Intelligence},
  publisher = {William Kaufman, Inc.},
  year      = {1968},
  editor    = {P. R. Cohen and E. A. Feigenbaum},
  volume    = {3},
  address   = {Los Altos, California}
}

@techreport{CohGhaJor95,
  author      = {D. Cohn and Z. Ghahramani and M.~I. Jordan},
  title       = {Active Learning with Statistical Models},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1995},
  type        = {{A.I. Memo}},
  number      = {1522},
  address     = {Cambridge, MA},
  month       = jan
}

@article{CohGhaJor96,
  author  = {David A. Cohn and Z. Ghahramani and M.~I. Jordan},
  title   = {Active Learning with Statistical Models},
  journal = {Journal of Artificial Intelligence Research},
  year    = {1996},
  volume  = {4},
  pages   = {129--145}
}

@inbook{CohGreSch94,
  chapter   = {11},
  pages     = {171--181},
  title     = {Probabilistic hill-climbing},
  publisher = {{MIT} Press},
  year      = {1994},
  author    = {William W. Cohen and Russell Greiner and Dale Schuurmans},
  volume    = {II: Intersections Between Theory and Experiment},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@inproceedings{CohHir92,
  author    = {W. W. Cohen and H. Hirsh},
  title     = {Learnability of description logics},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {116--127},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{CohHir94,
  author    = {William W. Cohen and Haym Hirsh},
  title     = {Learning the {CLASSIC} Description Logic: Theoretical and
               Experimental Results},
  booktitle = {Principles of Knowledge Representation and Reasoning: the
               Fourth International Conference (KR94)},
  year      = {1994},
  publisher = {Morgan Kaufmann},
  editor    = {J. Doyle and E. Sandewall and P. Torasso}
}

@article{CohHir94b,
  author    = {William W. Cohen and Haym Hirsh},
  title     = {Learnability of description logics with equality
               constraints},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {17},
  pages     = {169--200},
  number    = {2/3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{CohHir95,
  author    = {William W. Cohen and Haym Hirsh},
  title     = {Corrigendum for: {L}earnability of description logics},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {463},
  publisher = {ACM Press, New York, NY}
}

@article{CohKulBer95,
  author    = {D. M. Cohen and C. Kulikowski and H. Berman},
  title     = {{DEXTER}: {A} System that Experiments with Choices of
               Training Data Using Expert Knowledge in the Domain of {DNA}
               Hydration},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {21},
  pages     = {81--101},
  number    = {1/2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{CohRosKol19,
  title     = {Certified Adversarial Robustness via Randomized
               Smoothing},
  author    = {Jeremy M Cohen and Elan Rosenfeld and J. Zico Kolter},
  booktitle = icml,
  year      = {2019}
}

@article{CohSchSin99,
  author  = {William W. Cohen and Robert E. Schapire and Yoram Singer},
  title   = {Learning to Order Things},
  journal = {Journal of Artificial Intelligence Research},
  year    = {1999},
  volume  = {10},
  pages   = {243--270}
}

@inproceedings{CohTes91,
  author    = {D. Cohn and G. Tesauro},
  title     = {Can Neural Networks do better than the {VC} Bounds},
  booktitle = {Advances in Neural Information Processing Systems 3},
  year      = {1991},
  editor    = {R. Lippmann and J.~E. Moody and D.~S. Touretzky},
  pages     = {911--917},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{CohWel16,
  abstract  = {We introduce Group equivariant Convolutional Neural
               Networks (G-CNNs), a natural generalization of
               convolutional neural networks that reduces sample
               complexity by exploiting symmetries. G-CNNs use
               G-convolutions, a new type of layer that enjoys a
               substantially higher degree of weight sharing than regular
               convolution layers. G-convolutions increase the expressive
               capacity of the network without increasing the number of
               parameters. Group convolution layers are easy to use and
               can be implemented with negligible computational overhead
               for discrete groups generated by translations, reflections
               and rotations. G-CNNs achieve state of the art results on
               CIFAR10 and rotated MNIST.},
  booktitle = icml,
  author    = {Cohen, Taco S. and Welling, Max},
  title     = {{Group Equivariant Convolutional Networks}},
  year      = {2016}
}

@article{CoiShkSigSin08,
  author  = {Ronald R. Coifman and Yoel Shkolnisky and Fred J. Sigworth
             and Amit Singer},
  title   = {Graph Laplacian Tomography From Unknown Random
             Projections},
  journal = {IEEE Transactions on Image Processing},
  volume  = 17,
  pages   = {1891--1899},
  year    = 2008
}

@article{CoiWic92,
  author  = {R.~R. Coifman and M.~V. Wickerhauser},
  title   = {Entropy-based algorithms for best-basis selection},
  journal = {IEEE Transactions on Information Theory},
  year    = {1992},
  volume  = {38},
  pages   = {713--718}
}

@techreport{ColBen00,
  author      = {R. Colloberet and S. Bengio},
  title       = {Support Vector Machines for Large-Scale Regression
                 Problems},
  institution = {{IDIAP}},
  year        = {2000},
  type        = {{IDIAP-RR}},
  number      = {00-17},
  note        = {Description of {SVM} {T}orch Regression Code}
}

@inproceedings{ColBenBen02,
  author    = {R. Collobert and S. Bengio and Y. Bengio},
  title     = {A Parallel Mixture of {SVMs} for Very Large Scale
               Problems},
  booktitle = {Advances in Neural Information Processing Systems 15},
  year      = {2002},
  editor    = {S. Becker and S. Thrun and K. Obermayer},
  publisher = {{MIT} Press}
}

@inproceedings{ColBunWal00,
  author    = {Simon Colton and Alan Bundy and Toby Walsh},
  title     = {Automatic Identification of Mathematical Concepts},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {183--190},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{ColDuf01,
  author    = {M. Collins and N. Duffy},
  title     = {Convolution Kernels for Natural Language},
  booktitle = {Advances in Neural Information Processing Systems 14},
  year      = {2001},
  pages     = {625-632},
  editor    = {T.~G. Dietterich and S. Becker and Z. Ghahramani},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{ColGloKooCaretal08,
  author    = {Michael Collins and Amir Globerson and Terry Koo and
               Xavier Carreras and Peter Bartlett},
  title     = {Exponentiated Gradient Algorithms for Conditional Random
               Fields and Max-Margin Markov Networks},
  journal   = jmlr,
  year      = {2008},
  volume    = {9},
  pages     = {1775--1822},
  publisher = mitpr
}

@inproceedings{ColHar00,
  author       = {R. Cole and R. Hariharan},
  title        = {Faster suffix tree construction with missing suffix
                  links},
  booktitle    = {the Thirty Second Annual Symposium on the Theory of
                  Computing},
  year         = {2000},
  address      = {Portland, OR, USA},
  month        = {May},
  organization = {ACM}
}

@inproceedings{Collins00,
  author    = {M. Collins},
  title     = {Discriminative Reranking for Natural Language Parsing},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {175--182},
  publisher = {Morgan Kaufmann},
  address   = {San Francisco, CA}
}

@inproceedings{Collins02,
  author    = {M. Collins},
  title     = {Discriminative training methods for Hidden {M}arkov
               Models},
  booktitle = {the Conference on Empirical Methods in Natural Language
               Processing},
  year      = {2002}
}

@inproceedings{Collobert11,
  author    = {Ronan Collobert},
  title     = {Deep Learning for Efficient Discriminative Parsing},
  booktitle = icml,
  year      = 2011
}

@article{ColMyrNeu96,
  author  = {Charles J. Colbourn and Wendy J. Myrvold and Eugene
             Neufeld},
  title   = {Two Algorithms for Unranking Arboresences},
  journal = {Journal of Algorithms},
  year    = {1996},
  volume  = {20},
  pages   = {268-281}
}

@inproceedings{ColSchSin00,
  author    = {M. Collins and R. E. Schapire and Y. Singer},
  title     = {Logistic Regression, {A}da{B}oost and {B}regman
               Distances},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {158--169},
  publisher = {Morgan Kaufmann, San Francisco}
}

@inproceedings{ColSinWesBot06,
  title     = {Trading convexity for scalability},
  author    = {R. Collobert and F.H. Sinz and J. Weston and L. Bottou},
  booktitle = {Machine Learning, the Twenty-Third International
               Conference ({ICML} 2006)},
  publisher = {ACM},
  year      = {2006},
  editor    = {W.W. Cohen and A. Moore},
  pages     = {201--208}
}

@article{columbus2017linkedin,
  title   = {LinkedIn's Fastest-Growing Jobs Today Are In Data Science
             And Machine Learning},
  author  = {Louis Columbus},
  journal = {Forbes},
  year    = {2017},
  url     = {https://www.forbes.com/sites/louiscolumbus/2017/12/11/linkedins-fastest-growing-jobs-today-are-in-data-science-machine-learning}
}

@article{CombettesWajs05,
  author  = {Patrick L. Combettes and Val\'erie R. Wajs},
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@article{CoxIsh88,
  author  = {D.R. Cox and V. Isham},
  title   = {A simple spatial-temporal model of rainfall},
  journal = {the Royal Society London {A}},
  year    = {1988},
  volume  = {415},
  pages   = {317-328}
}

@inbook{CoxLeo94,
  chapter   = {18},
  pages     = {297--319},
  title     = {Unsupervised learning for mobile robot navigation using
               probabilistic data association},
  publisher = {{MIT} Press},
  year      = {1994},
  author    = {Ingemar J. Cox and John J. Leonard},
  volume    = {II: Intersections Between Theory and Experiment},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@article{CoxOSu90,
  author  = {D. Cox and F.~O'Sullivan},
  title   = {Asymptotic analysis of penalized likelihood and related
             estimators},
  journal = {Ann. Statist.},
  year    = {1990},
  volume  = {18},
  pages   = {1676--1695}
}

@incollection{CozCoh06,
  title     = {Risks of Semi-Supervised Learning: How Unlabeled Data Can
               Degrade Performance of Generative Classifiers},
  author    = {Fabio Cozman and Ira Cohen},
  year      = {2006},
  booktitle = {Semi-Supervised Learning},
  pages     = {57--72},
  series    = {Adaptive Computation and Machine Learning},
  editor    = {Olivier Chapelle and Bernhard Schoelkopf and Alexander
               Zien},
  publisher = {{MIT} Press}
}

@inproceedings{CozCohCir03,
  author    = {F.G. Cozman and I. Cohen and M.C. Cirelo},
  title     = {Semi-Supervised Learning of Mixture Models},
  booktitle = {the Twentieth International Conference on Machine
               Learning},
  year      = {2003},
  editor    = {T. Fawcett and N. Mishra},
  volume    = {20},
  pages     = {99--106},
  publisher = {{AAAI} Press}
}

@misc{CPLEX-Optimization-Inc-94,
  author       = {{{CPLEX} Optimization Inc.}},
  title        = {Using the {CPLEX} Callable Library},
  howpublished = {Manual},
  year         = {1994}
}

@article{CraDekKesShaetal06,
  author  = {K. Crammer and Ofer Dekel and Joseph Keshet and Shai
             Shalev-Shwartz and Y. Singer},
  title   = {Online passive-aggressive algorithms},
  journal = {J. Mach. Learn. Res.},
  year    = {2006},
  volume  = {7},
  pages   = {551--585},
  month   = {March}
}

@inproceedings{CraDekShaSin03,
  author    = {K. Crammer and Ofer Dekel and Shai Shalev-Shwartz and Y.
               Singer},
  title     = {Online passive-aggressive algorithms},
  booktitle = {Advances in Neural Information Processing Systems 16},
  year      = {2003},
  editor    = {S. Thrun and L. Saul and B. Sch\"{o}lkopf},
  publisher = {{MIT} Press}
}

@inproceedings{CraHut95,
  author    = {Susan Craw and Paul Hutton},
  title     = {Protein folding: symbolic refinement competes with neural
               networks},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {133--141},
  publisher = {Morgan Kaufmann}
}

@inproceedings{CraKanSin04,
  author    = {K. Crammer and J. Kandola and Y. Singer},
  title     = {Online Classification on a Budget},
  editor    = {S. Thrun and L. Saul and B. Sch\"{o}lkopf},
  booktitle = {Advances in Neural Information Processing Systems 16},
  pages     = {225--232},
  year      = {2004},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@incollection{CraKeaWor07,
  title     = {Learning from Multiple Sources},
  author    = {K. Crammer and M. Kearns and J. Wortman},
  booktitle = nips,
  pages     = {321--328},
  year      = {2007}
}

@inproceedings{CraKesSin02,
  author    = {K. Crammer and J. Keshet and Y. Singer},
  title     = {Kernel Design Using Boosting},
  booktitle = {Advances in Neural Information Processing Systems 15},
  year      = {2002},
  editor    = {S. Becker and S. Thrun and K. Obermayer}
}

@book{Cramer46,
  title     = {Mathematical Methods of Statistics},
  publisher = {Princeton University Press},
  year      = {1946},
  author    = {H. Cram\'{e}r}
}

@phdthesis{Crammer05,
  author = {K. Crammer},
  title  = {Online Learning for Complex Categorical Problems},
  school = {Hebrew University of Jerusalem},
  year   = {2005}
}

@article{crammer2001algorithmic,
  title   = {On the algorithmic implementation of multiclass
             kernel-based vector machines},
  author  = {Crammer, Koby and Singer, Yoram},
  journal = {Journal of machine learning research},
  volume  = {2},
  number  = {Dec},
  pages   = {265--292},
  year    = {2001}
}

@unpublished{Crane06,
  author = {Keenan Crane},
  title  = {Homotopy, Homology, and Cut Graphs},
  note   = {{\small\url{http://graphics.cs.uiuc.edu/svn/
            kcrane/web/project_topology.html}}},
  year   = {2006}
}

@article{cranko2018lipschitz,
  title   = {Lipschitz Networks and Distributional Robustness},
  author  = {Cranko, Zac and Kornblith, Simon and Shi, Zhan and Nock,
             Richard},
  journal = {arXiv:1809.01129},
  year    = {2018}
}

@inproceedings{Cranko_BoostedDensity_2019,
  address   = {Long Beach, CA, USA},
  title     = {Boosted density estimation remastered},
  volume    = {97},
  abstract  = {There has recently been a steady increase in the number
               iterative approaches to density estimation. However, an
               accompanying burst of formal convergence guarantees has not
               followed; all results pay the price of heavy assumptions
               which are often unrealistic or hard to check. The
               \textit{Generative Adversarial Network (GAN)} literature
               — seemingly orthogonal to the aforementioned pursuit —
               has had the side effect of a renewed interest in
               variational divergence minimisation (notably f-GAN). We
               show how to combine this latter approach and the classical
               boosting theory in supervised learning to get the first
               density estimation algorithm that provably achieves
               geometric convergence under very weak assumptions. We do so
               by a trick allowing to combine \textit{classifiers} as the
               sufficient statistics of an exponential family. Our
               analysis includes an improved variational characterisation
               of f-GAN.},
  booktitle = {Proceedings of the 36th international conference on
               machine learning},
  publisher = {Proceedings of machine learning research},
  author    = {Cranko, Zac and Nock, Richard},
  editor    = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  month     = jun,
  year      = {2019},
  pages     = {1416--1425}
}

@inproceedings{Cranko_MongeBlunts_2019,
  address   = {Long Beach, CA, USA},
  title     = {Monge blunts {Bayes}: hardness results for adversarial
               training},
  volume    = {97},
  abstract  = {The last few years have seen a staggering number of
               empirical studies of the robustness of neural networks in a
               model of adversarial perturbations of their inputs. Most
               rely on an adversary which carries out local modifications
               within prescribed balls. None however has so far questioned
               the broader picture: how to frame a
               \textit{resource-bounded} adversary so that it can be
               \textit{severely detrimental} to learning, a non-trivial
               problem which entails at a minimum the choice of loss and
               classifiers. We suggest a formal answer for losses that
               satisfy the minimal statistical requirement of being
               \textit{proper}. We pin down a simple sufficient property
               for any given class of adversaries to be detrimental to
               learning, involving a central measure of “harmfulness”
               which generalizes the well-known class of integral
               probability metrics. A key feature of our result is that it
               holds for \textit{all} proper losses, and for a popular
               subset of these, the optimisation of this central measure
               appears to be \textit{independent of the loss}. When
               classifiers are Lipschitz – a now popular approach in
               adversarial training –, this optimisation resorts to
               \textit{optimal transport} to make a low-budget compression
               of class marginals. Toy experiments reveal a finding
               recently separately observed: training against a
               sufficiently budgeted adversary of this kind
               \textit{improves} generalization.},
  booktitle = {Proceedings of the 36th international conference on
               machine learning},
  publisher = {Proceedings of machine learning research},
  author    = {Cranko, Zac and Menon, Aditya and Nock, Richard and Ong,
               Cheng Soon and Shi, Zhan and Walder, Christian},
  editor    = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  month     = jun,
  year      = {2019},
  pages     = {1406--1415}
}

@inproceedings{CraPagShaBocetal00,
  author    = {Mark Craven and David Page and Jude Shavlik and Joseph
               Bockhorst and Jeremy Glasner},
  title     = {Using Multiple Levels of Learning and Diverse Evidence
               Sources to Uncover Coordinately Controlled Genes},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {199--206},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@book{CraPir86,
  title     = {Applicable Differential Geometry},
  publisher = {Cambridge University Press},
  year      = {1986},
  author    = {M. Crampin and F.~A.~E. Pirani},
  address   = {Cambridge, UK}
}

@inproceedings{CraSha94,
  author    = {Mark W. Craven and Jude W. Shavlik},
  title     = {Using sampling and queries to extract rules from trained
               neural networks},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1994},
  pages     = {37--45},
  publisher = {Morgan Kaufmann}
}

@inbook{CraSha95,
  chapter   = {16},
  pages     = {307--321},
  title     = {Investigating the value of a good input representation},
  publisher = {{MIT} Press},
  year      = {1995},
  author    = {Mark W. Craven and Jude W. Shavlik},
  volume    = {III: Selecting Good Models},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@inproceedings{CraSin00,
  author    = {K. Crammer and Y. Singer},
  title     = {On the Learnability and Design of Output Codes for
               Multiclass Problems},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2000},
  editor    = {N. Cesa-Bianchi and S. Goldman},
  pages     = {35--46},
  address   = {San Francisco, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@article{CraSin01,
  author  = {K. Crammer and Y. Singer},
  title   = {On the Algorithmic Implementation of Multiclass
             Kernel-based Vector Machines},
  journal = {J. Mach. Learn. Res.},
  volume  = {2},
  pages   = {265--292},
  year    = {2001}
}

@inproceedings{CraSin02,
  author    = {K. Crammer and Y. Singer},
  title     = {Pranking with ranking},
  booktitle = {Advances in Neural Information Processing Systems 14},
  year      = {2002},
  publisher = {{MIT} Press},
  address   = {Cambridge, MA}
}

@article{CraSin02b,
  author  = {K. Crammer and Y. Singer},
  title   = {On the Learnability and Design of Output Codes for
             Multiclass Problems.},
  journal = {Machine Learning},
  volume  = {47},
  number  = {2-3},
  year    = {2002},
  pages   = {201--233}
}

@article{CraSin03,
  author  = {K. Crammer and Y. Singer},
  title   = {Ultraconservative Online Algorithms for Multiclass
             Problems},
  journal = {Journal of Machine Learning Research},
  year    = {2003},
  volume  = {3},
  pages   = {951--991},
  month   = {January}
}

@article{CraSin03b,
  author  = {K. Crammer and Y. Singer},
  title   = {A Family of Additive Online Algorithms for Category
             Ranking},
  journal = {J. Mach. Learn. Res.},
  year    = {2003},
  volume  = {3},
  pages   = {1025--1058},
  month   = {February}
}

@inproceedings{CraSin05,
  author    = {K. Crammer and Y. Singer},
  editor    = {P. Auer and R. Meir},
  title     = {Loss Bounds for Online Category Ranking},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2005},
  publisher = {Springer-Verlag},
  address   = {Berlin, Germany},
  pages     = {48--62}
}

@article{CraSin05b,
  author  = {K. Crammer and Y. Singer},
  title   = {Online Ranking by Projecting.},
  journal = {Neural Computation},
  volume  = {17},
  number  = {1},
  year    = {2005},
  pages   = {145--175}
}

@article{CraSla01,
  author    = {Mark Craven and Se{\'{a}}n Slattery},
  title     = {Relational Learning with Statistical Predicate Invention:
               {B}etter Models for Hypertext},
  journal   = {Machine Learning},
  year      = {2001},
  volume    = {43},
  pages     = {97--119},
  number    = {1/2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Craven02,
  author  = {M. Craven},
  title   = {The Genomics of a Signaling Pathway: A {KDD} Cup Challenge
             Task},
  journal = {{SIGKDD} Explorations},
  year    = {2002},
  volume  = {4},
  number  = {2}
}

@article{Craven02b,
  author  = {Craven, M.},
  title   = {The Genomics of a Signaling Pathway: A KDD Cup Challenge
             Task},
  journal = {SIGKDD Explorations},
  year    = {2002},
  volume  = {4},
  number  = {2}
}

@article{CraWah79,
  author  = {P. Craven and G. Wahba},
  title   = {Smoothing noisy data with spline functions: estimating the
             correct degree of smoothing by the method of generalized
             cross-validation},
  journal = {Numer.\ Math.},
  year    = {1979},
  volume  = {31},
  pages   = {377--403}
}

@book{Crawley05,
  title     = {Statistics: An Introduction using R},
  publisher = {Wiley},
  year      = {2005},
  author    = {M.J. Crawley}
}

@inproceedings{CraZarRob05,
  author    = {N. Craswell and H. Zaragoza and S. Robertson},
  title     = {Microsoft Cambridge at TREC-14: Enterprise Track},
  booktitle = {the Fourteenth Text REtrieval Conference (TREC 2005)},
  year      = 2005,
  address   = {Gaithersburg}
}

@inproceedings{Creageretal19,
  title     = {Flexibly Fair Representation Learning by Disentanglement},
  author    = {Creager, Elliot and Madras, David and Jacobsen,
               Joern-Henrik and Weis, Marissa and Swersky, Kevin and
               Pitassi, Toniann and Zemel, Richard},
  booktitle = icml,
  year      = {2019}
}

@inproceedings{CreKorTur10,
  title        = {Performance of recommender algorithms on top-n
                  recommendation tasks},
  author       = {Cremonesi, Paolo and Koren, Yehuda and Turrin, Roberto},
  booktitle    = {the fourth ACM conference on Recommender systems},
  pages        = {39--46},
  year         = {2010},
  organization = {ACM}
}

@unpublished{CreMan80,
  author = {S. Crespi-Reghizzi and D. Mandrioli},
  title  = {Inferring grammars by means of profiles: a unifying view},
  note   = {Internal report, Istituto di Elettrotechnica ed
            Elettronica del Politechnico di Milano},
  year   = {1980}
}

@incollection{Crespi-Reghizzi72,
  author    = {S. Crespi-Reghizzi},
  title     = {An effective model for grammar inference},
  booktitle = {Information Processing 71},
  publisher = {North-Holland Publishing Co.},
  year      = {1972},
  pages     = {524--529}
}

@book{Cressie93,
  title     = {Statistics for Spatial Data},
  publisher = {John Wiley and Sons},
  year      = {1993},
  author    = {N.~A.~C. Cressie},
  address   = {New York}
}

@unpublished{CRF_TAO,
  title = {Project of training {CRFs} using {TAO} and {Torch}},
  note  = {\url{https://www.cs.uic.edu/~zhangx/courses.htm}}
}

@inproceedings{CriBur00,
  author    = {D.~J. Crisp and C.~J.~C. Burges},
  title     = {A Geometric Interpretation of $\nu$-{SVM} Classifiers},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A. Solla and T.~K. Leen and K.-R. MÂÃ¼ller},
  pages     = {244--250},
  publisher = {{MIT} Press}
}

@inproceedings{CriBur99,
  author    = {D. J. Crisp and C. J. C. Burges},
  title     = {A geometric interpretation of $\nu$-{SVM} classifiers},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {1999},
  editor    = {S.~A. Solla and T.~K. Leen and K.-R. MÂÃ¼ller},
  volume    = {12},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@techreport{CriCamSha98,
  author      = {Nello Cristianini and Colin Campbell and John
                 Shawe-Taylor},
  title       = {Multiplicative Updatings for Support Vector Learning},
  institution = {Royal Holloway College},
  year        = {1998},
  type        = {{NeuroCOLT} {Technical} {Report}},
  number      = {NC-TR-98-016}
}

@inproceedings{CriCamSha99,
  author    = {Nello Cristianini and Colin Campbell and John
               Shawe-Taylor},
  title     = {Multiplicative Updatings for Support Vector Learning},
  booktitle = {Proc. of 7th European Symposium on Artificial Neural
               Networks},
  year      = {1999},
  pages     = {189--194},
  address   = {Bruges,Belgium},
  month     = {April},
  publisher = {D-Facto Publishing}
}

@incollection{CriCamSha99b,
  author    = {N. Cristianini and C. Campbell and J. Shawe-Taylor},
  title     = {Dynamically adapting kernels in support vector machines},
  booktitle = {Advances in Neural Information Processing Systems 11},
  publisher = {{MIT} Press},
  year      = {1999},
  editor    = {M.~S. Kearns and S.~A. Solla and D.~A. Cohn},
  pages     = {204--210},
  address   = {Cambridge, MA}
}

@article{Crick89,
  author  = {F. Crick},
  title   = {The recent excitement about Neural Networks},
  journal = {Nature},
  year    = {1989},
  volume  = {337},
  pages   = {129--132}
}

@techreport{CriEliSha01,
  author      = {N. Cristianini and A. Elisseeff and J. Shawe-Taylor},
  title       = {On Optimizing Kernel Alignment},
  institution = {NeuroCOLT},
  year        = {2001},
  number      = {NC2-TR-2001-087},
  address     = {http://www.neurocolt.com}
}

@techreport{CriKanEliSha03,
  author      = {N. Cristianini and J. Kandola and A. Elisseeff and J.
                 Shawe-Taylor},
  title       = {On Optimizing Kernel Alignment},
  institution = {UC Davis Department of Statistics},
  year        = {2003}
}

@incollection{CriMarPog80,
  author    = {F.~H. Crick and D.~C. Marr and T. Poggio},
  title     = {An information-processing approach to understanding the
               visual cortex},
  booktitle = {The Organization of the Cerebral Cortex},
  publisher = {{MIT} Press},
  year      = {1980},
  editor    = {E.~O. Schmitt and F.~G. Worden and G.~S. Dennis},
  pages     = {503--533},
  address   = {Cambridge, CA}
}

@book{CriSha00,
  title     = {An Introduction to Support Vector Machines and Other
               Kernel-based Learning Methods},
  publisher = {Cambridge University Press},
  year      = {2000},
  author    = {Nello Cristianini and John {Shawe-Taylor}},
  address   = {Cambridge, UK}
}

@inproceedings{CriSha99,
  author    = {N. Cristianini and J. Shawe-Taylor},
  title     = {Bayesian Voting Schemes and Large Margin Classifiers},
  booktitle = {Advances in Kernel Methods\,---\,Support Vector Learning},
  year      = {1999},
  editor    = {B. Sch{\"o}lkopf and C.~J.~C. Burges and A. J. Smola},
  pages     = {55--68},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{CriShaEliKan02,
  author    = {N. Cristianini and J. Shawe-Taylor and A. Elisseeff and J.
               Kandola},
  title     = {On Kernel-Target Alignment},
  booktitle = {Advances in Neural Information Processing Systems 14},
  year      = {2002},
  editor    = {T.~G. Dietterich and S. Becker and Z. Ghahramani},
  pages     = {367--373},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{CriShaKan02,
  author    = {N. Cristianini and J. Shawe-Taylor and J. Kandola},
  title     = {Spectral Kernel Methods for Clustering},
  booktitle = {Advances in Neural Information Processing Systems 15},
  year      = {2002},
  editor    = {S. Becker and S. Thrun and K. Obermayer},
  volume    = {15},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{CriShaLod01,
  author    = {N. Cristianini and J. Shawe-Taylor and H. Lodhi},
  title     = {Latent semantic kernels},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2001},
  address   = {San Francisco},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{CriShaSyk98,
  author    = {Nello Cristianini and John Shawe-Taylor and Peter
               Sykacek},
  title     = {Bayesian Classifiers are Large Margin Hyperplanes in a
               {H}ilbert Space},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  editor    = {J. Shavlik},
  address   = {San Francisco, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@incollection{Critchley78,
  author    = {F. Critchley},
  title     = {Multidimensional scaling: a short critique and a new
               method},
  booktitle = {COMPSTAT},
  publisher = {Physica-Verlag},
  year      = {1978},
  editor    = {L.~C.~A. Corsten and J. Hermans},
  address   = {Vienna}
}

@unpublished{CRPWiki,
  title = {Chinese restaurant process},
  note  = {\url{https://en.wikipedia.org/wiki/Chinese_restaurant_process}}
}

@inproceedings{CruVid98,
  author    = {P. P. Cruz-Alc{\'a}zar and E. Vidal-Ruiz},
  title     = {Learning regular grammars to model music style: comparing
               different coding schemes},
  booktitle = {Proc. 4th International Colloquium on Grammatical
               Inference - ICGI '98},
  year      = {1998},
  volume    = {1433},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {211--222},
  publisher = {Springer-Verlag}
}

@inbook{CS12,
  title     = {Compressed Sensing: Theory and Applications},
  publisher = {Cambridge University Press},
  year      = {2012},
  editor    = {Yonina C. Eldar and Gitta Kutyniok}
}

@phdthesis{Csa02,
  author = {Lehel Csat\'o},
  title  = {Gaussian Processes - Iterative Sparse Approximations},
  school = {Aston University},
  year   = {2002}
}

@misc{CsaFokOpp00,
  author = {L. Csat\'o and E. Fokou\'e and M. Opper and B. Schottky
            and O. Winther},
  title  = {Efficient Approaches to {G}aussian Process
            Classification},
  year   = {2000}
}

@inproceedings{CsaOpp01,
  author    = {L. Csat\'o and M. Opper},
  title     = {Sparse representation for {G}aussian process models},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {444--450},
  publisher = {{MIT} Press}
}

@article{Csiszar_InformationtypeMeasures_1967,
  title    = {Information-type measures of difference of probability
              distributions and indirect observations},
  volume   = {2},
  issn     = {0081-6906},
  journal  = {Studia Scientiarum Mathematicarum Hungarica},
  author   = {Csiszár, I.},
  year     = {1967},
  note     = {tex.fjournal: Studia Scientiarum Mathematicarum Hungarica.
              A Quarterly of the Hungarian Academy of Sciences
              tex.mrclass: 94.20 (60.00) tex.mrnumber: 219345
              tex.mrreviewer: S. Kullback},
  keywords = {⛔ No DOI found},
  pages    = {299--318}
}

@inproceedings{CsuDanFan04,
  author    = {Gabriella Csurka and Christopher R. Dance and Lixin Fan
               and Jutta Willamowski and Cdric Bray},
  title     = {Visual categorization with bags of keypoints},
  booktitle = {In Workshop on Statistical Learning in Computer Vision},
  year      = {2004},
  pages     = {1--22}
}

@incollection{CuaFor00,
  author    = {C.~M. Cuadras and J. Fortiana},
  title     = {The Importance of Geometry in Multivariate Analysis and
               some Applications},
  booktitle = {Statistics for the 21st Century},
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  year      = {2018}
}

@techreport{CulKulMcC05,
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  series    = {Lecture Notes in Artificial Intelligence},
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@inproceedings{Dai_ProvableBayesian_2016,
  address   = {Cadiz, Spain},
  title     = {Provable {Bayesian} inference via particle mirror
               descent},
  volume    = {51},
  abstract  = {Bayesian methods are appealing in their flexibility in
               modeling complex data and ability in capturing uncertainty
               in parameters. However, when Bayes’ rule does not result
               in tractable closed-form, most approximate inference
               algorithms lack either scalability or rigorous guarantees.
               To tackle this challenge, we propose a simple yet provable
               algorithm, Particle Mirror Descent (PMD), to iteratively
               approximate the posterior density. PMD is inspired by
               stochastic functional mirror descent where one descends in
               the density space using a small batch of data points at
               each iteration, and by particle filtering where one uses
               samples to approximate a function. We prove result of the
               first kind that, with m particles, PMD provides a posterior
               density estimator that converges in terms of KL-divergence
               to the true posterior in rate O(1/). We demonstrate
               competitive empirical performances of PMD compared to
               several approximate inference algorithms in mixture models,
               logistic regression, sparse Gaussian processes and latent
               Dirichlet allocation on large scale datasets.},
  booktitle = {Proceedings of the 19th international conference on
               artificial intelligence and statistics},
  publisher = {Proceedings of machine learning research},
  author    = {Dai, Bo and He, Niao and Dai, Hanjun and Song, Le},
  editor    = {Gretton, Arthur and Robert, Christian C.},
  month     = may,
  year      = {2016},
  pages     = {985--994},
  file      = {Dai et al - Provable Bayesian inference via particle
               mirror descent
               (2016).pdf:/Users/zcranko/Documents/Library/Conference
               Paper/Dai et al - Provable Bayesian inference via particle
               mirror descent (2016).pdf:application/pdf}
}

@inproceedings{Daietal18,
  author    = {Hanjun Dai and Hui Li and Tian Tian and Xin Huang and Lin
               Wang and Jun Zhu and Le Song},
  title     = {Adversarial Attack on Graph Structured Data},
  booktitle = icml,
  year      = {2018}
}

@article{DaiFle06,
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@inproceedings{DaiLiTia18,
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               Wang, Lin and Zhu, Jun and Song, Le},
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@inproceedings{DaiXueYanYu07,
  title        = {Co-clustering based classification for out-of-domain
                  documents},
  author       = {Dai, Wenyuan and Xue, Gui-Rong and Yang, Qiang and Yu,
                  Yong},
  booktitle    = kdd,
  pages        = {210--219},
  year         = {2007},
  organization = {ACM}
}

@inproceedings{DaiXueYanYu07a,
  title     = {Transferring naive bayes classifiers for text
               classification},
  author    = {Dai, Wenyuan and Xue, Gui-Rong and Yang, Qiang and Yu,
               Yong},
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  year      = {2007}
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@inproceedings{DaiYanXueYu07,
  author    = {Dai, Wenyuan and Yang, Qiang and Xue, Gui-Rong and Yu,
               Yong},
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  booktitle = icml,
  series    = {ICML '07},
  year      = {2007},
  isbn      = {978-1-59593-793-3},
  pages     = {193--200},
  numpages  = {8},
  doi       = {10.1145/1273496.1273521},
  acmid     = {1273521},
  publisher = {ACM}
}

@inproceedings{DaiYanXueYu08,
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  booktitle = icml,
  year      = {2008},
  author    = {Dai, Wenyuan and Yang, Qiang and Xue, Gui-Rong and Yu,
               Yong}
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  author    = {Arnak Dalalyan and Renaud Keriven},
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  booktitle = nips,
  year      = {2009},
  editor    = {Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I.
               Williams and A. Culotta},
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  author    = {Nilesh Dalvi and Pedro Domingos and Mausam and Sumit
               Sanghai and Deepak Verma},
  title     = {Adversarial classification},
  booktitle = {KDD '04: the tenth ACM SIGKDD international conference on
               Knowledge discovery and data mining},
  year      = {2004},
  isbn      = {1-58113-888-1},
  pages     = {99--108},
  location  = {Seattle, WA, USA},
  doi       = {http://doi.acm.org/10.1145/1014052.1014066},
  publisher = {ACM Press},
  address   = {New York, NY, USA}
}

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  year      = {1997},
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  year      = {1993},
  pages     = {199--208},
  publisher = {ACM Press, New York, NY}
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  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {301--312},
  publisher = {Springer-Verlag}
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@article{Dalmau99b,
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  journal   = {Machine Learning},
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  volume    = {35},
  pages     = {207--224},
  number    = {3},
  comment   = {first in 10th COLT '97},
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@inproceedings{DalPitVelWil91,
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  year    = {1986},
  volume  = {69},
  pages   = {12--40},
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@inproceedings{Dalvietal04,
  author    = {N. Dalvi and P. Domingos and Mausam and S. Sanghai and D.
               Verma},
  title     = {Adversarial classification},
  booktitle = {KDD},
  year      = {2004}
}

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  author    = {Peter Damaschke},
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               Proceedings},
  year      = {2000},
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  series    = {Lecture Notes in Computer Science},
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  publisher = {Springer, Berlin}
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@inproceedings{Damaschke98,
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@proceedings{DanBotLit09,
  editor    = {Andrea Pohoreckyj Danyluk and L{\'e}on Bottou and Michael
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  title     = {the 26th Annual International Conference on Machine
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               2009},
  booktitle = icml,
  publisher = {ACM},
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  volume    = {382},
  year      = {2009},
  isbn      = {978-1-60558-516-1}
}

@inproceedings{Dangauthieretal08,
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               Thore Graepel},
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  year      = 2008,
  booktitle = {NIPS}
}

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               web},
  year      = {2009},
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  title     = {Semi-supervised or Semi-unsupervised?},
  author    = {Hal Daum{\'e} III},
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               S. Ganguli and Y. Bengio},
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               high-dimensional non-convex optimization},
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  year      = 2014
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@incollection{DavBliCraPer07,
  title     = {Analysis of Representations for Domain Adaptation},
  author    = {S. {Ben-David} and J. Blitzer and K. Crammer and F.
               Pereira},
  booktitle = {NIPS 19},
  publisher = {MIT Press},
  pages     = {137--144},
  year      = {2007}
}

@article{DavDonBou01,
  author  = {M. Davy and C. Doncarli and G. Faye Boudreaux-Bartels},
  title   = {Improved Optimization of Time-Frequency Based Signal
             Classifiers},
  journal = {IEEE Signal processing letters},
  year    = {2001},
  volume  = {8},
  pages   = {52--57},
  number  = {2},
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@inproceedings{DavDonTou00,
  author    = {M. Davy and C. Doncarli and J.Y. Tourneret},
  title     = {Supervised Classification Using MCMC Methods},
  booktitle = {IEEE ICASSP},
  year      = {2000},
  address   = {Istanbul, Turquie},
  month     = {June}
}

@techreport{DavDonTou01,
  author      = {M. Davy and C. Doncarli and J.Y. Tourneret},
  title       = {Supervised Bayesian learning Using MCMC Methods.
                 Application to the classification of chirps},
  institution = {Engineering Department, University of Cambridge, UK},
  year        = {2001},
  number      = {CUED/F-INFENG/TR.401}
}

@article{DavGreDouRay02,
  author  = {M. Davy and A. Gretton and A. Doucet and P. J. W. Rayner},
  title   = {Optimized Support Vector Machines for Nonstationary Signal
             Classification},
  journal = {IEEE Signal Processing Letters},
  year    = {2002},
  volume  = {9},
  pages   = {442--445},
  number  = {12}
}

@book{DavHin97,
  title     = {Bootstrap Methods and their Application},
  publisher = {Cambridge University Press},
  year      = {1997},
  author    = {A. Davison and D. Hinkley},
  address   = {Cambridge, UK}
}

@article{Davidetal04,
  title   = {Rcv1: A new benchmark collection for text categorization
             research},
  author  = {Lewis, David D and Yang, Yiming and Rose, Tony G and Li,
             Fan},
  journal = jmlr,
  volume  = {5},
  number  = {Apr},
  pages   = {361--397},
  year    = {2004}
}

@inproceedings{davidoff2011learning,
  title        = {Learning patterns of pick-ups and drop-offs to support
                  busy family coordination},
  author       = {Davidoff, Scott and Ziebart, Brian D and Zimmerman, John
                  and Dey, Anind K},
  booktitle    = {SIGCHI Conference on Human Factors in Computing Systems
                  (CHI)},
  pages        = {1175--1184},
  year         = {2011},
  organization = {ACM}
}

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  author    = {William C. Davidon},
  title     = {Variable Metric Method for Minimization},
  publisher = {SIAM},
  year      = {1991},
  journal   = {SIAM Journal on Optimization},
  volume    = {1},
  number    = {1},
  pages     = {1-17},
  doi       = {10.1137/0801001}
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@unpublished{Davies92,
  author = {E.~B. Davies},
  title  = {Spectral theory of {Markov} chains},
  note   = {Course taught at King's College, London},
  year   = {1992}
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@book{Davies_LinearOperators_2007,
  address   = {Cambridge, UK},
  series    = {Cambridge studies in advanced mathematics},
  title     = {Linear operators and their spectra},
  isbn      = {978-0-521-86629-3},
  number    = {106},
  publisher = {Cambridge University Press},
  author    = {Davies, E. B.},
  year      = {2007},
  note      = {OCLC: ocm76935905},
  file      = {Davies - Linear operators and their spectra
               (2007).pdf:/Users/zcranko/Documents/Library/Book/Davies -
               Linear operators and their spectra
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@inproceedings{Davis_RadonNikodymProperty_1974,
  series    = {1},
  title     = {The {Radon}-{Nikodym} {Property} and {Dentable} {Sets} in
               {Banach} {Spaces}},
  volume    = {45},
  abstract  = {In order to prove a Radon-Nikodym theorem for the Bochner
               integral, Rieffel [5] introduced the class of "dentable"
               subsets of Banach spaces. Maynard [31 later introduced the
               strictly larger class of "s-dentable" sets, and extended
               Rieffel's result to show that a Banach space has the
               Radon-Nikodym property if and only if every bounded
               nonempty subset of E is s-dentable. He left open, however,
               the question as to whether, in a space with the
               Radon-Nikodym property, every bounded nonenipty set is
               dentable. In the present note we give an elementary
               construction which shows this question has an affirmative
               answer.},
  language  = {en},
  booktitle = {Proceedings of the {American} {Mathematical} {Society}},
  publisher = {American Mathematical Society},
  author    = {Davis, W J and Phelps, R R},
  year      = {1974},
  keywords  = {⛔ No DOI found},
  pages     = {119--122},
  file      = {Davis, Phelps - The Radon-Nikodym Property and Dentable
               Sets in Banach Spaces
               (1974).pdf:/Users/zcranko/Documents/Library/Conference
               Paper/Davis, Phelps - The Radon-Nikodym Property and
               Dentable Sets in Banach Spaces (1974).pdf:application/pdf}
}

@inproceedings{DavKulJanSra07,
  title        = {Information-theoretic metric learning},
  author       = {Davis, Jason V and Kulis, Brian and Jain, Prateek and Sra,
                  Suvrit and Dhillon, Inderjit S},
  booktitle    = icml,
  pages        = {209--216},
  year         = {2007},
  organization = {ACM}
}

@article{DavMer80,
  author  = {S.~B. Davis and P. Mermelstein},
  title   = {Comparison of Parametric Representations for Monosyllabic
             Word Recognition in Continuously Spoken Sentences},
  journal = {IEEE Transactions on Acoustics, Speech, and Signal
             Processing},
  year    = {1980},
  volume  = {ASSP-28},
  pages   = {357--366},
  number  = {4}
}

@incollection{DavSza03,
  author    = {K. R. Davidson and S. J. Szarek},
  title     = {Local Operator Theory, Random Matrices and {B}anach
               spaces},
  booktitle = {Handbook of the Geometry of {B}anach Spaces},
  publisher = {North-Holland},
  year      = {2003},
  editor    = {J. Lindenstrauss and W. Johnson},
  volume    = {1},
  chapter   = {8},
  pages     = {317-366},
  address   = {Amsterdam}
}

@inproceedings{DavTsu99,
  author    = { {David Guijarro, Jun Tarui} and Tatsuie Tsukiji},
  title     = {Finding Relevant Variables in {PAC} Model with Membership
               Queries},
  booktitle = {Proc. 10th International Conference on Algorithmic
               Learning Theory - ALT '99},
  year      = {1999},
  volume    = {1720},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {313--322},
  publisher = {Springer-Verlag}
}

@article{Dawid92,
  author    = {A. P. Dawid},
  title     = {Prequential Analysis, Stochastic Complexity and Bayesian
               Inference},
  journal   = {Bayesian Statistics},
  year      = {1992},
  volume    = {109},
  pages     = {109--125},
  address   = {Oxford},
  publisher = {Oxford University Press}
}

@article{Dawid92b,
  author  = {Dawid, A. P.},
  title   = {Applications of a general propagation algorithm for
             probabilistic expert systems},
  journal = {Stat. Comput.},
  year    = {1992},
  volume  = {2},
  pages   = {25-36}
}

@article{Dawid_CoherentDispersion_1999,
  title   = {Coherent dispersion criteria for optimal experimental
             design},
  volume  = {27},
  issn    = {0090-5364},
  doi     = {10.1214/aos/1018031101},
  number  = {1},
  journal = {The Annals of Statistics},
  author  = {Dawid, A. Philip and Sebastiani, Paola},
  year    = {1999},
  note    = {tex.fjournal: The Annals of Statistics tex.mrclass: 62K05
             (62C10) tex.mrnumber: 1701101 tex.mrreviewer: Holger
             Dette},
  pages   = {65--81},
  file    = {Dawid, Sebastiani - Coherent dispersion criteria for
             optimal experimental design
             (1999).pdf:/Users/zcranko/Documents/Library/Journal
             Article/Dawid, Sebastiani - Coherent dispersion criteria
             for optimal experimental design
             (1999).pdf:application/pdf}
}

@techreport{Dawid_CoherentMeasures_1998,
  address     = {London, UK},
  title       = {Coherent measures of discrepancy, uncertainty and
                 dependence, with applications to bayesian predictive
                 experimental design},
  abstract    = {We show how, associated with any decision problem, we may
                 derive related functions measuring uncertainty, discrepancy
                 and dependence of distributions. Such
                 {\textbackslash}coherent" functions have special
                 properties, which we characterise, and each functions
                 essentially determines the others. The theory is applied to
                 the Bayesian formulation of the problem of choosing an
                 experiment in order to make a subsequent prediction. It is
                 shown that coherent choice criteria may be based on any of
                 the coherent functions, with related functions yielding
                 identical solutions.},
  language    = {en},
  number      = {139},
  institution = {University College London},
  author      = {Dawid, A. Philip},
  year        = {1998},
  note        = {\# of pages 37},
  file        = {Dawid - Coherent measures of discrepancy, uncertainty and
                 dependence, with applications
                 (1998).pdf:/Users/zcranko/Documents/Library/Report/Dawid -
                 Coherent measures of discrepancy, uncertainty and
                 dependence, with applications (1998).pdf:application/pdf}
}

@article{Dawid_GeometryProper_2007,
  title    = {The geometry of proper scoring rules},
  volume   = {59},
  issn     = {1572-9052},
  doi      = {10.1007/s10463-006-0099-8},
  abstract = {A decision problem is defined in terms of an outcome
              space, an action space and a loss function. Starting from
              these simple ingredients, we can construct: Proper Scoring
              Rule; Entropy Function; Divergence Function; Riemannian
              Metric; and Unbiased Estimating Equation. From an abstract
              viewpoint, the loss function defines a duality between the
              outcome and action spaces, while the correspondence between
              a distribution and its Bayes act induces a self-duality.
              Together these determine a “decision geometry” for the
              family of distributions on outcome space. This allows
              generalisation of many standard statistical concepts and
              properties. In particular we define and study generalised
              exponential families. Several examples are analysed,
              including a general Bregman geometry.},
  number   = {1},
  journal  = {Annals of the Institute of Statistical Mathematics},
  author   = {Dawid, A. Philip},
  month    = mar,
  year     = {2007},
  pages    = {77--93},
  file     = {Dawid - The geometry of proper scoring rules
              (2007).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Dawid - The geometry of proper scoring rules
              (2007).pdf:application/pdf}
}

@article{Dawid_TheoryApplications_2014,
  title    = {Theory and applications of proper scoring rules},
  volume   = {72},
  issn     = {0026-1424, 2281-695X},
  doi      = {10.1007/s40300-014-0039-y},
  abstract = {We give an overview of some uses of proper scoring rules
              in statistical inference, including frequentist estimation
              theory and Bayesian model selection with improper priors.},
  language = {en},
  number   = {2},
  journal  = {METRON},
  author   = {Dawid, A. Philip and Musio, Monica},
  month    = aug,
  year     = {2014},
  pages    = {169--183},
  file     = {Dawid, Musio - Theory and applications of proper scoring
              rules (2014).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Dawid, Musio - Theory and applications of proper
              scoring rules (2014).pdf:application/pdf}
}

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  year      = {2005}
}

@inproceedings{DawLau05b,
  author    = {A. P. Dawid and S. L. Lauritzen},
  booktitle = {2nd International Symposium on Information Geometry and
               its Applications},
  title     = {The Geometry of Decision Theory},
  year      = {2005}
}

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@inproceedings{DecGen95,
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  volume    = {28},
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}

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}

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  number  = {7}
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@inproceedings{Dietterich98b,
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  year      = {1998},
  pages     = {118--126},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@incollection{Dietterich99,
  author    = {T. G. Dietterich},
  title     = {Machine Learning},
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  publisher = {{MIT} Press},
  year      = {1999},
  editor    = {Rob Wilson and Frank Keil}
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@article{DinAn88,
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@phdthesis{Ding13,
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  author = {Ding, N},
  year   = {2013},
  school = {PhD thesis, Purdue University, West Lafayette, Indiana,
            USA}
}

@article{Dinh_ConvexityClosedness_2019,
  title    = {Convexity and closedness in stable robust duality},
  volume   = {13},
  issn     = {1862-4472, 1862-4480},
  doi      = {10.1007/s11590-018-1311-5},
  language = {en},
  number   = {2},
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  author   = {Dinh, N. and Goberna, M. A. and López, M. A. and Volle,
              M.},
  month    = mar,
  year     = {2019},
  pages    = {325--339},
  file     = {Dinh et al - Convexity and closedness in stable robust
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@article{Dinh_FunctionalInequalities_2010,
  title    = {Functional inequalities in the absence of convexity and
              lower semicontinuity with applications to optimization},
  volume   = {20},
  issn     = {1052-6234, 1095-7189},
  doi      = {10.1137/09077552x},
  language = {en},
  number   = {5},
  journal  = {SIAM Journal on Optimization},
  author   = {Dinh, N. and López, M. A. and Volle, M.},
  month    = jan,
  year     = {2010},
  pages    = {2540--2559},
  file     = {Dinh et al - Functional inequalities in the absence of
              convexity and lower semicontinuity
              (2010).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Dinh et al - Functional inequalities in the absence
              of convexity and lower semicontinuity
              (2010).pdf:application/pdf}
}

@inproceedings{DinHe04,
  author    = {Chris Ding and Xiaofeng He},
  title     = {K-means clustering via principal component analysis},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2004}
}

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  author  = {Werner Dinkelbach},
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  number  = {7}
}

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  author    = {C. Ding and T. Li and M. Jordan},
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  booktitle = {LBNL Tech Report 60428},
  year      = {2007}
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@inproceedings{DinLiPenPar06,
  author    = {C. Ding and T. Li and W. Peng and H. Park},
  title     = {Orthogonal Nonnegative Matrix Tri-factorizations for
               Clustering},
  booktitle = {Proceedings International Conference on Knowledge
               Discovery and Data Mining},
  year      = {2006}
}

@inproceedings{DinVis10,
  author    = {Nan Ding and S.~V.~N. Vishwanathan},
  title     = {$t$-logistic regression},
  booktitle = nips23,
  editor    = nips23e,
  year      = 2010
}

@unpublished{DinVis11,
  author = {Nan Ding and S.~V.~N. Vishwanathan},
  title  = {Duality relationship between the log-partition function
            and entropy in $\phi$-exponential family of distributions},
  note   = {Unpublished Manuscript}
}

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  year    = {1995},
  volume  = {5},
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@inproceedings{DoaDomHal01,
  author    = {AnHai Doan and P. Domingos and Alon Y. Halevy},
  title     = {Reconciling Schemas of Disparate Data Sources: A
               Machine-Learning Approach.},
  booktitle = {SIGMOD Conference},
  year      = {2001}
}

@article{DoaHal05,
  author  = {AnHai Doan and Alon~Y. Halvey},
  title   = {Semantic Integration Research in the Database Community},
  journal = {AI Magazine, Special Issue on Semantic Integration},
  year    = {2005}
}

@book{DobBar08,
  author    = {Annette J. Dobson and Adrian Barnett},
  title     = {Introduction to Generalized Linear Models},
  year      = {2008},
  series    = {Texts in Statistical Science},
  edition   = {3},
  publisher = {Chapman \& Hall/CRC}
}

@article{DobDoi03,
  author   = {P. D. Dobson and A. J. Doig},
  title    = {Distinguishing enzyme structures from non-enzymes without
              alignments},
  journal  = {J Mol Biol},
  volume   = {330},
  number   = {4},
  pages    = {771-783},
  month    = {Jul},
  year     = {2003},
  abstract = {The ability to predict protein function from structure is
              becoming increasingly important as the number of structures
              resolved is growing more rapidly than our capacity to study
              function. Current methods for predicting protein function
              are mostly reliant on identifying a similar protein of
              known function. For proteins that are highly dissimilar or
              are only similar to proteins also lacking functional
              annotations, these methods fail. Here, we show that protein
              function can be predicted as enzymatic or not without
              resorting to alignments. We describe 1178 high-resolution
              proteins in a structurally non-redundant subset of the
              Protein Data Bank using simple features such as
              secondary-structure content, amino acid propensities,
              surface properties and ligands. The subset is split into
              two functional groupings, enzymes and non-enzymes. We use
              the support vector machine-learning algorithm to develop
              models that are capable of assigning the protein class.
              Validation of the method shows that the function can be
              predicted to an accuracy of 77\% using 52 features to
              describe each protein. An adaptive search of possible
              subsets of features produces a simplified model based on 36
              features that predicts at an accuracy of 80\%. We compare
              the method to sequence-based methods that also avoid
              calculating alignments and predict a recently released set
              of unrelated proteins. The most useful features for
              distinguishing enzymes from non-enzymes are
              secondary-structure content, amino acid frequencies, number
              of disulphide bonds and size of the largest cleft. This
              method is applicable to any structure as it does not
              require the identification of sequence or structural
              similarity to a protein of known function.},
  keywords = {Algorithms | Aspartic Acid/chemistry | Computational
              Biology | Databases | Enzymes/*chemistry | Genome | Ligands
              | Phenylalanine/chemistry | Protein Conformation | Protein
              Structure, Secondary | Proteome | Research Support,
              Non-U.S. Gov't | Software | 2003/08/07 05:00},
  address  = {Department of Biomolecular Sciences, UMIST, P.O. Box 88,
              Manchester M60 1QD, UK.},
  note     = {}
}

@inproceedings{DobGun95,
  author    = {David P. Dobkin and Dimitrios Gunopulos},
  title     = {Concept learning with geometric hypotheses},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {329--336},
  publisher = {ACM Press, New York, NY}
}

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@inproceedings{DruCor96,
  author    = {H. Drucker and Corinna Cortes},
  title     = {Boosting Decision Trees},
  booktitle = {Advances in Neural Information Processing Systems 8},
  year      = {1996},
  editor    = {D.~S. Touretzky and M.~C. Mozer and M.~E. Hasselmo},
  pages     = {479--485},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{DruCorJacLeCetal94,
  author  = {H. Drucker and Corinna Cortes and L.~D. Jackel and Y.
             LeCun and V. Vapnik},
  title   = {Boosting and Other Ensemble Methods},
  journal = {Neural Computation},
  year    = {1994},
  volume  = {6},
  pages   = {1289--1301},
  number  = {6},
  type    = {Letter}
}

@inproceedings{DruCorJacLeCetal94b,
  author    = {H. Drucker and Corinna Cortes and L.~D. Jackel and Y.
               Le{C}un and V. Vapnik},
  title     = {Boosting and other machine learning algorithms},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1994},
  pages     = {53--61},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{DruHol00,
  author    = {Chris Drummond and Robert C. Holte},
  title     = {Exploiting the Cost of (In)sensitivity of Decision Tree
               Splitting Criteria},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {239--246},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{DruMaC10,
  author    = {Gregory Druck and Andrew McCallum},
  title     = {High-Performance Semi-Supervised Learning using
               Discriminatively Constrained Generative Models},
  booktitle = icml,
  year      = {2010}
}

@inproceedings{DruManMcC08,
  title     = {Learning from labeled features using generalized
               expectation criteria},
  author    = {G. Druck and G.S. Mann and A. McCallum},
  booktitle = {SIGIR},
  publisher = {ACM},
  year      = {2008},
  editor    = {S.-H. Myaeng and D.W. Oard and F. Sebastiani and T.-S.
               Chua and M.-K. Leong},
  pages     = {595--602}
}

@inproceedings{DruManMcC09,
  title     = {Semi-supervised learning of dependency parsers using
               generalized expectation criteria},
  author    = {Gregory Druck and Gideon S. Mann and Andrew McCallum},
  booktitle = {the Joint Conference of the 47th Annual Meeting of the ACL
               and the 4th International Joint Conference on Natural
               Language Processing of the AFNLP},
  year      = {2009}
}

@inproceedings{Drummond09,
  title     = {Replicability is not reproducibility: Nor is it good
               science},
  author    = {Chris Drummond},
  booktitle = {Workshop on Evaluation Methods for Machine Learning},
  year      = {Montreal, Canada, 2009}
}

@inproceedings{DruPalZhuMaC07,
  title     = {Semi-Supervised Classification with Hybrid
               Generative/Discriminative Methods},
  author    = {Gregory Druck and Chris Pal and Xiaojin Zhu and Andrew
               McCallum},
  booktitle = {the 13th {ACM} {SIGKDD} International Conference on
               Knowledge Discovery and Data Mining},
  year      = {2007}
}

@incollection{DruSchSim92,
  author    = {H. Drucker and R. Schapire and P. Simard},
  title     = {Improving Performance in Neural Networks Using a Boosting
               Algorithm},
  booktitle = {Advances in Neural Information Processing Systems 4},
  publisher = {Morgan Kaufmann},
  year      = {1992},
  editor    = {J.~E. Moody and S.~J. Hanson and R. Lippmann}
}

@article{DruSchSim93,
  author  = {H. Drucker and R. Schapire and P. Simard},
  title   = {Boosting performance in neural networks},
  journal = {International Journal of Pattern Recognition and
             Artificial Intelligence},
  year    = {1993},
  volume  = {7},
  pages   = {705--719},
  number  = {4}
}

@article{DruWuVap99,
  author  = {H. Drucker and D. Wu and V.~N. Vapnik},
  title   = {Support Vector Machines for Span Categorization},
  journal = {IEEE Transactions on Neural Networks},
  year    = {1999},
  volume  = {10},
  pages   = {1048--1054},
  number  = {5}
}

@inproceedings{DuaAndStaetal17,
  author    = {Yan Duan and Marcin Andrychowicz and Bradly C. Stadie and
               Jonathan Ho and Jonas Schneider and Ilya Sutskever and
               Pieter Abbeel and Wojciech Zaremba},
  title     = {One-Shot Imitation Learning},
  booktitle = nips,
  year      = {2017}
}

@inproceedings{DuaAndStaHoetal17,
  author    = {Yan Duan and Marcin Andrychowicz and Bradly C. Stadie and
               Jonathan Ho and Jonas Schneider and Ilya Sutskever and
               Pieter Abbeel and Wojciech Zaremba},
  title     = {One-Shot Imitation Learning},
  booktitle = nips,
  year      = {2017}
}

@article{DuaKeePoo03,
  author  = {K. Duan and S.S. Keerthi and A.N. Poo},
  title   = {Evaluation of Simple Performance Measures for Tuning {SVM}
             Hyperparameters},
  journal = {Neurocomputing},
  year    = {2003},
  volume  = {51},
  pages   = {41--59}
}

@article{DuaKenPenRow95,
  author  = {S. Duane and A.~D. Kennedy and B.~J. Pendleton and D.
             Roweth},
  title   = {Hybrid {Monte} {Carlo}},
  journal = {Physics Letters {B}},
  year    = {1995},
  volume  = {195},
  pages   = {216--222}
}

@article{DuaXuTsa12,
  title   = {Learning with augmented features for heterogeneous domain
             adaptation},
  author  = {Duan, Lixin and Xu, Dong and Tsang, Ivor},
  journal = {arXiv preprint arXiv:1206.4660},
  year    = {2012}
}

@article{DubMucMayDraetal99,
  author  = {I. Dubchak and I. Muchnik and C. Mayor and I. Dralyuk and
             S. Kim},
  title   = {Recognition of a Protein Fold in the Context of the {SCOP}
             Classification},
  journal = {Proteins: Structure, Function, and Genetics},
  year    = {1999},
  volume  = {35},
  pages   = {401-407}
}

@inproceedings{DucAgaWai10,
  title     = {Distributed Dual Averaging In Networks},
  author    = {John Duchi and Alekh Agarwal and Martin Wainwright},
  booktitle = {Advances in Neural Information Processing Systems 23},
  year      = {2010}
}

@article{DucGlyNam16,
  author  = {John Duchi and Peter Glynn and Hongseok Namkoong},
  title   = {Statistics of Robust Optimization: A Generalized Empirical
             Likelihood Approach},
  year    = {2016},
  journal = {arXiv:1610.03425}
}

@inproceedings{Duchietal10,
  author    = {John C. Duchi and Shai Shalev-Shwartz and Yoram Singer and
               Ambuj Tewari},
  title     = {Composite Objective Mirror Descent},
  booktitle = colt,
  year      = {2010}
}

@article{Duchon76,
  author  = {J. Duchon},
  title   = {Interpolation des Fonctions de Deux Variables suivant le
             Principle de la Flexion des Plaques Minces},
  journal = {R.A.I.R.O. Anal.\ Numer.},
  year    = {1976},
  volume  = {10},
  pages   = {5--12}
}

@incollection{Duchon77,
  author    = {J. Duchon},
  title     = {Spline minimizing rotation-invariant semi-norms in
               {S}obolev spaces},
  booktitle = {Constructive theory of functions of several variables},
  publisher = {Springer},
  year      = {1977},
  editor    = {W. Schempp and K~Zeller},
  volume    = {571},
  series    = {Lecture Notes in Mathematics},
  address   = {Berlin}
}

@inproceedings{DucShaSigCha08,
  author    = {John Duchi and Shai Shalev-Shwartz and Yoram Singer and
               Tushar Chandrae},
  title     = {Efficient Projections onto the $\ell_1$-Ball for Learning
               in High Dimensions},
  booktitle = icml,
  year      = {2008}
}

@article{DucSin09,
  author  = {John Duchi and Yoram Singer},
  title   = {Online and Batch Learning using Forward-Backward
             Splitting},
  journal = jmlr,
  note    = {Accepted},
  year    = {2009}
}

@article{DudGinZin91,
  author  = {R.~M. Dudley and E. Gine and J. Zinn},
  title   = {Uniform and universal {G}livenko-{C}antelli classes},
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  year    = {1991},
  volume  = {4},
  pages   = {485--510}
}

@book{DudHar73,
  title     = {Pattern Classification and Scene Analysis},
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@book{DudHarSto01,
  title     = {Pattern Classification and Scene Analysis},
  publisher = {John Wiley and Sons},
  year      = {2001},
  author    = {R. O. Duda and Peter E. Hart and David G. Stork},
  address   = {New York},
  note      = {Second edition}
}

@inproceedings{Dudiketal12,
  author    = {Miroslav Dudik and Zaid Harchaoui and Jerome Malick},
  title     = {Lifted Coordinate Descent for Learning with Trace-norm
               Regularizations},
  booktitle = {Conference on Artificial Intelligence and Statistics},
  year      = {2012}
}

@techreport{DudKulRicZei92,
  author      = {R. Dudley and S. Kulkarni and T. Richardson and O.
                 Zeituni},
  title       = {A metric entropy bound is not sufficient for
                 learnability},
  institution = {MIT LIDS},
  year        = {1992},
  number      = {LIDS-P-2144},
  month       = oct
}

@book{Dudley02,
  title     = {Real analysis and probability},
  publisher = {Cambridge University Press},
  year      = {2002},
  author    = {R.~M. Dudley},
  address   = {Cambridge, UK}
}

@article{Dudley78,
  author  = {R. M. Dudley},
  title   = {Central Limit Theorems for Empirical Measures},
  journal = {Annals of Probability},
  year    = {1978},
  volume  = {6},
  pages   = {899--929},
  number  = {6},
  comment = {Generalization and proofs of Vapnik-Chervonenkis
             results.}
}

@article{Dudley79,
  author  = {R.~M. Dudley},
  title   = {{Balls in $R^k$ Do Not Cut All Subsets of $k+2$ Points}},
  journal = {Advances in Mathematics},
  year    = {1979},
  volume  = {31},
  pages   = {306--308}
}

@article{Dudley84,
  author  = {R.~M. Dudley},
  title   = {A course on empirical processes},
  journal = {Lecture Notes in Mathematics},
  year    = {1984},
  volume  = {1097},
  pages   = {2--142}
}

@article{Dudley87,
  author  = {R.~M. Dudley},
  title   = {Universal {Donsker} classes and metric entropy},
  journal = {Annals of Probability},
  year    = {1987},
  volume  = {14},
  pages   = {1306--1326},
  number  = {4}
}

@book{Dudley89,
  title     = {Real analysis and probability},
  publisher = {Wadsworth and Brooks/Cole},
  year      = {1989},
  author    = {R.~M. Dudley},
  series    = {Mathematics Series},
  address   = {Pacific Grove, CA}
}

@unpublished{Dudley91,
  author = {R.~M. Dudley},
  title  = {Comments on two preprints: {Barron (1991), Jones (1991)}},
  note   = {Personal communication},
  month  = mar,
  year   = {1991}
}

@incollection{DudMeu90,
  author    = {E. Dudewicz and E. van der Meulen},
  title     = {The Empiric Entropy: a New Approach to Nonparametric
               Entropy Estimation},
  booktitle = {New Perspectives in Empirical and Applied Statistics},
  publisher = {Wiley},
  year      = {1990},
  pages     = {207-227},
  address   = {New York}
}

@inproceedings{DudPhiSch04,
  author    = {M. Dud\'ik and S. Phillips and R.E. Schapire},
  title     = {Performance Guarantees for Regularized Maximum Entropy
               Density Estimation},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2004},
  publisher = {Springer Verlag}
}

@article{DudPhiSch07,
  author  = {Miroslav Dud\'{\i}k and Steven J. Phillips and Robert E.
             Schapire},
  title   = {Maximum Entropy Density Estimation with Generalized
             Regularization and an Application to Species Distribution
             Modeling},
  journal = jmlr,
  year    = {2007},
  volume  = {8},
  pages   = {1217--1260}
}

@inproceedings{DudSch06,
  author    = {M. Dud\'ik and R. E. Schapire},
  title     = {Maximum Entropy Distribution Estimation with Generalized
               Regularization},
  booktitle = colt,
  year      = {2006},
  editor    = {G\'abor Lugosi and Hans U. Simon},
  month     = {June},
  publisher = {Springer Verlag}
}

@inproceedings{DudSchPhi05,
  author    = {M. Dud\'ik and R.E. Schapire and S.J. Phillips},
  title     = {Correcting sample selection bias in maximum entropy
               density estimation},
  booktitle = nips,
  year      = {2005}
}

@inproceedings{Duetal17,
  author    = {Simon S. Du and Jianshu Chen and Lihong Li and Lin Xiao
               and Dengyong Zhou},
  title     = {Stochastic Variance Reduction Methods for Policy
               Evaluation},
  booktitle = icml,
  year      = {2017}
}

@inproceedings{Duetal19,
  title     = {Gradient Descent Finds Global Minima of Deep Neural
               Networks},
  booktitle = icml,
  author    = {Simon S. Du and Jason D. Lee and Haochuan Li and Liwei
               Wang and Xiyu Zhai},
  year      = {2019}
}

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  author    = {M. O. Duff},
  title     = {{Q}-learning for bandit problems},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {209--217},
  publisher = {Morgan Kaufmann}
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@inproceedings{DufHel00,
  author    = {Nigel Duffy and David Helmbold},
  title     = {Leveraging for Regression},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {208--219},
  publisher = {Morgan Kaufmann, San Francisco}
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@inproceedings{DufHel99,
  author    = {N. Duffy and D. Helmbold},
  title     = {A Geometric Approach to Leveraging Weak Learners},
  booktitle = {the European Conference on Computational Learning Theory},
  year      = {1999},
  volume    = {1572},
  series    = {Lecture Notes in Computer Science},
  pages     = {18--33},
  publisher = {Springer}
}

@techreport{DugDes96,
  author      = {R. Dugad and U.~B. Desai},
  title       = {A Tutorial on Hidden {M}arkov Models},
  institution = {Indian Institute of Technology, Bombay},
  year        = {1996},
  type        = {Tutorial},
  number      = {SPANN-96-1},
  address     = {Bombay, India},
  month       = {May}
}

@article{DuiRidTax98,
  author  = {R.~P.~W. Duin and D.~de Ridder and D.~M.~J. Tax},
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  year    = {1998},
  volume  = {34},
  pages   = {399--404}
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@article{Dumais98,
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  journal = {IEEE Intelligent Systems},
  year    = {1998},
  volume  = {13(4)},
  note    = {In: M.~A. Hearst, B.~{Sch\"olkopf}, S. Dumais, E. Osuna,
             and J. Platt: Trends and Controversies\,---\,Support Vector
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}

@inproceedings{DuMVolCorPreetal99,
  author    = {W. DuMouchel and C. Volinsky and C. Cortes and D. Pregibon
               and T. Johnson},
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  booktitle = {International Conference on Knowledge Discovery and Data
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  address   = {New York, NY, USA},
  series    = {Wiley classics library},
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@book{Dunford_LinearOperators_1988a,
  address   = {New York, NY, USA},
  series    = {Linear operators},
  title     = {Linear operators {I}: general theory},
  isbn      = {978-0-471-60848-6},
  language  = {eng},
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}

@article{Dunham63,
  author  = {B. Dunham},
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@article{Dunn79,
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  year    = {1979},
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@book{DunSch58,
  title     = {Linear operators},
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  author    = {N. Dunford and J. Schwartz},
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@book{DunSch63,
  title     = {Linear Operators Part {II}: Spectral Theory, Self Adjoint
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  publisher = {John Wiley and Sons},
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@inproceedings{DunShaKoi97,
  author    = {N. Dunkin and J. Shawe-Taylor and P. Koiran},
  title     = {A new incremental learning technique},
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               on Neural Nets},
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}

@inproceedings{DupAme00,
  author    = {Pierre Dupont and Juan-Carlos Amengual},
  title     = {Smoothing Probabilistic Automata: {A}n Error-Correcting
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               5th International Colloquium, ICGI 2000, Lisbon, Portugal,
               September 11--13, 2000 ; Proceedings},
  year      = {2000},
  volume    = {1891},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {51--64},
  publisher = {Springer, Berlin}
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@inproceedings{DupCha98,
  author    = {Pierre Dupont and Lin Chase},
  title     = {Using symbol clustering to improve probabilistic automaton
               inference},
  booktitle = {Proc. 4th International Colloquium on Grammatical
               Inference - ICGI '98},
  year      = {1998},
  volume    = {1433},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {232--243},
  publisher = {Springer-Verlag}
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@inproceedings{Dupon94,
  author    = {P. Dupon},
  title     = {Regular grammatical inferencefrom positive and negative
               samples by genetic search: The {GIG} method},
  booktitle = {Proc. 2nd International Colloquium on Grammatical
               Inference - ICGI '94},
  year      = {1994},
  volume    = {862},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {236--245},
  publisher = {Springer-Verlag}
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@inproceedings{Dupont96,
  author    = {Pierre Dupont},
  title     = {Incremental regular inference},
  booktitle = {Grammatical Inference: {L}earning Syntax from Sentences,
               3rd International Colloquium, ICGI-96, Montpellier, France,
               September 25--27, 1996, Proceedings},
  year      = {1996},
  volume    = {1147},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {222--237},
  publisher = {Springer, Berlin}
}

@book{DurEddKroMit98,
  title     = {Biological Sequence Analysis: Probabilistic models of
               proteins and nucleic acids},
  publisher = {Cambridge University Press},
  year      = {1998},
  author    = {R. Durbin and S. Eddy and A. Krogh and G. Mitchison}
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@article{DurMit90,
  author  = {R. Durbin and G. Mitchison},
  title   = {A dimension reduction framework for understanding cortical
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  journal = {Nature},
  year    = {1990},
  volume  = {343},
  pages   = {644--647},
  month   = feb
}

@book{Durrett96,
  title     = {Probability: Theory and Examples},
  publisher = {Duxbury Press},
  year      = {1996},
  author    = {R. Durrett},
  address   = {Belmont, California}
}

@article{DurRum89,
  author  = {R. Durbin and D.~E. Rumelhart},
  title   = {Product units: a computationally powerful and biologically
             plausible extension to backpropagation networks},
  journal = {Neural Computation},
  year    = {1989},
  volume  = {1},
  pages   = {133--142}
}

@inproceedings{DutechEtAl05,
  author    = {A. Dutech and others},
  title     = {Proc of Reinforcement Learning Benchmarks and Bake-offs
               {II}},
  year      = {2005},
  booktitle = {NIPS 2005 Workshops}
}

@inproceedings{DuvMacIpaBom15,
  title     = {Convolutional networks on graphs for learning molecular
               fingerprints},
  author    = {Duvenaud, David K and Maclaurin, Dougal and Iparraguirre,
               Jorge and Bombarell, Rafael and Hirzel, Timothy and
               Aspuru-Guzik, Al{\'a}n and Adams, Ryan P},
  booktitle = nips,
  pages     = {2224--2232},
  year      = {2015}
}

@inproceedings{DuvMacIpaetal15,
  title     = {Convolutional networks on graphs for learning molecular
               fingerprints},
  author    = {Duvenaud, David K and Maclaurin, Dougal and Iparraguirre,
               Jorge and Bombarell, Rafael and Hirzel, Timothy and
               Aspuru-Guzik, Al{\'a}n and Adams, Ryan P},
  booktitle = {Advances in neural information processing systems},
  pages     = {2224--2232},
  year      = {2015}
}

@inproceedings{Dvijothametal18,
  title     = {A Dual Approach to Scalable Verification of Deep
               Networks},
  author    = {Krishnamurthy (Dj)Dvijotham and Robert Stanforth and Sven
               Gowal and Timothy Mann and Pushmeet Kohli},
  booktitle = uai,
  year      = {2018}
}

@article{Dwivedietal20,
  title   = {Benchmarking Graph Neural Networks},
  author  = {Vijay Prakash Dwivedi and Chaitanya K. Joshi and Thomas
             Laurent and Yoshua Bengio and Xavier Bresson},
  journal = {arXiv:2003.00982},
  year    = {2020}
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@inproceedings{DyBro00,
  author    = {Jennifer G. Dy and C.E. Brodley},
  title     = {Feature Subset Selection and Order Identification for
               Unsupervised Learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {247--254},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{DyeChaSmi13,
  title     = {A Simple, Fast, and Effective Reparameterization of IBM
               Model 2},
  author    = {Chris Dyer and Victor Chahuneau and Noah A. Smith},
  booktitle = {HLT-NAACL},
  year      = {2013}
}

@incollection{Dyn87,
  author    = {N. Dyn},
  title     = {Interpolation of scattered data by radial functions},
  booktitle = {Topics in multivariate approximation},
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                analysis},
  isbn       = {978-1-4419-7514-0},
  shorttitle = {Banach space theory},
  abstract   = {Banach spaces provide a framework for linear and nonlinear
                functional analysis, operator theory, abstract analysis,
                probability, optimization and other branches of
                mathematics. This book introduces the reader to linear
                functional analysis and to related parts of
                infinite-dimensional Banach space theory. Key Features: -
                Develops classical theory, including weak topologies,
                locally convex space, Schauder bases and compact operator
                theory - Covers Radon-Nikodým property, finite-dimensional
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                Rosenthal's L₁ theorem, fixed points, and more - Includes
                information about further topics and directions of research
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                Provides numerous exercises for practice. The text is
                suitable for graduate courses or for independent study.
                Prerequisites include basic courses in calculus and linear.
                Researchers in functional analysis will also benefit for
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  institution = {Computer Sciences Department, University of Wisconsin},
  year        = {2000},
  type        = {Data Mining Institute Technical Report},
  number      = {00-05},
  address     = {Madison, Wisconsin},
  annote      = {Submitted to SIOPT},
  key         = {95}
}

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  pages   = {783--804}
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@article{FerMun99,
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}

@article{FerVal01,
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  abstract = {In this work we study input gradient regularization of
              deep neural networks, and demonstrate that such
              regularization leads to generalization proofs and improved
              adversarial robustness. The proof of generalization does
              not overcome the curse of dimensionality, but it is
              independent of the number of layers in the networks. The
              adversarial robustness regularization combines adversarial
              training, which we show to be equivalent to Total Variation
              regularization, with Lipschitz regularization. We
              demonstrate empirically that the regularized models are
              more robust, and that gradient norms of images can be used
              for attack detection.},
  journal  = {arXiv:1808.09540 [cs, math, stat]},
  author   = {Finlay, Chris and Calder, Jeff and Abbasi, Bilal and
              Oberman, Adam},
  month    = sep,
  year     = {2019},
  note     = {arXiv: 1808.09540},
  keywords = {⛔ No DOI found, Computer Science - Machine Learning,
              Statistics - Machine Learning, Mathematics - Numerical
              Analysis}
}

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  address     = {New York}
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  volume   = {10},
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  year     = {1962},
  note     = {tex.fjournal: Mathematica Scandinavica tex.mrclass: 52.30
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  keywords = {⛔ No DOI found},
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}

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@misc{flow,
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@inproceedings{FraWit98b,
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@inproceedings{FraWit99,
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  year    = {1994},
  volume  = {6},
  pages   = {63--72},
  number  = {1},
  note    = {Special issue for AII '92}
}

@article{Freitag00,
  author    = {D. Freitag},
  title     = {Machine Learning for Information Extraction in Informal
               Domains},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {39},
  pages     = {169--202},
  number    = {2/3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Freitag98,
  author    = {D. Freitag},
  title     = {Multistrategy learning for information extraction},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {161--169},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@incollection{Freivalds74,
  author    = {R. Freivalds},
  title     = {On the Complexity and Optimality of Computation in the
               Limit},
  booktitle = {Theory of Algorithms and Programs},
  publisher = {Latvian State University, Riga},
  year      = {1974},
  volume    = {1},
  pages     = {155--173},
  note      = {(in Russian)},
  annote    = {Journal also called {\it Latvii gosudarst. Univ. ucenye
               Zapiski}}
}

@article{Freivalds74b,
  author  = {R. V. Freivalds},
  title   = {On the limit synthesis of numbers of general recursive
             functions in various computable numerations},
  journal = {Soviet Math. Doklady},
  year    = {1974},
  volume  = {15},
  pages   = {1681--1683}
}

@incollection{Freivalds74c,
  author    = {R. Freivalds},
  title     = {Uniform and Non Uniform Predictability},
  booktitle = {Theory of Algorithms and Programs},
  publisher = {Latvian State University, Riga},
  year      = {1974},
  volume    = {1},
  pages     = {89--100}
}

@inproceedings{Freivalds75,
  author    = {R. V. Freivalds},
  title     = {Functions computable in the Limit by Probabilistic
               Machines},
  booktitle = {Proc. 3rd Symposium on Mathematical Foundations of
               Computer Science, June 17--22, 1974},
  year      = {1975},
  volume    = {28},
  series    = {Lecture Notes in Computer Science},
  pages     = {77--87},
  publisher = {Springer-Verlag}
}

@inproceedings{Freivalds75b,
  author    = {R. V. Freivalds},
  title     = {Minimal {G}{\"o}del numbers and their identification in
               the limit},
  booktitle = {Proc. 4th Symposium on Mathematical Foundations of
               Computer Science, September 1--5, 1975},
  year      = {1975},
  volume    = {32},
  series    = {Lecture Notes in Computer Science},
  pages     = {219--225},
  publisher = {Springer-Verlag}
}

@inproceedings{Freivalds79,
  author    = {R. V. Freivalds},
  title     = {Finite identification of general recursive functions by
               probabilistic strategies},
  booktitle = {Fundamentals of Computations Theory, FCT' 79, Proc. of the
               Conference on Algebraic, Arithmetic, and Categorial Methods
               in Computation Theory},
  year      = {1979},
  pages     = {138--145},
  address   = {Berlin},
  publisher = {Akademie-Verlag}
}

@article{Freivalds79b,
  author  = {R. Freivalds},
  title   = {On the Principle Capabilities of Probabilistic Algorithms
             in Inductive Inference},
  journal = {Semiotika Inform},
  year    = {1979},
  volume  = {12},
  pages   = {137--140}
}

@article{Freivalds85,
  author  = {R. Freivalds},
  title   = {Recursiveness of the Enumerating Functions Increases the
             Inferrability of Recursively Enumerable Sets},
  journal = {Bulletin of the European Association for Theoretical
             Computer Science},
  year    = {1985},
  volume  = {27},
  pages   = {35--40}
}

@inproceedings{Freivalds90,
  author    = {R. Freivalds},
  title     = {Inductive inference of minimal programs},
  booktitle = {Proc. 3rd Annu. Workshop on Comput. Learning Theory},
  year      = {1990},
  pages     = {3--20},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{FreIyeSchSin03,
  address              = {Cambridge, MA, USA},
  author               = {Freund, Yoav and Iyer, Raj and Schapire, Robert E. and
                          Singer, Yoram },
  citeulike-article-id = {1736801},
  journal              = {J. Mach. Learn. Res.},
  keywords             = {learning2rank, rankboost},
  pages                = {933--969},
  posted-at            = {2008-10-31 16:37:04},
  priority             = {2},
  publisher            = {MIT Press},
  title                = {An efficient boosting algorithm for combining
                          preferences},
  volume               = {4},
  year                 = {2003}
}

@inproceedings{FreIyeSchSin98,
  author    = {Yoav Freund and Raj Iyer and Robert E. Schapire and Y.
               Singer},
  title     = {An Efficient Boosting Algorithm for Combining
               Preferences},
  booktitle = icml,
  year      = {1998},
  pages     = {170--178},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{FreJai97,
  author  = {Rusins Freivalds and Sanjay Jain},
  title   = {Kolmogorov Numberings and Minimal Identification},
  journal = {Theoret. Comput. Sci.},
  year    = {1997},
  volume  = {188},
  pages   = {79--99},
  number  = {1--2},
  note    = {first in EuroColt '95}
}

@inproceedings{FreKarSmi94,
  author    = {R. Freivalds and M. Karpinski and C. H. Smith},
  title     = {Co-Learning of Total Recursive Functions},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {190--197},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{FreKeaManRonetal95,
  author    = {Yoav Freund and M. Kearns and Yishay Mansour and Dana Ron
               and Ronitt Rubinfeld},
  title     = {Efficient Algorithms for Learning to Play Repeated Games
               Against Computationally Bounded Adversaries},
  booktitle = {Proc. of the 36th Annual Symposium on Foundations of
               Computer Science},
  year      = {1995},
  pages     = {332--341},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@inproceedings{FreKeaRonRubetal93,
  author    = {Y. Freund and M. Kearns and D. Ron and R. Rubinfeld and R.
               Schapire and L. Sellie},
  title     = {Efficient Learning of Typical Finite Automata from Random
               Walks},
  booktitle = {Proc. 25th Annu. ACM Sympos. Theory Comput.},
  year      = {1993},
  pages     = {315--324},
  publisher = {ACM Press, New York, NY}
}

@incollection{FreKin77,
  author    = {R. Freivalds and E. B. Kinber},
  title     = {Limit Identification of Minimal {G\"odel} Numbers},
  booktitle = {Theory of Algorithms and Programs},
  publisher = {Latvian State University, Riga},
  year      = {1977},
  volume    = {3},
  pages     = {3--34}
}

@article{FreKinSmi95,
  author  = {R. Freivalds and E. Kinber and C. Smith},
  title   = {On the Impact of Forgetting on Learning Machines},
  journal = {Journal of the ACM},
  year    = {1995},
  volume  = {42},
  pages   = {1146--1168},
  number  = {6},
  note    = {Earlier version in 6th COLT, 1993}
}

@incollection{FreKinSmi95b,
  author    = {R. Freivalds and E. B. Kinber and C. H. Smith},
  title     = {Probabilistic versus Deterministic Memory Limited
               Learning},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {155--161}
}

@article{FreKinSmi96,
  author  = {R. Freivalds and E. Kinber and C. Smith},
  title   = {On the Intrinsic Complexity of Learning},
  journal = {Inform. Comput.},
  year    = {1996},
  volume  = {123},
  pages   = {64--71},
  number  = {2},
  note    = {Earlier version in EUROCOLT 1995}
}

@article{FreKinSmi99,
  author  = {Rusins Freivalds and Efim Kinber and Carl H. Smith},
  title   = {The functions of finite support: a canonical learning
             problem},
  journal = {J. of Experimental and Theoret. Artif. Intell.},
  year    = {1999},
  volume  = {11},
  pages   = {543--552},
  number  = {4}
}

@article{FreKinWie82,
  author  = {R. Freivalds and E. B. Kinber and R. Wiehagen},
  title   = {Inductive Inference and Computable One-one Numberings},
  journal = {Zeitschr. f. math. Logik und Grundlagen d. Math. Bd.},
  year    = {1982},
  volume  = {28},
  pages   = {463--479}
}

@article{FreKinWie84,
  author  = {R. Freivalds and E. B. Kinber and R. Wiehagen},
  title   = {Connections between Identifying Functionals, Standardizing
             Operations, and Computable Numberings},
  journal = {Zeitschr. f. math. Logik und Grundlagen d. Math.},
  year    = {1984},
  volume  = {30},
  pages   = {145--164}
}

@article{FreKinWie88,
  author  = {R. Freivalds and E. B. Kinber and R. Wiehagen},
  title   = {Probabilistic Versus Deterministic Inductive Inference in
             Nonstandard Numberings},
  journal = {Zeitschr. f. math. Logik und Grundlagen d. Math.},
  year    = {1988},
  volume  = {34},
  pages   = {531--539}
}

@inproceedings{FreKinWie89,
  author    = {R. Freivalds and E. B. Kinber and R. Wiehagen},
  title     = {Inductive Inference From Good Examples},
  booktitle = {Proc. of the Second International Workshop on Analogical
               and Inductive Inference},
  year      = {1989},
  pages     = {1--17}
}

@article{FreKinWie93,
  author  = {R. Freivalds and E. B. Kinber and R. Wiehagen},
  title   = {On the power of inductive inference from good examples},
  journal = {Theoret. Comput. Sci.},
  year    = {1993},
  volume  = {110},
  pages   = {131--144},
  number  = {1}
}

@incollection{FreKinWie95,
  author    = {R. Freivalds and E. B. Kinber and R. Wiehagen},
  title     = {Error detecting in inductive inference},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {25--48}
}

@article{FreKinWie95b,
  author  = {R\={u}si\c{n}\v{s} Freivalds and Efim B. Kinber and Rolf
             Wiehagen},
  title   = {How Inductive Inference Strategies Discover Their Errors},
  journal = {Inform. Comput.},
  year    = {1995},
  volume  = {118},
  pages   = {208--226},
  number  = {2}
}

@incollection{FreKinWie95c,
  author    = {R. Freivalds and E. B. Kinber and R. Wiehagen},
  title     = {Learning from good examples},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {49--62}
}

@inproceedings{FreMac98,
  author    = {Brendan J. Frey and David J. C. MacKay},
  title     = {A Revolution: Belief Propagation in Graphs with Cycles},
  pages     = {479--485},
  editor    = nips10e,
  booktitle = nips10,
  volume    = 10,
  publisher = {MIT Press},
  year      = 1998
}

@inproceedings{FreMan97,
  author    = {Yoav Freund and Yishay Mansour},
  title     = {Learning under persistent drift},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  pages     = {109--118},
  publisher = {Springer-Verlag}
}

@inproceedings{FreMan99,
  author    = {Yoav Freund and Yishay Mansour},
  title     = {Estimating a mixture of two product distributions},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {53--62},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{FreManSch00,
  author    = {Yoav Freund and Yishay Mansour and Robert E. Schapire},
  title     = {Analysis of a Pseudo-{B}ayesian Prediction Method},
  booktitle = {the Conference on Information Science and Systems},
  year      = {2000}
}

@inproceedings{FreMas99,
  author    = {Yoav Freund and Llew Mason},
  title     = {The alternating decision tree learning algorithm,},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {124--133},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@book{Fremlin_MeasureAlgebras_2002,
  address   = {Colchester},
  edition   = {1},
  series    = {Measure theory},
  title     = {Measure algebras},
  isbn      = {978-0-9538129-3-6},
  language  = {en},
  number    = {D. H. Fremlin ; Vol. 3},
  publisher = {Fremlin},
  author    = {Fremlin, David H.},
  year      = {2002},
  note      = {OCLC: 248402938},
  file      = {Fremlin - Measure algebras
               (2002).pdf:/Users/zcranko/Documents/Library/Book/Fremlin -
               Measure algebras (2002).pdf:application/pdf}
}

@article{FreNirGee00,
  author  = {J.~F.~G. de Freitas and M. Niranjan and A.~H. Gee},
  title   = {Hierarchical Bayesian Models for Regularisation in
             Sequential Learning},
  journal = {Neural Computation},
  year    = {2000},
  volume  = {12},
  pages   = {933--953},
  number  = {4}
}

@inproceedings{FreNirGee98,
  author    = {J.~F.~G. de Freitas and M. Niranjan and A.~H. Gee},
  title     = {Regularisation in Sequential Learning Algorithms},
  booktitle = {Advances in Neural Information Processing Systems 10},
  year      = {1998},
  editor    = {M.~I. Jordan and M.~J. Kearns and S.~A. Solla},
  volume    = {10},
  publisher = {{MIT} Press}
}

@inproceedings{FreOpp00,
  author    = {Yoav Freund and Manfred Opper},
  title     = {Continuous Drifting Games},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {126--132},
  publisher = {Morgan Kaufmann, San Francisco}
}

@inproceedings{FreRon95,
  author    = {Yoav Freund and Dana Ron},
  title     = {Learning to model sequences generated by switching
               distributions},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {41--50},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{FreSch95,
  author    = {Y. Freund and R.~E. Schapire},
  title     = {A decision-theoretic generalization of on-line learning
               and an application to boosting},
  booktitle = {the European Conference on Computational Learning Theory},
  year      = {1995},
  pages     = {23--37},
  publisher = {Springer}
}

@inproceedings{FreSch96,
  author    = {Y. Freund and R.~E. Schapire},
  title     = {Experiments with a new Boosting algorithm},
  booktitle = {the International Conference on Machine Learing},
  year      = {1996},
  pages     = {148--146},
  address   = {San Francisco, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{FreSch96b,
  author    = {Yoav Freund and Robert E. Schapire},
  title     = {Game theory, on-line prediction and boosting},
  booktitle = {Proc. 9th Annu. Conf. on Comput. Learning Theory},
  year      = {1996},
  pages     = {325--332},
  publisher = {ACM Press, New York, NY}
}

@article{FreSch97,
  author  = {Y. Freund and R.~E. Schapire},
  title   = {A decision-theoretic generalization of on-line learning
             and an application to boosting},
  journal = {Journal of Computer and System Sciences},
  year    = {1997},
  volume  = {55},
  pages   = {119--139},
  number  = {1},
  month   = aug
}

@inproceedings{FreSch98,
  author    = {Y. Freund and R.~E. Schapire},
  title     = {Large Margin Classification Using the Perceptron
               Algorithm},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  editor    = {J. Shavlik},
  pages     = {209--217},
  address   = {San Francisco, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@article{FreSch99,
  author  = {Y. Freund and R.~E. Schapire},
  title   = {Large Margin Classification using the Perceptron
             Algorithm},
  journal = {Machine Learning},
  year    = {1999},
  volume  = {37},
  pages   = {277--296},
  number  = {3}
}

@article{FreSeuShaTis97,
  author    = {Y. Freund and S. Seung and E. Shamir and N. Tishby},
  title     = {Selective sampling using the query by committee
               algorithm},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {28},
  pages     = {133--168},
  publisher = {Kluwer Academic}
}

@article{FreSla91,
  author    = {Peter W. Frey and David J. Slate},
  title     = {Letter Recognition Using Holland-Style Adaptive
               Classifiers},
  journal   = {Machine Learning},
  year      = {1991},
  volume    = {6},
  pages     = {161--182},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{FreSmi92,
  author    = {Rusins Freivalds and Carl Smith},
  title     = {Memory Limited Inductive Inference Machines},
  booktitle = {Proc. 3rd Scandinavian Workshop on Algorithm Theory, SWAT
               '92},
  year      = {1992},
  volume    = {621},
  series    = {Lecture Notes in Computer Science},
  pages     = {19--29},
  publisher = {Springer-Verlag}
}

@inproceedings{FreSmi93,
  author    = {R. Freivalds and C. H. Smith},
  title     = {On the duality between mechanistic learners and what it is
               they learn},
  booktitle = alt-ws,
  year      = {1993},
  volume    = {744},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {137--149},
  publisher = {Springer-Verlag}
}

@article{FreSmi93b,
  author  = {R. Freivalds and C. H. Smith},
  title   = {On the role of procrastination for machine learning},
  journal = {Inform. Comput.},
  year    = {1993},
  volume  = {107},
  pages   = {237--271},
  number  = {2},
  annote  = {Was in COLT 1992 pages 363--376 and Univ. of MD. TR-91-27
             in 1991}
}

@article{FreSmiVel89,
  author  = {R. Freivalds and C. H. Smith and M. Velauthapillai},
  title   = {Trade-Off Among Parameters Affecting Inductive Inference},
  journal = {Inform. Comput.},
  year    = {1989},
  volume  = {82},
  pages   = {323--349}
}

@article{Freund01,
  author    = {Yoav Freund},
  title     = {An Adaptive Version of the Boost by Majority Algorithm},
  journal   = {Machine Learning},
  year      = {2001},
  volume    = {43},
  pages     = {293--318},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@techreport{Freund09,
  author      = {Yoav Freund},
  title       = {A more robust boosting algorithm},
  institution = {Arxiv},
  year        = 2009,
  number      = {Arxiv/0905.2138},
  month       = {May}
}

@inproceedings{Freund92,
  author    = {Y. Freund},
  title     = {An improved boosting algorithm and its implications on
               learning complexity},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {391--398},
  publisher = {ACM Press, New York, NY}
}

@phdthesis{Freund93,
  author = {Yoav Freund},
  title  = {Data filtering and distribution modeling algorithms for
            machine learning},
  school = {UCSC},
  year   = 1993
}

@article{Freund95,
  author  = {Y. Freund},
  title   = {Boosting a Weak Learning Algorithm by Majority},
  journal = {Inform. Comput.},
  year    = {1995},
  volume  = {121},
  pages   = {256--285},
  number  = {2},
  month   = sep,
  note    = {Also appeared in COLT90}
}

@inproceedings{Freund96,
  author    = {Yoav Freund},
  title     = {Predicting a binary sequence almost as well as the optimal
               biased coin},
  booktitle = {Proc. 9th Annu. Conf. on Comput. Learning Theory},
  year      = {1996},
  pages     = {89--98},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{Freund98,
  author    = {Yoav Freund},
  title     = {Self bounding learning algorithms},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1998},
  pages     = {247--258},
  address   = {Madison, Wisconsin}
}

@inproceedings{Freund99,
  author    = {Y. Freund},
  title     = {An Adaptive Version of the Boost by Majority Algorithm},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1999},
  note      = {To Appear}
}

@article{Freund_DecisiontheoreticGeneralization_1997,
  title    = {A decision-theoretic generalization of on-line learning
              and an application to boosting},
  volume   = {55},
  issn     = {00220000},
  doi      = {10.1006/jcss.1997.1504},
  language = {en},
  number   = {1},
  journal  = {Journal of Computer and System Sciences},
  author   = {Freund, Yoav and Schapire, Robert E},
  month    = aug,
  year     = {1997},
  pages    = {119--139},
  file     = {Freund, Schapire - A decision-theoretic generalization of
              on-line learning and an application to
              (1997).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Freund, Schapire - A decision-theoretic
              generalization of on-line learning and an application to
              (1997).pdf:application/pdf}
}

@unpublished{Freundetal13,
  author = {Robert M. Freund and Paul Grigas and Rahul Mazumder},
  title  = {Adaboost and Forward Stagewise Regression are First-Order
            Convex Optimization Methods},
  note   = {arXiv:1307.1192},
  year   = {2013}
}

@unpublished{FreundGrigas13,
  author = {Robert M. Freund and Paul Grigas},
  title  = {New Analysis and Results for the Conditional Gradient
            Method},
  note   = {preprint},
  year   = {2013}
}

@article{FreundSchapire97,
  author  = {Yoav Freund and Robert E. Schapire},
  title   = {A Decision-Theoretic Generalization of On-line Learning
             and an Application to Boosting},
  journal = {Journal of Computer and System Sciences},
  year    = {1997},
  volume  = {55},
  pages   = {119--139}
}

@inproceedings{FreWanMahLan06,
  author    = {Nando de Freitas and Yang Wang and Maryam Mahdaviani and
               Dustin Lang},
  title     = {Fast {K}rylov Methods for {N}-Body Learning},
  booktitle = {Advances in Neural Information Processing Systems 18},
  year      = {2006},
  editor    = {Y. Weiss and B. {Sch\"olkopf} and J. Platt},
  pages     = {251--258},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{FreWie79,
  author  = {R. V. Freivalds and R. Wiehagen},
  title   = {Inductive inference with additional information},
  journal = {Elektronische Informationsverarbeitung und Kybernetik},
  year    = {1979},
  volume  = {15},
  pages   = {179--185}
}

@inproceedings{FreZeu96,
  author    = {R. Freivalds and T. Zeugmann},
  title     = {Co-Learning of Recursive Languages from Positive Data},
  booktitle = {Proc.\ Perspectives of System Informatics, Second
               International Andrei Ershov Memorial Conference},
  address   = {Akademgorodok, Novosibirsk, Russia},
  month     = {June},
  year      = {1996},
  volume    = {1181},
  series    = {Lecture Notes in Computer Science},
  pages     = {122--133},
  publisher = {Springer},
  note      = {Also TR RIFIS-TR-CS-110, Kyushu University 33, 1995.}
}

@article{Frflink,
  author = {Fast-random-forest},
  title  = {http://code.google.com/p/fast-random-forest/}
}

@article{FriBenFin77,
  author  = {J.~H. Friedman and J.~L. Bentley and R.~A. Finkel},
  title   = {An algorithm for finding best matches in logarithmic
             expected time},
  journal = {{ACM} transaction on mathematical software},
  year    = {1977},
  volume  = {3},
  pages   = {209--226},
  number  = {3}
}

@inproceedings{FriCriCam98,
  author    = {T.-T. Frie{\ss} and N. Cristianini and C. Campbell},
  title     = {The Kernel Adatron Algorithm: {A} Fast and Simple Learning
               Procedure for Support Vector Machines.},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  editor    = {J. Shavlik},
  pages     = {188--196},
  publisher = {Morgan Kaufmann Publishers}
}

@techreport{Frie99,
  author      = {T.\ Frie{\ss}},
  title       = {Support Vector Neural Networks: The Kernel Adatron with
                 Bias and Soft-Margin},
  institution = {The University of Sheffield},
  year        = {1999}
}

@article{Friedman01,
  author  = {Jerome H. Friedman},
  title   = {Greedy Function Approximation: a Gradient Boosting
             Machine},
  journal = {Annals of Statistics},
  year    = 2001,
  volume  = 29,
  number  = 5,
  pages   = {1189--1232}
}

@article{Friedman87,
  author  = {J.~H. Friedman},
  title   = {Exploratory projection pursuit},
  journal = {J. Amer. Statist. Assoc.},
  year    = {1987},
  volume  = {82},
  pages   = {249--266},
  comment = {Reference from PRNN}
}

@article{Friedman89,
  author  = {J.~H. Friedman},
  title   = {Regularized discriminant analysis},
  journal = {Journal of the American Statistical Association},
  year    = {1989},
  volume  = {84},
  pages   = {165--175}
}

@article{Friedman91,
  author  = {J.~H. Friedman},
  title   = {Multivariate Adaptive Regression Splines.},
  journal = {Annals of Statistics},
  year    = {1991},
  volume  = {19},
  pages   = {1--141},
  number  = {1}
}

@techreport{Friedman96,
  author      = {J.~H. Friedman},
  title       = {Another Approach to Polychotomous Classification},
  institution = {Department of Statistics and Stanford Linear Accelerator
                 Center, Stanford University},
  year        = {1996}
}

@inproceedings{Friedman97,
  author    = {Nir Friedman},
  title     = {Learning belief networks in the presence of missing values
               and hidden variables},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {125--133},
  publisher = {Morgan Kaufmann}
}

@techreport{Friedman99,
  author      = {J. Friedman},
  title       = {Greedy function approximation: a gradient boosting
                 machine},
  institution = {Stanford University},
  year        = {1999}
}

@article{Friedmanetal07,
  author  = {J. Friedman and T. Hastie and H. H\"ofling and R.
             Tibshirani},
  title   = {Pathwise Coordinate Optimization},
  journal = {The Annals of Applied Statistics},
  year    = {2007},
  volume  = {1},
  pages   = {302--332},
  number  = {2}
}

@article{FriGeiGol97,
  author    = {Nir Friedman and Dan Geiger and Moises Goldszmidt},
  title     = {Bayesian network classifiers},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {29},
  pages     = {131--163},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{FriGol96,
  author    = {Nir Friedman and Moises Goldszmidt},
  title     = {Discretizing continuous attributes while learning
               {B}ayesian neworks},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {157--165},
  publisher = {Morgan Kaufmann}
}

@inproceedings{FriGolLee98,
  author    = {Nir Friedman and Moises Goldszmidt and T. J. Lee},
  title     = {Bayesian network classification with continuous
               attributes: getting the best of both discretization and
               parametric fitting},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {179--187},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@techreport{FriHar98,
  author      = {T.~T. Frie{\ss} and R.~F. Harrison},
  title       = {Linear Programming Support Vector Machiens for Pattern
                 Classification and Regression Estimation and The Set
                 Reduction Algorithm},
  institution = {University of Sheffield},
  year        = {1998},
  type        = {TR},
  number      = {RR-706},
  address     = {Sheffield, UK}
}

@techreport{FriHasTib98,
  author      = {J. Friedman and T. Hastie and R. Tibshirani},
  title       = {Additive logistic regression: a statistical view of
                 Boosting},
  institution = {Stanford University, Dept.\ of Statistics},
  year        = {1998}
}

@article{FriJer95,
  author    = {A. Frieze and M. Jerrum},
  title     = {An analysis of a Monte Carlo algorithm for estimating the
               permanent},
  journal   = {Combinatorica},
  pages     = {67--83},
  year      = {1995},
  number    = {1},
  volume    = {15},
  publisher = {Akad{\'e}miai Kiad{\'o}},
  address   = {Budapest, Springer International}
}

@article{FriKol03,
  author  = {N. Friedman and D. Koller},
  title   = {Being Bayesian about network structure: a Bayesian
             approach to structure discovery in Bayesian networks},
  journal = {Machine Learning},
  volume  = {50},
  year    = {2003},
  pages   = {95-126}
}

@inproceedings{FriMatAlmMac03,
  author    = {B. Friedrich and I. Matschke and E. Almasri and J. Mack},
  title     = {Data fusion techniques for adaptive traffic signal
               control},
  booktitle = {10th IFAC Symposium on Control in Transportation Systems,
               Tokyo, Japan},
  year      = {2003}
}

@article{FriMunDilBocetal96,
  author    = {H. Friedrich and S. M{\"u}nch and R. Dillman and S.
               Bocionek and M. Sassin},
  title     = {Robot Programming by Demonstration ({RPD}): Supporting the
               Induction by Human Interaction},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {23},
  pages     = {163--189},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{FriRaf79,
  author  = {J. Friedman and L. Rafsky},
  title   = {Multivariate Generalizations of the {W}ald-{W}olfowitz and
             {S}mirnov Two-Sample Tests},
  journal = {The Annals of Statistics},
  year    = {1979},
  volume  = {7},
  pages   = {697--717},
  number  = {4}
}

@article{FriStu81,
  author  = {J.~H. Friedman and W. Stuetzle},
  title   = {Projection Pursuit Regression},
  journal = {Journal of the American Statistical Association},
  year    = {1981},
  volume  = {76},
  pages   = {817--823},
  number  = {376},
  month   = dec,
  comment = {Good description of projection pursuit}
}

@article{FriTuk74,
  author  = {J.~H. Friedman and J.~W. Tukey},
  title   = {A projection pursuit algorithm for exploratory data
             analysis},
  journal = {{IEEE} Trans. Comput.},
  year    = {1974},
  volume  = {C-23},
  pages   = {881--890},
  number  = {9}
}

@inproceedings{Fritzke91,
  author    = {B. Fritzke},
  title     = {Unsupervised clustering with growing cell structures},
  booktitle = {the IEEE International Joint conference on Neural
               Networks},
  year      = {1991},
  volume    = {II},
  pages     = {531--536}
}

@inproceedings{FroCorShlBen13,
  title     = {Devise: A deep visual-semantic embedding model},
  author    = {Frome, Andrea and Corrado, Greg S and Shlens, Jon and
               Bengio, Samy and Dean, Jeff and Mikolov, Tomas and others},
  booktitle = nips,
  pages     = {2121--2129},
  year      = {2013}
}

@inproceedings{Frongillo_ConvexFoundations_2014,
  address = {Canberra, ACT, Australia},
  title   = {Convex foundations for generalized {MaxEnt} models},
  volume  = {1636},
  doi     = {10.1063/1.4903703},
  author  = {Frongillo, Rafael and Reid, Mark D.},
  month   = feb,
  year    = {2014},
  pages   = {11--16},
  file    = {Frongillo, Reid - Convex foundations for generalized
             MaxEnt models
             (2014).pdf:/Users/zcranko/Documents/Library/Conference
             Paper/Frongillo, Reid - Convex foundations for generalized
             MaxEnt models (2014).pdf:application/pdf}
}

@inproceedings{Frongillo_GeneralTruthfulness_2014,
  address   = {Cham, Switzerland},
  title     = {General truthfulness characterizations via convex
               analysis},
  isbn      = {978-3-319-13129-0},
  abstract  = {We present a model of truthful elicitation which
               generalizes and extends mechanisms, scoring rules, and a
               number of related settings that do not quite qualify as one
               or the other. Our main result is a characterization
               theorem, yielding characterizations for all of these
               settings, including a new characterization of scoring rules
               for non-convex sets of distributions. We generalize this
               model to eliciting some property of the agent’s private
               information, and provide the first general characterization
               for this setting. We also show how this yields a new proof
               of a result in mechanism design due to Saks and Yu.},
  booktitle = {Web and {Internet} {Economics}},
  publisher = {Springer International Publishing},
  author    = {Frongillo, Rafael and Kash, Ian},
  editor    = {Liu, Tie-Yan and Qi, Qi and Ye, Yinyu},
  year      = {2014},
  pages     = {354--370},
  file      = {Frongillo, Kash - General truthfulness characterizations
               via convex analysis
               (2014).pdf:/Users/zcranko/Documents/Library/Conference
               Paper/Frongillo, Kash - General truthfulness
               characterizations via convex analysis
               (2014)3.pdf:application/pdf}
}

@article{FroWegSikZel06,
  author  = {Holger Fr\"ohlich and J\"org K Wegner and Florian Siker
             and andreas Zell},
  title   = {Kernel Functions for Attributed Molecular Graphs\,---\,A
             New Similarity Based Approach to {ADME} Prediction in
             Classification and Regression},
  journal = {QSAR and Combinatorial Science},
  year    = {2006},
  volume  = {25},
  number  = {4},
  pages   = {317--326}
}

@book{Fu74,
  title     = {Syntactic Methods in Pattern Recognition},
  publisher = {Academic Press, N. Y.},
  year      = {1974},
  author    = {K. S. Fu}
}

@book{Fu77,
  title     = {Syntactic Pattern Recognition, Applications},
  publisher = {Springer-Verlag},
  year      = {1977},
  author    = {K. S. Fu}
}

@article{Fu83,
  author  = {James C. Fu},
  title   = {On An Upper Bound of Sanov's Inequality for the
             Probability of a Large Deviation},
  journal = {Statistics and Probability Letters},
  year    = {1983},
  volume  = {1},
  pages   = {107--113}
}

@article{Fu98,
  author  = {Fu, Wenjiang J.},
  journal = {J COMPUT GRAPH STAT},
  pages   = {397--416},
  title   = {Penalized Regressions: The Bridge versus the Lasso},
  year    = {1998}
}

@article{Fu98b,
  author  = {Fu, Wenjiang J.},
  journal = {J COMPUT GRAPH STAT},
  pages   = {397--416},
  title   = {Penalized Regressions: The Bridge versus the Lasso},
  year    = {1998}
}

@article{FuBoo75,
  author  = {K. Fu and T. L. Booth},
  title   = {Grammatical Inference: Introduction and Survey - Part 1},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics},
  year    = {1975},
  volume  = {SMC-5},
  pages   = {85--111},
  number  = {1},
  month   = jan,
  comment = {Inference of finite-state and context-free grammars
             reviewed.}
}

@article{FuBoo75b,
  author  = {K. Fu and T. L. Booth},
  title   = {Grammatical Inference: Introduction and Survey - Part 2},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics},
  year    = {1975},
  volume  = {SMC-5},
  pages   = {409--423},
  number  = {4},
  month   = jul,
  comment = {Inference of stochastic finite-state and context-free
             grammars.}
}

@inproceedings{Fuchs95,
  author    = {Matthias Fuchs},
  title     = {Learning proof heuristics by adapting parameters},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {235--243},
  publisher = {Morgan Kaufmann}
}

@inproceedings{FujHar92,
  author    = {K. Fujita and M. Harao},
  title     = {Proving based on similarity},
  booktitle = alt-ws,
  year      = {1992},
  pages     = {213--223},
  publisher = {IOS Press}
}

@book{Fujishige05,
  title     = {Submodular Functions and Optimization},
  author    = {Satoru Fujishige},
  publisher = {Elsevier},
  edition   = {2nd},
  year      = {2005}
}

@techreport{FukBacGre05,
  author      = {K. Fukumizu and F. Bach and A. Gretton},
  title       = { Consistency of Kernel Canonical Correlation Analysis},
  institution = {Institute of Statistical Mathematics},
  year        = {2005},
  number      = {942},
  address     = {Tokyo, Japan}
}

@article{FukBacGre07,
  author  = {K. Fukumizu and F. Bach and A. Gretton},
  title   = {Statistical Consistency of Kernel Canonical Correlation
             Analysis},
  journal = {J. Mach. Learn. Res.},
  year    = {2007},
  volume  = {8},
  pages   = {361--383}
}

@article{FukBacJor04,
  author  = {K. Fukumizu and F. R. Bach and M. I. Jordan},
  title   = {Dimensionality reduction for supervised learning with
             reproducing kernel {H}ilbert spaces},
  journal = jmlr,
  year    = {2004},
  volume  = {5},
  pages   = {73-99}
}

@techreport{FukBacJor06,
  author      = {K. Fukumizu and F. Bach and M. Jordan},
  title       = {Kernel dimension reduction in regression},
  institution = {Department of Statistics, University of California,
                 Berkeley},
  year        = {2006},
  number      = {715}
}

@article{FukBacJor09,
  author  = {Kenji Fukumizu, Francis R. Bach, and Michael I. Jordan},
  title   = {Kernel dimension reduction in regression},
  journal = {Annals of Statistics},
  year    = {2009},
  volume  = {37},
  number  = {4},
  pages   = {1871--1905}
}

@inproceedings{FukGreSunSch08,
  author    = {K. Fukumizu and A. Gretton and X. Sun and B. Sch\"olkopf},
  title     = {Kernel Measures of Conditional Dependence},
  booktitle = {Advances in Neural Information Processing Systems 20},
  year      = {2008},
  pages     = {489--496},
  publisher = {MIT Press},
  address   = {Cambridge, MA}
}

@inproceedings{FukLanSri11,
  title     = {Learning in {Hilbert} vs. {Banach} Spaces: A Measure
               Embedding Viewpoint},
  author    = {Kenji Fukumizu and Gert R. Lanckriet and Bharath K.
               Sriperumbudur},
  booktitle = nips,
  year      = {2011}
}

@inproceedings{FukSonGre10,
  author    = {K. Fukumizu and L. Song and A. Gretton},
  title     = {Kernel Bayes' Law},
  booktitle = {Submitted},
  year      = {2010}
}

@inproceedings{FukSriGreSch09,
  author    = {K. Fukumizu and B. Sriperumbudur and A. Gretton and B.
               Schoelkopf},
  title     = {Characteristic Kernels on Groups and Semigroups},
  booktitle = {Advances in Neural Information Processing Systems 21},
  pages     = {473--480},
  year      = {2009},
  publisher = {Curran Associates Inc.},
  address   = {Red Hook, NY}
}

@inproceedings{Fukumizu99,
  author    = {K. Fukumizu},
  title     = {Generalization Error of Linear Neural Networks in
               Unidentifiable Cases},
  booktitle = alt,
  year      = {1999},
  volume    = {1720},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {51--62},
  publisher = {Springer-Verlag}
}

@book{Fukunaga90,
  title     = {Introduction to Statistical Pattern Recognition},
  publisher = {Academic Press},
  year      = {1990},
  author    = {K. Fukunaga},
  address   = {San Diego},
  edition   = {2nd}
}

@article{FukushimaMine81,
  author  = {Masao Fukushima and Hisashi Mine},
  title   = {A Generalized Proximal Point Algorithm for Certain
             Non-Convex Minimization Problems},
  journal = {International Journal of Systems Science},
  volume  = {12},
  number  = {8},
  pages   = {989--1000},
  year    = {1981}
}

@proceedings{FulCas90,
  title     = {the Third Annu. Workshop on Comput. Learning Theory},
  year      = {1990},
  editor    = {M. Fulk and J. Case},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann, Inc.}
}

@inproceedings{FulJai89,
  author    = {M. A. Fulk and S. Jain},
  title     = {Learning in the Presence of Inaccurate Information},
  booktitle = {Proc. of the Second Annual Workshop on Comput. Learning
               Theory, Santa Cruz, California},
  year      = {1989},
  pages     = {175--188},
  month     = aug,
  publisher = {Morgan Kaufmann, Inc.},
  annote    = {Also University of Rochester TR 279 from 1989}
}

@article{FulJai94,
  author  = {M. A. Fulk and S. Jain},
  title   = {Approximate Inference and Scientific Method},
  journal = {Inform. Comput.},
  year    = {1994},
  volume  = {114},
  pages   = {179--191},
  number  = {2},
  annote  = {Was in {\sl the First International Workshop on
             Algorithmic Learning Theory}, Tokyo, Japan, October 1990.
             Was also Univ of Rochester TR 313 in 1989}
}

@article{FulJaiOsh94,
  author  = {Mark Fulk and Sanjay Jain and Daniel N. Osherson},
  title   = {Open problems in {\em Systems that learn}},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1994},
  volume  = {49},
  pages   = {589--604},
  number  = {3}
}

@phdthesis{Fulk85,
  author = {M. Fulk},
  title  = {A Study of Inductive Inference machines},
  school = {SUNY at Buffalo},
  year   = {1985}
}

@article{Fulk86,
  author  = {M. Fulk},
  title   = {The Disjunctive Learning Problem},
  journal = {20th Asilomar Conference on Signals, Systems and
             Computers},
  year    = {1986},
  pages   = {343--345}
}

@article{Fulk88,
  author  = {M. A. Fulk},
  title   = {Saving the Phenomenon: Requirements that Inductive
             Inference Machines not Contradict Known Data},
  journal = {Inform. Comput.},
  year    = {1988},
  volume  = {79},
  pages   = {193--209},
  number  = {3}
}

@unpublished{Fulk89,
  author = {M. Fulk},
  title  = {Regressiveness},
  note   = {unpublished},
  year   = {1989}
}

@article{Fulk90,
  author  = {M. A. Fulk},
  title   = {Prudence and Other Conditions on Formal Language
             Learning},
  journal = {Inform. Comput.},
  year    = {1990},
  volume  = {85},
  pages   = {1--11},
  number  = {1}
}

@inproceedings{Fulk90b,
  author    = {M. A. Fulk},
  title     = {Robust separations in inductive inference},
  booktitle = {Proc. of the 31st Symposium on the Foundations of Comp.
               Sci.},
  year      = {1990},
  pages     = {405--410},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@inproceedings{FulKasSal95,
  author    = {Truxton Fulton and Simon Kasif and Steven Salzberg},
  title     = {Efficient algorithms for finding multi-way splits for
               decision trees},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {244--251},
  publisher = {Morgan Kaufmann}
}

@article{Funahashi89,
  author  = {K. Funahashi},
  title   = {On the approximate realization of continuous mappings by
             neural networks},
  journal = {Neural Networks},
  year    = {1989},
  volume  = {2},
  pages   = {183--192}
}

@inproceedings{FunGuePasSuk06,
  author    = {S. Funiak and C. Guestrin and M. Paskin and R.
               Sukthankar},
  title     = {Distributed Inference in Dynamical Systems},
  booktitle = {NIPS},
  year      = {2006}
}

@inproceedings{FunMan00,
  author    = {G. Fung and O.~L. Mangasarian},
  title     = {Data Selection for Support Vector Machine Classifiers},
  booktitle = {{KDD}'2000},
  year      = {2000},
  note      = {also: Data Mining Institute Technical Report 00-02,
               University of Wisconsin, Madison}
}

@techreport{FunManSha00,
  author      = {G. Fung and O.L. Mangasarian and J.W. Shavlik},
  title       = {Knowledge-Based Nonlinear Kernel Classifiers},
  institution = {Data Mining Institute},
  year        = {2000},
  booktitle   = {Proc.\ Annual Conf.\ Computational Learning Theory},
  publisher   = {Morgan Kaufmann Publishers}
}

@inproceedings{FunManSha02,
  author    = {G. Fung and O.L. Mangasarian and J.W. Shavlik},
  title     = {Knowledge-based Support Vector Machine Classifiers},
  booktitle = {Advances in Neural Information Processing Systems 15},
  year      = {2002},
  editor    = {S. Becker and S. Thrun and K. Obermayer},
  volume    = {15},
  publisher = {{MIT} Press}
}

@techreport{FunManSmo00,
  author      = {G. Fung and O.~L. Mangasarian and A. J. Smola},
  title       = {Minimal Kernel Classifiers},
  institution = {Data Mining Institute},
  year        = {2000},
  number      = {00-08},
  address     = {University of Wisconsin},
  month       = {November}
}

@article{FunManSmo02,
  author  = {G. Fung and O.~L. Mangasarian and A. J. Smola},
  title   = {Minimal Kernel Classifiers},
  journal = {Journal of Machine Learning Research},
  year    = {2002},
  volume  = {3},
  pages   = {303--321}
}

@article{FurDufCriBedetal00,
  author  = {T.~S. Furey and N. Duffy and N. Cristianini and D.
             Bednarski and M. Schummer and D. Haussler},
  title   = {Support Vector Machine Classification and Validation of
             Cancer Tissue Samples Using Microarray Expression Data},
  journal = {Bioinformatics},
  year    = {2000},
  volume  = {16},
  pages   = {906--914},
  number  = {10}
}

@inproceedings{FurJacSmi91,
  author    = {M. L. Furst and J. C. Jackson and S. W. Smith},
  title     = {Improved Learning of {$AC^0$} Functions},
  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {317--325},
  publisher = {Morgan Kaufmann},
  annote    = {Also CMU TR CMU-CS-90-183}
}

@inproceedings{FurMiy92,
  author    = {S. Furuya and S. Miyano},
  title     = {Analogy is {NP}-hard},
  booktitle = alt-ws,
  year      = {1992},
  pages     = {207--212},
  publisher = {IOS Press}
}

@inproceedings{Furukawa92,
  author    = {K. Furukawa},
  title     = {Preliminary Study on Program Synthesis Based on Induction
               and Verification},
  booktitle = alt-ws,
  year      = {1992},
  pages     = {299--306},
  publisher = {IOS Press}
}

@inproceedings{FurWid94,
  author    = {J. F{\"u}rnkranz and G. Widmer},
  title     = {Incremental Reduced Error Pruning},
  booktitle = icml,
  year      = {1994},
  pages     = {70--77},
  publisher = {Morgan Kaufmann}
}

@inproceedings{FuSonXin09,
  author    = {Wenjie Fu and Le Song and Eric P. Xing},
  title     = {Dynamic Mixed Membership Blockmodel for Evolving
               Networks},
  booktitle = {International Conference on Machine Learning {ICML}},
  year      = 2009
}

@inproceedings{GabDumHor06,
  title     = {Newsjunkie: providing personalized newsfeeds via analysis
               of information novelty},
  author    = {E. Gabrilovich and S. T. Dumais and E. Horvitz},
  booktitle = {Conference on World Wide Web, {WWW}},
  publisher = {ACM},
  year      = {2004},
  editor    = {S. I. Feldman and M. Uretsky and M. Najork and C. E.
               Wills},
  pages     = {482--490},
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  pages    = {151--163},
  file     = {Gammerman, Vovk - Hedging Predictions in Machine Learning
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              Learning (2007).pdf:application/pdf}
}

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               (UAI)},
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               Models},
  journal   = jmlr,
  year      = {2010},
  volume    = {11},
  pages     = {2001−-2049},
  publisher = {{MIT} Press}
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  year      = {2015}
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               Yaoliang Yu},
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               Transfer},
  booktitle = icml,
  year      = {2018}
}

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  author    = {Jean-Gabriel Ganascia and J. T. and Philippe Laublet},
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  title   = {Domain-Adversarial Training of Neural Networks},
  author  = {Yaroslav Ganin and Evgeniya Ustinova and Hana Ajakan and
             Pascal Germain and Hugo Larochelle and François Laviolette
             and Mario Marchand and Victor Lempitsky},
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@inproceedings{GanZhaYuWen16,
  author    = {Vignesh Ganapathiraman and Xinhua Zhang and Yaoliang Yu
               and Junfeng Wen},
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  booktitle = nips,
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@article{GaoKle16,
  title    = {Distributionally robust stochastic optimization with
              wasserstein distance},
  abstract = {Distributionally robust stochastic optimization (DRSO) is
              an approach to optimization under uncertainty in which,
              instead of assuming that there is an underlying probability
              distribution that is known exactly, one hedges against a
              chosen set of distributions. In this paper, we consider
              sets of distributions that are within a chosen Wasserstein
              distance from a nominal distribution. We argue that such a
              choice of sets has two advantages: (1) The resulting
              distributions hedged against are more reasonable than those
              resulting from other popular choices of sets, such as
              \{{\textbackslash}Phi\}-divergence ambiguity set. (2) The
              problem of determining the worst-case expectation has
              desirable tractability properties. We derive a dual
              reformulation of the corresponding DRSO problem and
              construct approximate worst-case distributions (or an exact
              worst-case distribution if it exists) explicitly via the
              first-order optimality conditions of the dual problem. Our
              contributions are five-fold. (i) We identify necessary and
              sufficient conditions for the existence of a worst-case
              distribution, which is naturally related to the growth rate
              of the objective function. (ii) We show that the worst-case
              distributions resulting from an appropriate Wasserstein
              distance have a concise structure and a clear
              interpretation. (iii) Using this structure, we show that
              data-driven DRSO problems can be approximated to any
              accuracy by robust optimization problems, and thereby many
              DRSO problems become tractable by using tools from robust
              optimization. (iv) To the best of our knowledge, our proof
              of strong duality is the first constructive proof for DRSO
              problems, and we show that the constructive proof technique
              is also useful in other contexts. (v) Our strong duality
              result holds in a very general setting, and we show that it
              can be applied to infinite dimensional process control
              problems and worst-case value-at-risk analysis.},
  journal  = {arXiv:1604.02199 [math]},
  author   = {Gao, Rui and Kleywegt, Anton J.},
  month    = jul,
  year     = {2016},
  note     = {arXiv: 1604.02199},
  keywords = {⛔ No DOI found, Mathematics - Optimization and Control,
              90C15, 90C46},
  file     = {Gao, Kleywegt - Distributionally robust stochastic
              optimization with wasserstein distance
              (2016).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Gao, Kleywegt - Distributionally robust stochastic
              optimization with wasserstein distance
              (2016).pdf:application/pdf}
}

@article{GaoKle16,
  title   = {Distributionally robust stochastic optimization with
             {Wasserstein} distance},
  author  = {Gao, Rui and Kleywegt, Anton J},
  journal = {arXiv:1604.02199},
  year    = {2016}
}

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@article{Gardner_OperationsFunctions_2018,
  title   = {Operations between functions},
  volume  = {26},
  issn    = {1019-8385},
  doi     = {20180906135315},
  number  = {4},
  journal = {Communications in Analysis and Geometry},
  author  = {Gardner, Richard J. and Kiderlen, Markus},
  year    = {2018},
  note    = {tex.fjournal: Communications in Analysis and Geometry
             tex.mrclass: 26B25 (39B22 39B62 52A41) tex.mrnumber:
             3853928 tex.mrreviewer: Hossein Mohebi},
  pages   = {787--855},
  file    = {Gardner, Kiderlen - Operations between functions
             (2018).pdf:/Users/zcranko/Documents/Library/Journal
             Article/Gardner, Kiderlen - Operations between functions
             (2018).pdf:application/pdf}
}

@article{Gardner_OperationsSets_2013,
  title    = {Operations between sets in geometry},
  volume   = {15},
  doi      = {10.4171/jems/422},
  abstract = {An investigation is launched into the fundamental
              characteristics of operations on and between sets, with a
              focus on compact convex sets and star sets (compact sets
              star-shaped with respect to the origin) in
              \$n\$-dimensional Euclidean space \${\textbackslash}mathbb
              \{R\}{\textasciicircum}n\$. It is proved that if
              \$n{\textbackslash}ge 2\$, with three trivial exceptions,
              an operation between origin-symmetric compact convex sets
              is continuous in the Hausdorff metric, \$GL(n)\$ covariant,
              and associative if and only if it is \$L\_p\$ addition for
              some \$1{\textbackslash}le p{\textbackslash}le
              {\textbackslash}infty \$. It is also demonstrated that if
              \$n{\textbackslash}ge 2\$, an operation \$*\$ between
              compact convex sets is continuous in the Hausdorff metric,
              \$GL(n)\$ covariant, and has the identity property (i.e.,
              \$K*{\textbackslash}lbrace o{\textbackslash}rbrace
              =K={\textbackslash}lbrace o{\textbackslash}rbrace *K\$ for
              all compact convex sets \$K\$, where \$o\$ denotes the
              origin) if and only if it is Minkowski addition. Some
              analogous results for operations between star sets are
              obtained. Various characterizations are given of operations
              that are projection covariant, meaning that the operation
              can take place before or after projection onto subspaces,
              with the same effect. Several other new lines of
              investigation are followed. A relatively little-known but
              seminal operation called \$M\$-addition is generalized and
              systematically studied for the first time.
              Geometric-analytic formulas that characterize continuous
              and \$GL(n)\$-covariant operations between compact convex
              sets in terms of \$M\$-addition are established. It is
              shown that if \$n{\textbackslash}ge 2\$, an
              \$o\$-symmetrization of compact convex sets (i.e., a map
              from the compact convex sets to the origin-symmetric
              compact convex sets) is continuous in the Hausdorff metric,
              \$GL(n)\$ covariant, and translation invariant if and only
              if it is of the form \${\textbackslash}lambda DK\$ for some
              \${\textbackslash}lambda {\textbackslash}ge 0\$, where
              \$DK=K+(−K)\$ is the difference body of \$K\$. The term
              “polynomial volume” is introduced for the property of
              operations \$∗\$ between compact convex or star sets that
              the volume of \$rK*sL, r,s{\textbackslash}ge 0\$, is a
              polynomial in the variables \$r\$ and \$s\$. It is proved
              that if \$n{\textbackslash}ge 2\$, with three trivial
              exceptions, an operation between origin-symmetric compact
              convex sets is continuous in the Hausdorff metric,
              \$GL(n)\$ covariant, associative, and has polynomial volume if and only if it is Minkowski addition.},
  language = {eng},
  number   = {6},
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               Multiagent Systems},
  year      = {2008}
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  volume    = {17},
  number    = {4},
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@article{Giner_NecessarySufficient_2009,
  title    = {Necessary and sufficient conditions for the interchange
              between infimum and the symbol of integration},
  volume   = {17},
  issn     = {1877-0533},
  doi      = {10.1007/s11228-009-0119-y},
  abstract = {We make a study of various notions of decomposability for
              subsets of measurable functions in relation with the
              interchange results between infimum and integration. For
              this we introduce the notions of serial decomposability and
              of decomposability relatively to an integrand. A
              characterization of closed serially decomposable subsets of
              the Lebesgue spaces Lpis given. The second notion of
              decomposability introduced is characteristic for the
              interchange property studied. Many examples are presented.
              The links are made with R. T. Rockafellar’s
              decomposability, F. Hiai, H. Umegaki’s decomposability,
              G. Bouchitté and M. Valadier’s stability and normal
              decomposability introduced by O. Anza Hafsa and J.-P.
              Mandallena. As applications we obtain exact lower bounds
              for minimization problems of integral functionals on
              normally decomposable spaces (spaces of continuous
              functions for example), and for the minimization of a class
              of functionals of the Calculus of Variations.},
  number   = {4},
  journal  = {Set-Valued and Variational Analysis},
  author   = {Giner, Emmanuel},
  year     = {2009},
  pages    = {321--357},
  file     = {Giner - Necessary and sufficient conditions for the
              interchange between infimum and the
              (2009).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Giner - Necessary and sufficient conditions for the
              interchange between infimum and the
              (2009).pdf:application/pdf}
}

@article{Giner_SubdifferentiationIntegral_2018,
  title    = {Subdifferentiation of integral functionals},
  volume   = {168},
  issn     = {0025-5610, 1436-4646},
  doi      = {10.1007/s10107-017-1204-x},
  language = {en},
  number   = {1-2},
  journal  = {Mathematical Programming},
  author   = {Giner, Emmanuel and Penot, Jean-Paul},
  month    = mar,
  year     = {2018},
  pages    = {401--431},
  file     = {Giner, Penot - Subdifferentiation of integral functionals
              (2018).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Giner, Penot - Subdifferentiation of integral
              functionals (2018).pdf:application/pdf}
}

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  volume    = {2},
  opteditor = {L.~N. Kanal and A. Rosenfeld}
}

@article{GoldfarbMa11,
  author  = {Donald Goldfarb and Shiqian Ma},
  title   = {Convergence of Fixed-Point Continuation Algorithms for
             Matrix Rank Minimization},
  journal = {Foundations of Computational Mathematics},
  year    = {2011},
  volume  = {11},
  pages   = {183--210},
  number  = {2}
}

@phdthesis{Goldman90,
  author = {S. Goldman},
  title  = {Learning Binary Relations, Total Orders, and Read-Once
            Formulas},
  school = {MIT Dept.\ of Electrical Engineering and Computer Science},
  year   = {1990},
  month  = sep,
  note   = {(MIT Laboratory for Computer Science Technical Report
            MIT/LCS/TR-483, July 1990)}
}

@article{Goldman95,
  author    = {Sally A. Goldman},
  title     = {Guest Editor's Introduction},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {18},
  pages     = {127--129},
  publisher = {Kluwer Academic Publishers, Boston}
}

@book{Goldstein86,
  title     = {Classical Mechanics},
  publisher = {Addison-Wesley},
  year      = {1986},
  author    = {H. Goldstein},
  address   = {Reading, MA}
}

@inproceedings{Golea95,
  author    = {Mostefa Golea},
  title     = {Average case analysis of a learning algorithm for
               {$\mu$-DNF} expressions},
  booktitle = {Computational Learning Theory: EuroColt '95},
  year      = {1995},
  pages     = {342--356},
  publisher = {Springer-Verlag}
}

@inbook{Golea97,
  chapter   = {14},
  pages     = {227--243},
  title     = {On learning the neural network architecture: a case
               study},
  publisher = {{MIT} Press},
  year      = {1997},
  author    = {Mostefa Golea},
  volume    = {IV: Making Learning Systems Practical},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@inproceedings{GolGol94,
  author    = {P. W. Goldberg and S. A. Goldman},
  title     = {Learning one-dimensional geometric patterns under
               one-sided random misclassification noise},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {246--255},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{GolGolMat94,
  author    = {P. W. Goldberg and S. A. Goldman and H. D. Mathais},
  title     = {Learning unions of boxes with membership and equivalence
               queries},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {198--207},
  publisher = {ACM Press, New York, NY}
}

@article{GolGolSco96,
  author    = {Paul W. Goldberg and Sally A. Goldman and Stephen D.
               Scott},
  title     = {{PAC} Learning of One-Dimensional Patterns},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {25},
  pages     = {51--70},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{GolHarWil96,
  author    = {Leslie Ann Goldberg and William E. Hart and David Bruce
               Wilson},
  title     = {Analysis of a simple learning algorithm: learning foraging
               thresholds for lizards},
  booktitle = {Proc. 9th Annu. Conf. on Comput. Learning Theory},
  year      = {1996},
  pages     = {2--9},
  publisher = {ACM Press, New York, NY}
}

@article{GolHeaWah79,
  author  = {G. Golub and M. Heath and G. Wahba},
  title   = {Generalized cross validation as a method for choosing a
             good ridge parameter},
  journal = {Technometrics},
  year    = {1979},
  volume  = {21},
  pages   = {215--224}
}

@article{GolHol88,
  author    = {D. E. Goldberg and J. H. Holland},
  title     = {Genetic Algorithms and Machine Learning},
  journal   = {Machine Learning},
  year      = {1988},
  volume    = {3},
  pages     = {95--99},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{GolHoo98,
  author    = {Lev Goldfarb and Jaroslav Hook},
  title     = {Why Classical Models for Pattern Recognition are Not
               Pattern Recognition Models},
  booktitle = {the International Conference on Advances in Pattern
               Recognition},
  year      = {1998}
}

@article{GolJer95,
  author    = {Paul W. Goldberg and Mark R. Jerrum},
  title     = {Bounding the {V}apnik-{C}hervonenkis dimension of concept
               classes parametrized by real numbers},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {18},
  pages     = {131--148},
  number    = {2/3},
  annote    = {Earlier version in sixth COLT, 1993},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{GolKea95,
  author  = {Sally A. Goldman and M. J. Kearns},
  title   = {On the complexity of teaching},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1995},
  volume  = {50},
  pages   = {20--31},
  number  = {1},
  note    = {Earlier version in 4th COLT, 1991}
}

@inproceedings{GolKeaSch90,
  author    = {S. A. Goldman and M. J. Kearns and R. E. Schapire},
  title     = {On the sample complexity of weak learning},
  booktitle = {Proc. 3rd Annu. Workshop on Comput. Learning Theory},
  year      = {1990},
  pages     = {217--231},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann},
  note      = {To appear, {\em Information and Computation}}
}

@article{GolKeaSch93,
  author  = {S. A. Goldman and M. J. Kearns and R. E. Schapire},
  title   = {Exact identification of circuits using fixed points of
             amplification functions},
  journal = {SIAM J. Comput.},
  year    = {1993},
  volume  = {22},
  pages   = {705--726},
  number  = {4},
  month   = aug,
  annote  = {Earlier version in FOCS90}
}

@inproceedings{GolKraStr10,
  title     = {Online Learning of Assignments that Maximize Submodular
               Functions},
  author    = {D. Golovin and A. Krause and M.J. Streeter},
  booktitle = {Neural Information Processing Systems},
  year      = {2010},
  url       = {http://arxiv.org/abs/0908.0772}
}

@inproceedings{GolKwe00,
  author    = {Paul Goldberg and Stephen Kwek},
  title     = {The Precision of Query Points as a Resource for Learning
               Convex Polytopes with Membership Queries},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {225--235},
  publisher = {Morgan Kaufmann, San Francisco}
}

@inproceedings{GolKwe99,
  author    = {Sally A. Goldman and Stephen S. Kwek},
  title     = {On Learning Unions of Pattern Languages and Tree
               Patterns},
  booktitle = alt,
  year      = {1999},
  volume    = {1720},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {347--363},
  publisher = {Springer-Verlag}
}

@article{GolKweSco01,
  author  = {Sally A. Goldman and Stephen S. Kwek and Stephen D.
             Scott},
  title   = {Agnostic Learning of Geometric Patterns},
  journal = {J. of Comput. Syst. Sci.},
  year    = {2001},
  volume  = {62},
  pages   = {123--151},
  number  = {1}
}

@inproceedings{GolKweSco97,
  author    = {Sally A. Goldman and Stephen S. Kwek and Stephen D.
               Scott},
  title     = {Agnostic learning of geometric patterns},
  booktitle = {Proc. 10th Annu. Conf. on Comput. Learning Theory},
  year      = {1997},
  pages     = {325--333},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{GolKweSco97b,
  author    = {Sally A. Goldman and Stephen S. Kwek and Stephen D.
               Scott},
  title     = {Learning From Examples With Unspecified Attribute Values},
  booktitle = {Proc. 10th Annu. Conf. on Comput. Learning Theory},
  year      = {1997},
  pages     = {231--242},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{GolLawSej91,
  author    = {B. A. Golomb and D. T. Lawrence and T. J. Sejnowski},
  title     = {{SEXNET}: A neural network identified sex from human
               faces},
  booktitle = {Advances in Neural Information Processing Systems 3},
  year      = {1991},
  editor    = {R. Lippmann and J. E. Moody and D. S. Touretzky},
  pages     = {572--577},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{GolMar93,
  author    = {M. Golea and M. Marchand},
  title     = {Average case analysis of the clipped {H}ebb rule for
               nonoverlapping perceptron networks},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {151--157},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{GolMar94,
  author    = {M. Golea and M. Marchand},
  title     = {On Learning Simple Deterministic and Probabilistic Neural
               Concepts},
  booktitle = {Computational Learning Theory: EuroColt '93},
  year      = {1994},
  volume    = {New Series Number 53},
  series    = {The Institute of Mathematics and its Applications
               Conference Series},
  pages     = {47--60},
  address   = {Oxford},
  publisher = {Oxford University Press},
  isbn      = {0 19 853492 2}
}

@article{GolMaSch10,
  author  = {D. Goldfarb and S. Ma and K. Scheinberg},
  title   = {Fast Alternating Linearization Methods for Minimizing the
             Sum of Two Convex Functions},
  journal = {Mathematical Programming},
  year    = {to appear}
}

@inproceedings{GolMat00,
  author    = {Dani Goldberg and Maja J. Matari{\'{c}}},
  title     = {Learning Multiple Models for Reward Maximization},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {319--326},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{GolMat92,
  author    = {S. A. Goldman and H. D. Mathias},
  title     = {Learning k-term {DNF} formulas with an incomplete
               membership oracle},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {77--84},
  publisher = {ACM Press, New York, NY}
}

@article{GolMat96,
  author  = {Sally A. Goldman and H. David Mathias},
  title   = {Teaching a smarter learner},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1996},
  volume  = {52},
  pages   = {255--267},
  number  = {2},
  note    = {Earlier version in 6th COLT, 1993}
}

@article{GolMat97,
  author  = {G. Golub and U. von~{M}att},
  title   = {Generalized Cross-validation for Large-Scale Problems},
  journal = {J. Comput.\ Graph.\ Statist.},
  year    = {1997},
  volume  = {6},
  pages   = {1--34}
}

@inproceedings{GolMatSak96,
  author    = {Mostefa Golea and Masahiro Matsuoka and Yasubumi
               Sakakibara},
  title     = {Stochastic simple recurrent neural networks},
  booktitle = {Grammatical Inference: {L}earning Syntax from Sentences,
               3rd International Colloquium, ICGI-96, Montpellier, France,
               September 25--27, 1996, Proceedings},
  year      = {1996},
  volume    = {1147},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {262--273},
  publisher = {Springer, Berlin}
}

@inbook{GolNig94,
  chapter   = {3, Artificial Intelligence and Neural Networks: Steps
               towards Principled Integration},
  title     = {The Unified Learning Paradigm: A Foundation for {AI}},
  publisher = {Academic Press},
  year      = {1994},
  author    = {L. Goldfarb and S. Nigam}
}

@article{GolRan96,
  author  = {S. Gold and A. Rangarajan},
  title   = {A Graduated Assignment Algorithm for Graph Matching},
  pages   = {377--388},
  journal = {IEEE Trans.\ Pattern Analysis and Machine Intelligence},
  volume  = 18,
  number  = 4,
  year    = 1996
}

@incollection{GolRiv88,
  author    = {S. A. Goldman and R. L. Rivest},
  title     = {A Non-Iterative Maximum Entropy Algorithm},
  booktitle = {Uncertainty in Artificial Intelligence},
  publisher = {Elsevier Science B.V. North-Holland},
  year      = {1988},
  volume    = {2},
  pages     = {133--148}
}

@article{GolRivSch93,
  author  = {S. A. Goldman and R. L. Rivest and R. E. Schapire},
  title   = {Learning binary relations and total orders},
  journal = {SIAM J. Comput.},
  year    = {1993},
  volume  = {22},
  pages   = {1006--1034},
  number  = {5},
  month   = oct,
  annote  = {Earlier version in FOCS89}
}

@inproceedings{GolRot08,
  author    = {Dan Goldwasser and Dan Roth},
  title     = {Transliteration as Constrained Optimization},
  booktitle = emnlp,
  year      = 2008
}

@inproceedings{GolRot96,
  author    = {Andrew R. Golding and D. Roth},
  title     = {Applying winnow to context-sensitive spelli ng
               correction},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {182--190},
  publisher = {Morgan Kaufmann}
}

@article{GolRot99,
  author    = {Andrew R. Golding and D. Roth},
  title     = {A {W}innow-Based Approach to Context-Sensitive Spelling
               Correction},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {34},
  pages     = {107--130},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{GolRowHinSal04,
  title     = {Neighbourhood Component Analysis},
  author    = {J. Goldberger and S. Roweis and G. Hinton and R.
               Salakhutdinov},
  booktitle = {Advances in Neural Information Processing Systems 17},
  editors   = {L. K. Saul and Y. Weiss and L. Bottou},
  year      = {2004}
}

@inproceedings{GolRubSud95,
  author    = {Oded Goldreich and Ronitt Rubinfeld and Madhu Sudan},
  title     = {Learning polynomials with queries: the highly noisy case},
  booktitle = {Proc. of the 36th Annual Symposium on Foundations of
               Computer Science},
  year      = {1995},
  pages     = {294--303},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@techreport{GolSch01,
  author      = {D. Goldfarb and K. Scheinberg},
  title       = {A Product-Form {C}holesky Factorization Method for
                 Handling Dense Columns in Interior Point Methods for Linear
                 Programming},
  institution = {IBM Watson Research Center},
  year        = {2001},
  address     = {Yorktown Heights}
}

@inproceedings{GolSch87,
  author    = {M. von~Golitschek and L. Schumaker},
  title     = {Data fitting by penalized least squares},
  booktitle = {Algorithms for Approximation II},
  year      = {1987},
  editor    = {M. Cox and J. Mason and A. Watson}
}

@article{GolSco99,
  author    = {Sally A. Goldman and Stephen D. Scott},
  title     = {A Theoretical and Empirical Study of a Noise-Tolerant
               Algorithm to Learn Geometric Patterns},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {37},
  pages     = {5--49},
  number    = {1},
  comment   = {first in 13th ICML '96},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{GolSlo00,
  author    = {Judy Goldsmith and Robert H. Sloan},
  title     = {More theory revision with queries (extended abstract)},
  booktitle = {the Thirty-Second Annual ACM Symposium on Theory of
               Computing, May 21--23, 2000, Portland, OR, USA},
  year      = {2000},
  pages     = {441--448},
  publisher = {ACM Press}
}

@article{GolSlo94,
  author    = {S. A. Goldman and R. H. Sloan},
  title     = {The Power of Self-Directed Learning},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {14},
  pages     = {271--294},
  number    = {3},
  annote    = {Was WUCS-92-49},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{GolSlo95,
  author  = {S. A. Goldman and R. H. Sloan},
  title   = {Can {PAC} Learning Algorithms Tolerate Random Attribute
             Noise?},
  journal = {Algorithmica},
  year    = {1995},
  volume  = {14},
  pages   = {70--84},
  annote  = {Was Washington Univ Tech report WUCS-91-29, then updated
             in WUCS-92-25}
}

@inproceedings{GolSloSzoTur00,
  author    = {Judy Goldsmith and Robert H. Sloan and B. Sz{\"o}r{\'e}nyi
               and G. Tur{\'a}n},
  title     = {Improved Algorithms for Theory Revision with Queries},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {236--247},
  publisher = {Morgan Kaufmann, San Francisco}
}

@article{GolSloTamHuaetal99,
  author  = {T. R. Golub and D. K. Slonim and P. Tamayo and C. Huard
             and M. Gaasenbeek and J. P. Mesirov and H. Coller and M. L.
             Loh and J. R. Downing and M. A. Caligiuri and C. D.
             Bloomfield and E. S. Lander},
  title   = {Molecular Classification of Cancer: Class Discovery and
             Class Prediction by Gene Expression Monitoring},
  journal = {Science},
  volume  = {286},
  number  = {5439},
  pages   = {531--537},
  month   = {Oct},
  year    = {1999}
}

@book{GolVan96,
  title     = {Matrix Computations},
  publisher = {John Hopkins University Press},
  year      = {1996},
  author    = {Gene H. Golub and Charles F. {Van Loan}},
  address   = {Baltimore, MD},
  edition   = {3rd}
}

@article{GolWar95,
  author    = {Sally A. Goldman and M. K. Warmuth},
  title     = {Learning binary relations using weighted majority voting},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {20},
  pages     = {245--271},
  number    = {3},
  note      = {Earlier version in 6th COLT, 1993},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{GolWilBis98,
  author    = {P. W. Goldberg and C. K. I. Williams and C. M. Bishop},
  title     = {Regression with input-dependent noise: a Gaussian process
               treatment},
  booktitle = {Advances in Neural Information Processing Systems 10},
  year      = {1998},
  pages     = {493-499},
  publisher = {{MIT} Press},
  location  = {Denver, CO}
}

@inproceedings{GolZho00,
  author    = {Sally Goldman and Yan Zhou},
  title     = {Enhancing Supervised Learning with Unlabeled Data},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {327--334},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{GolZhuRecetal10,
  author    = {Andrew B. Goldberg and Xiaojin Zhu and Benjamin Recht and
               Jun-Ming Xu and Robert Nowak},
  title     = {Transduction with Matrix Completion: Three Birds with One
               Stone},
  booktitle = nips23,
  year      = 2010
}

@article{GomMooKap07,
  author  = {Vicen\cc G\'omez and Joris M. Mooij and Hilbert J. Kappen},
  title   = {Truncating the Loop Series Expansion for Belief
             Propagation},
  pages   = {1987--2016},
  journal = jmlr,
  volume  = 8,
  month   = sep,
  year    = 2007
}

@inbook{GonBaeSni92,
  chapter   = {5, New indices for text: PAT Trees and PAT arrays},
  pages     = {66--82},
  title     = {Information Retrieval: Data Structures and Algorithms},
  publisher = {Prentice-Hall, Inc.},
  year      = {1992},
  author    = {Gaston H. Gonnet and Ricardo A. Baeza-Yates and Tim
               Snider},
  address   = {Upper Saddle River, NJ, USA},
  isbn      = {0-13-463837-9}
}

@article{GonEdwTho76,
  author  = {R. C. Gonzalez and J. J. Edwards and M. G. Thomason},
  title   = {An algorithm for the inference of tree grammars},
  journal = {Internat. J. Comput. Info. Sci.},
  year    = {1976},
  volume  = {5},
  pages   = {145--164},
  number  = {2}
}

@inproceedings{GonLowGue09,
  title     = {Residual Splash for Optimally Parallelizing Belief
               Propagation},
  author    = {J. Gonzalez and Y. Low and C. Guestrin},
  booktitle = {In Artificial Intelligence and Statistics (AISTATS)},
  year      = 2009,
  address   = {Clearwater Beach, Florida}
}

@inproceedings{GonLowGueOha09,
  author    = {Joseph Gonzalez and Yucheng Low and Carlos Guestrin and
               David O'Hallaron },
  title     = {Distributed Parallel Inference on Large Factor Graphs},
  booktitle = {Uncertainty in Artificial Intelligence},
  year      = {2009}
}

@book{GonTho78,
  title     = {Syntactic Pattern Recognition, An Introduction},
  publisher = {Addison-Wesley, Reading, Mass.},
  year      = {1978},
  author    = {R. C. Gonzalez and M. G. Thomason}
}

@article{GonWahJohTri98,
  author  = {J. Gong and G. Wahba and D. Johnson and J. Tribbia},
  title   = {Adaptive tuning of numerical weather prediction models:
             simultaneous estimation of weighting, smoothing and
             physical parameters},
  journal = {Monthly Weather Review},
  year    = {1998},
  volume  = {125},
  pages   = {210--231}
}

@incollection{Good_RationalDecisions_1992,
  address   = {New York, NY, USA},
  title     = {Rational decisions},
  isbn      = {978-1-4612-0919-5},
  abstract  = {This paper deals first with the relationship between the
               theory of probability and the theory of rational behaviour.
               A method is then suggested for encouraging people to make
               accurate probability estimates, a connection with the
               theory of information being mentioned. Finally Wald’s
               theory of statistical decision functions is summarised and
               generalised and its relation to the theory of rational
               behaviour is discussed.},
  booktitle = {Breakthroughs in {Statistics}: {Foundations} and {Basic}
               {Theory}},
  publisher = {Springer},
  author    = {Good, I. J.},
  editor    = {Kotz, Samuel and Johnson, Norman L.},
  year      = {1992},
  doi       = {10.1007/978-1-4612-0919-5_24},
  keywords  = {\#nosource},
  pages     = {365--377}
}

@article{Goodfellow_ExplainingHarnessing_2015,
  title    = {Explaining and harnessing adversarial examples},
  journal  = {International Conference on Learning Representations},
  author   = {Goodfellow, Ian and Shlens, Jonathon and Szegedy,
              Christian},
  year     = {2015},
  keywords = {⛔ No DOI found},
  file     = {Goodfellow et al - Explaining and harnessing adversarial
              examples
              (2015).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Goodfellow et al - Explaining and harnessing
              adversarial examples (2015).pdf:application/pdf}
}

@inproceedings{Goodfellow_GenerativeAdversarial_2014,
  title     = {Generative adversarial nets},
  booktitle = {Advances in neural information processing systems 27},
  publisher = {Curran Associates, Inc.},
  author    = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi
               and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and
               Courville, Aaron and Bengio, Yoshua},
  editor    = {Ghahramani, Z. and Welling, M. and Cortes, C. and
               Lawrence, N. D. and Weinberger, K. Q.},
  year      = {2014},
  keywords  = {⛔ No DOI found},
  pages     = {2672--2680},
  file      = {Goodfellow et al - Generative adversarial nets
               (2014).pdf:/Users/zcranko/Documents/Library/Conference
               Paper/Goodfellow et al - Generative adversarial nets
               (2014).pdf:application/pdf}
}

@inproceedings{Goodfellowetal14,
  author    = {Ian Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and
               Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron
               Courville and Yoshua Bengio},
  title     = {Generative Adversarial Nets},
  booktitle = nips,
  year      = {2014}
}

@inproceedings{GooMirCouetal13,
  title     = {Multi-Prediction Deep Boltzmann Machines},
  author    = {Goodfellow, Ian and Mirza, Mehdi and Courville, Aaron and
               Bengio, Yoshua},
  booktitle = nips,
  year      = {2013}
}

@book{GooMitSam94,
  title           = {The {\LaTeX} Companion},
  publisher       = {Ad{\-d}i{\-s}on-Wes{\-l}ey},
  year            = {1994},
  author          = {M. Goossens and F. Mittelbach and A. Samarin},
  address         = {Reading, MA, USA},
  edition         = {Second},
  acknowledgement = {ack-nhfb},
  bibdate         = {Wed Nov 16 12:41:07 1994},
  isbn            = {0-201-54199-8},
  lccn            = {Z253.4.L38 G66 1994},
  pages           = {xxi + 530},
  price           = {US\$34.25}
}

@article{GooShlSze15,
  title   = {Explaining and harnessing adversarial examples},
  author  = {Goodfellow, Ian J and Shlens, Jonathon and Szegedy,
             Christian},
  journal = {arXiv:1412.6572},
  year    = {2015}
}

@inproceedings{GooShlSze15a,
  title     = {Explaining and harnessing adversarial examples},
  booktitle = iclr,
  author    = {Goodfellow, Ian J and Shlens, Jonathon and Szegedy,
               Christian},
  year      = {2015}
}

@article{Gordes95,
  author    = {Diana F. Gordon and Marie desJardins},
  title     = {Evaluation and selection of biases in machine learning},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {20},
  pages     = {5--22},
  number    = {1/2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Gordon95,
  author    = {Geoffrey J. Gordon},
  title     = {Stable function approximation in dynamic programming},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {261--268},
  publisher = {Morgan Kaufmann}
}

@inproceedings{Gordon98,
  author    = {Diana F. Gordon},
  title     = {Well-behaved {B}orgs, {B}olos, and {B}erserkers},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {224--232},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{Gordon99,
  author    = {Geoffrey J. Gordon},
  title     = {Regret bounds for prediction problems},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {29--40},
  publisher = {ACM Press, New York, NY}
}

@article{GorKonSch87,
  author  = {Y. Gordon and H. K\"onig and C. Sch\"utt},
  title   = {Geometric and Probabilistic Estimates for Entropy and
             Approximation Numbers of Operators},
  journal = {Journal of Approximation Theory},
  year    = {1987},
  volume  = {49},
  pages   = {219--239}
}

@inproceedings{GorMoo00,
  author    = {Geoffrey J. Gordon and Andrew Moore},
  title     = {Learning Filaments},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {335--342},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{GorPfeKla07,
  author  = {J. Gorski and F. Pfeuffer and K. Klamroth},
  title   = {Biconvex Sets and Optimization with Biconvex Functions --
             Survey and Extensions},
  journal = {Mathematical Methods of Operations Research},
  pages   = {373--408},
  year    = {2007},
  volume  = {6}
}

@inproceedings{GorPic94,
  author    = {M. Gorkani and R.~W. Picard},
  title     = {Texture Orientation for Sorting Photos ``at a Glance''},
  booktitle = {Proc.\ Intl.\ Conf.\ Pat.\ Rec.},
  year      = {1994},
  volume    = {1},
  pages     = {459--464},
  address   = {Jerusalem}
}

@inproceedings{GorSeg96,
  author    = {Geoffrey J. Gordon and Alberto Maria Segre},
  title     = {Nonparametric statistical methods for experimental
               evaluations of speedup learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {200--206},
  publisher = {Morgan Kaufmann}
}

@article{GorSej88,
  author  = {R.~P. Gorman and T.~J. Sejnowsky},
  title   = {Analysis of hidden units in a layered network trained to
             classify sonar targets},
  journal = {Neural Networks},
  year    = {1988},
  volume  = {1}
}

@article{GorSho85,
  author  = {J. Gordon and E. H. Shortliffe},
  title   = {A Method for Managing Evidential Reasoning in a
             Hierarchical Hypothesis Space},
  journal = {Artificial Intelligence},
  year    = {1985},
  volume  = {26},
  pages   = {323--357},
  number  = {3},
  month   = jul,
  comment = {Dempster-Shafer theory explained and generalized.}
}

@article{GorTes92,
  author   = {Marco Gori and Alberto Tesi},
  title    = {On the Problem of Local Minima in Backpropagation},
  journal  = {IEEE Transaction on Pattern Analysis and Machine
              Intelligence},
  year     = {1992},
  volume   = {14},
  pages    = {76--86},
  number   = {1},
  abstract = {Supervised Learning in Multi-Layered Neural Networks
              (MLNs) has been recently proposed through the well-known
              Backpropagation algorithm. This is a gradient method which
              can get stuck in local minima, as simple examples can show.
              In this paper, some conditions on the network architecture
              and the learning environment are proposed which ensure the
              convergence of the Backpropagation algorithm. It is proven
              in particular that the convergence holds if the classes are
              linearly-separable. In this case, the experience gained in
              several experiments shows that $MLNs$ exceed perceptrons in
              generalization to new examples.},
  keywords = {Multi-Layered Networks, learning environment,
              Backpropagation, pattern recognition, linearly-separable
              classes}
}

@article{Gosselin86,
  author  = {R.~P. Gosselin},
  title   = {Nonlinear approximation using rapidly increasing
             functions},
  journal = {Constr.\ Approx.},
  year    = {1986},
  volume  = {2},
  pages   = {253--261}
}

@inproceedings{GotHasKra15,
  author    = {Alkis Gotovos and Hamed Hassani and Andreas Krause},
  title     = {Sampling from Probabilistic Submodular Models},
  booktitle = nips,
  year      = 2015
}

@article{GouFerCheetal16,
  title   = {On Differentiating Parameterized Argmin and Argmax
             Problems with Application to Bi-level Optimization},
  author  = {Stephen Gould and Basura Fernando and Anoop Cherian and
             Peter Anderson and Rodrigo Santa Cruz and Edison Guo},
  journal = {arXiv:1607.05447},
  year    = {2016}
}

@article{GouFraPfaetal18,
  title   = {Regularisation of neural networks by enforcing {Lipschitz}
             continuity},
  author  = {Gouk, Henry and Frank, Eibe and Pfahringer, Bernhard and
             Cree, Michael},
  journal = {arXiv:1804.04368},
  year    = {2018}
}

@inproceedings{Gouk_MaxGainRegularisation_2019,
  address   = {Cham, Switzerland},
  title     = {{MaxGain}: regularisation of neural networks by
               constraining activation magnitudes},
  isbn      = {978-3-030-10925-7},
  abstract  = {Effective regularisation of neural networks is essential
               to combat overfitting due to the large number of parameters
               involved. We present an empirical analogue to the Lipschitz
               constant of a feed-forward neural network, which we refer
               to as the maximum gain. We hypothesise that constraining
               the gain of a network will have a regularising effect,
               similar to how constraining the Lipschitz constant of a
               network has been shown to improve generalisation. A simple
               algorithm is provided that involves rescaling the weight
               matrix of each layer after each parameter update. We
               conduct a series of studies on common benchmark datasets,
               and also a novel dataset that we introduce to enable easier
               significance testing for experiments using convolutional
               networks. Performance on these datasets compares favourably
               with other common regularisation techniques. Data related
               to this paper is available at:
               https://www.cs.waikato.ac.nz/{\textasciitilde}ml/sins10/.},
  booktitle = {Machine {Learning} and {Knowledge} {Discovery} in
               {Databases}},
  publisher = {Springer International Publishing},
  author    = {Gouk, Henry and Pfahringer, Bernhard and Frank, Eibe and
               Cree, Michael J.},
  year      = {2019},
  pages     = {541--556}
}

@article{Gouk_RegularisationNeural_2018,
  title    = {Regularisation of neural networks by enforcing lipschitz
              continuity},
  abstract = {We investigate the effect of explicitly enforcing the
              Lipschitz continuity of neural networks with respect to
              their inputs. To this end, we provide a simple technique
              for computing an upper bound to the Lipschitz constant of a
              feed forward neural network composed of commonly used layer
              types and demonstrate inaccuracies in previous work on this
              topic. Our technique is then used to formulate training a
              neural network with a bounded Lipschitz constant as a
              constrained optimisation problem that can be solved using
              projected stochastic gradient methods. Our evaluation study
              shows that, in isolation, our method performs comparatively
              to state-of-the-art regularisation techniques. Moreover,
              when combined with existing approaches to regularising
              neural networks the performance gains are cumulative. We
              also provide evidence that the hyperparameters are
              intuitive to tune and demonstrate how the choice of norm
              for computing the Lipschitz constant impacts the resulting
              model.},
  journal  = {arXiv:1804.04368 [cs, stat]},
  author   = {Gouk, Henry and Frank, Eibe and Pfahringer, Bernhard and
              Cree, Michael J.},
  month    = sep,
  year     = {2018},
  note     = {arXiv: 1804.04368},
  keywords = {⛔ No DOI found, Computer Science - Machine Learning,
              Statistics - Machine Learning}
}

@book{Govindarajulu75,
  title     = {Sequential Statistical Procedures},
  publisher = {Academic Press},
  year      = {1975},
  author    = {Z. Govindarajulu}
}

@article{Gowers_TwoCultures_2000,
  title    = {The two cultures of mathematics},
  journal  = {Mathematics: frontiers and perspectives},
  author   = {Gowers, William Timothy},
  year     = {2000},
  note     = {tex.mrclass: 00A30 (05Dxx) tex.mrnumber: 1754768
              tex.mrreviewer: Alain Plagne},
  keywords = {⛔ No DOI found},
  pages    = {65--78},
  file     = {Gowers - The two cultures of mathematics
              (2000).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Gowers - The two cultures of mathematics
              (2000).pdf:application/pdf}
}

@inproceedings{GraBen04,
  author    = {Yves Grandvalet and Yoshua Bengio},
  title     = {Semi-supervised Learning by Entropy Minimization},
  booktitle = nips17,
  year      = 2004
}

@inproceedings{GraBojGupetal18,
  title     = {Learning Word Vectors for 157 Languages},
  author    = {Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar
               and Joulin, Armand and Mikolov, Tomas},
  booktitle = {Proceedings of the International Conference on Language
               Resources and Evaluation (LREC 2018)},
  year      = {2018}
}

@inproceedings{Grabowski86,
  author    = {J. Grabowski},
  title     = {Inductive Inference of Functions From Noised
               Observations},
  booktitle = {Proc. 1st International Workshop on Analogical and
               Inductive Inference},
  year      = {1986},
  volume    = {265},
  series    = {Lecture Notes in Computer Science},
  pages     = {55--60},
  publisher = {Springer-Verlag}
}

@article{GraBroLee04,
  author  = {Graas, E.~L. and Brown, E.~A. and Lee, Robert H.},
  title   = {An FPGA-Based Approach to High-Speed Simulation of
             Conductance-Based Neuron Models},
  pages   = {417--436},
  journal = {Neuroinformatics},
  volume  = 2,
  number  = 4,
  year    = 2004
}

@article{GraBurObe98,
  author  = {Thore Graepel and Matthias Burger and Klaus Obermayer},
  title   = {Self-Organizing Maps: Generalizations and New Optimization
             Techniques},
  journal = {Neurocomputing},
  year    = {1998},
  pages   = {173--190},
  voulme  = {21}
}

@inproceedings{GraCosBotDouetal04,
  author    = {H.P. Graf and E. Cosatto and L. Bottou and I. Dourdanovic
               and V. Vapnik},
  title     = {Parallel Support Vector Machines: The Cascade SVM},
  booktitle = {NIPS},
  year      = {2004}
}

@inproceedings{GraCosBotetal04,
  author    = {Hans Peter Graf and Eric Cosatto and Leon Bottou and Igor
               Durdanovic and Vladimir Vapnik},
  title     = {Parallel Support Vector Machines: The Cascade {SVM}},
  booktitle = nips,
  year      = 2004
}

@inproceedings{GraGanTas07,
  title     = {Expectation Maximization and Posterior Constraints},
  author    = {J. Gra{\c c}a and K. Ganchev and B. Taskar},
  publisher = mitpr,
  year      = {2007},
  booktitle = {NIPS},
  editor    = {J. C. Platt and D. Koller and Y. Singer and S. T. Roweis},
  url       = {http://books.nips.cc/papers/files/nips20/NIPS2007_0918.pdf}
}

@inproceedings{GraGouKruHer01,
  author    = {Thore Graepel and Mike Goutrie and Marco Kr{\"u}ger and
               Ralf Herbrich},
  title     = {Learning on Graphs in the Game of {G}o},
  booktitle = {the International Joint Conference on Neural Networks},
  year      = {2001}
}

@article{Graham66,
  author  = {R. L. Graham},
  title   = {Bounds for Certain Multiprocessing Anomalies},
  journal = {Bell System Technical Journal (BSTJ)},
  volume  = {45},
  pages   = {1563--1581},
  year    = {1966}
}

@article{Graham80,
  author  = {R. Graham},
  title   = {Solution of {Fokker Plank} equations with and without
             manifest detailed balance},
  journal = {Zeitschrift fur Physik B\,---\,Condensed Matter},
  year    = {1980},
  volume  = {40},
  pages   = {149--155}
}

@inproceedings{Graham99,
  author    = {F.~C. Graham},
  title     = {Logarithmic {S}obolev Techniques For Random Walks On
               Graphs},
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  year      = {1999},
  editor    = {D.~A. Hejhal and J. Friedman and M.~C. Gutzwiller and
               A.~M. Odlyzko},
  number    = {109},
  series    = {IMA Volumes in Mathematics and its Applications},
  pages     = {175--186},
  publisher = {Springer},
  isbn      = {0-387-98824-6}
}

@inproceedings{GraHer01,
  author    = {Thore Graepel and Ralf Herbrich},
  title     = {The Kernel {G}ibbs Sampler},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {514--520},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{GraHer04,
  author    = {T. Graepel and R. Herbrich},
  title     = {Invariant Pattern Recognition by Semidefinite Programming
               Machines},
  booktitle = {Advances in Neural Information Processing Systems 16},
  year      = {2004},
  editor    = {S. Thrun and L. Saul and B. Sch\"{o}lkopf},
  publisher = {{MIT} Press}
}

@inproceedings{GraHerBolObe99,
  author    = {T. Graepel and R. Herbrich and P. Bollmann-Sdorra and K.
               Obermayer},
  title     = {Classification on Pairwise Proximity Data},
  booktitle = {Advances in Neural Information Processing Systems 11},
  year      = {1999},
  editor    = {M.~S. Kearns and S.~A. Solla and D.~A. Cohn},
  pages     = {438--444},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{GraHerHugMiaetal94,
  author    = {L. Grate and M. Herbster and R. Hughey and I.~S. Mian and
               H. Noller and D. Haussler},
  title     = {{RNA} Modeling Using {G}ibbs Sampling and Stochastic
               Context Free Grammars},
  booktitle = {Proc.\ 2nd Intl.\ Conf.\ Intelligent Systems for Molecular
               Biology},
  year      = {1994},
  pages     = {138--146},
  address   = {Menlo Park, CA},
  month     = aug,
  publisher = {AAAI/MIT Press}
}

@inproceedings{GraHerObe00,
  author    = {Thore Graepel and Ralf Herbrich and Klaus Obermayer},
  title     = {{B}ayesian Transduction},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A. Solla and T.~K. Leen and K.-R. M\"uller},
  pages     = {456--462},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{GraHerObe99,
  author    = {Thore Graepel and Ralf Herbrich and Klaus Obermayer},
  title     = {Bayesian transductive classification by maximizing volume
               in version space},
  booktitle = {Learning 1999 Conference},
  year      = {1999}
}

@inproceedings{GraHerSchSmoetal99,
  author    = {T. Graepel and R. Herbrich and B. Sch\"olkopf and A. J.
               Smola and P. Bartlett and K. M\"uller and K. Obermayer and
               R.~C. Williamson},
  title     = {Classification on proximity data with {LP}-machines},
  booktitle = {Ninth International Conference on Artificial Neural
               Networks},
  year      = {1999},
  series    = {Conference Publications No.~470},
  pages     = {304--309},
  address   = {London},
  publisher = {IEE}
}

@inproceedings{GraHerSha00,
  author    = {T. Graepel and R. Herbrich and J. Shawe-Taylor},
  title     = {Generalisation error bounds for sparse linear
               classifiers},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2000},
  pages     = {298--303}
}

@inproceedings{GraHerWil01,
  author    = {Thore Graepel and Ralf Herbrich and R.~C. Williamson},
  title     = {From Margin to Sparsity},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {210--216},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{GraMarBen06,
  author    = {Yves Grandvalet and Johnny Mariéthoz and Samy Bengio},
  title     = {A probabilistic interpretation of {SVMs} with an
               application to unbalanced classification},
  booktitle = nips18,
  pages     = {467--474},
  year      = 2006,
  editor    = nips18e,
  address   = {Cambride, MA},
  publisher = mitpr
}

@inproceedings{GraNanSch12,
  author    = {Grabocka, Josif and Nanopoulos, Alexandros and
               Schmidt-Thieme, Lars},
  editor    = {Flach, Peter A. and De Bie, Tijl and Cristianini, Nello},
  title     = {Invariant Time-Series Classification},
  booktitle = {Machine Learning and Knowledge Discovery in Databases},
  year      = {2012},
  pages     = {725--740}
}

@inproceedings{GraObe98,
  author    = {T. Graepel and K. Obermayer},
  title     = {Fuzzy Topographic Kernel Clustering},
  booktitle = {the 5th GI Workshop Fuzzy Neuro Systems '98},
  year      = {1998},
  editor    = {W. Brauer},
  pages     = {90--97}
}

@article{GraObe99,
  author  = {Thore Graepel and Klaus Obermayer},
  title   = {A Stochastic Self-Organizing Map for Proximity Data},
  journal = {Neural Computation},
  year    = {1999},
  volume  = {11},
  pages   = {139--155},
  address = {Technical University Berlin}
}

@unpublished{graphkerneldata,
  note = {\url{https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets}}
}

@article{GraPro83,
  author  = {P. Grassberger and I. Procaccia},
  title   = {Measuring the strangeness of strange attractors},
  journal = {Physica D},
  year    = {1983},
  volume  = {9},
  pages   = {189--208}
}

@article{GraRav19,
  title   = {Equivariant Entity-Relationship Networks},
  author  = {Graham, Devon and Ravanbakhsh, Siamak},
  journal = {arXiv:1903.09033},
  year    = {2019}
}

@book{GraRyz81,
  title     = {Table of Integrals, Series, and Products},
  publisher = {Academic Press},
  year      = {1981},
  author    = {I.~S. Gradshteyn and I.~M. Ryzhik},
  address   = {New York}
}

@inproceedings{GraSatTsi89,
  author    = {H. P. Graf and S. Satyanarayana and Y. Tsividis},
  title     = {A Reconfigurable Analog ({VLSI} Neural Network Chip},
  booktitle = {Proc. 1989 Neural Information Processing Conference},
  year      = {1989},
  pages     = {758--768}
}

@article{GraSch14,
  author  = {Grabocka, Josif and Schmidt-Thieme, Lars},
  title   = {Invariant time-series factorization},
  journal = {Data Mining and Knowledge Discovery},
  year    = {2014},
  volume  = {28},
  number  = {5},
  pages   = {1455--1479}
}

@article{GraSmoBor03,
  author  = {A.~B. Graf and A. J. Smola and S. Borer},
  title   = {Classification in a Normalized Feature Space using Support
             Vector Machines},
  journal = {{IEEE} Transactions on Neural Networks},
  year    = {2003},
  volume  = {14},
  pages   = {597--605},
  number  = {3}
}

@article{Grassberger84,
  author  = {P. Grassberger},
  title   = {Do climatic attractors exist?},
  journal = {Nature},
  year    = {1984},
  volume  = {323},
  pages   = {609--612}
}

@article{Grasselli10,
  author  = {Grasselli, M. R.},
  title   = {Dual connections in nonparametric classical information
             geometry},
  journal = {Annals of the Institute of Statistical Mathematics},
  year    = {2010},
  volume  = {62},
  number  = {5},
  pages   = {873--896}
}

@inproceedings{GreAlt04,
  author    = {M. Gregory and Y. Altun},
  title     = {Using Conditional Random Fields to Predict Pitch Accents
               in Conversational Speech},
  booktitle = {submitted to ACL'04},
  year      = {2004}
}

@article{GreBelMurSchetal06,
  author  = {A. Gretton and A. Belitski and Y. Murayama and B.
             Sch{\"o}lkopf and N. K. Logothetis},
  title   = {The effect of artifacts on dependence measurement in
             fMRI},
  journal = {Magnetic Resonance Imaging},
  year    = {2006},
  volume  = {24},
  number  = {4},
  pages   = {401--409}
}

@article{GreBorRasSchetal07,
  author  = {A. Gretton and K. Borgwardt and M. Rasch and B.
             SchÃ¶lkopf and A. Smola},
  title   = {A Kernel Approach to Comparing Distributions},
  journal = {the 22nd Conference on Artificial Intelligence (AAAI-07)},
  year    = {2007},
  pages   = {1637--1641}
}

@techreport{GreBorRasSchetal07b,
  author      = {A. Gretton and K. Borgwardt and M. Rasch and B.
                 Sch\"olkopf and A. Smola},
  title       = {A Kernel Method for the Two Sample Problem},
  institution = {MPI for Biological Cybernetics},
  year        = {2008},
  number      = {157}
}

@inproceedings{GreBorRasSchetal07c,
  author    = {A. Gretton and K. Borgwardt and M. Rasch and B.
               Sch{\"o}lkopf and A. Smola},
  title     = {A Kernel Method for the Two-Sample-Problem},
  booktitle = {Advances in Neural Information Processing Systems 19},
  year      = {2007},
  address   = {Cambridge, MA},
  pages     = {513--520},
  publisher = {MIT Press}
}

@inproceedings{GreBorRasSchetal07d,
  author    = {A. Gretton and K. Borgwardt and M. Rasch and B.
               Sch{\"o}lkopf and A. J. Smola},
  title     = {A Kernel Method for the Two-Sample-Problem},
  booktitle = {Advances in Neural Information Processing Systems 19},
  editor    = {B. {Sch\"olkopf} and J. Platt and T. Hofmann},
  publisher = {The {MIT} Press, Cambridge, MA},
  volume    = {19},
  year      = {2007}
}

@article{GreBorRasSchetal09,
  author  = {A. Gretton and K. Borgwardt and M. Rasch and B.
             Sch\"olkopf and A. Smola},
  title   = {Kernel Statistical Tests of Homogeneity},
  journal = {J. Mach. Learn. Res.},
  year    = {2009},
  note    = {Accepted with minor revisions}
}

@article{GreBorRasSchetal12,
  author    = {Arthur Gretton and Karsten M. Borgwardt and Malte J. Rasch
               and Bernhard Schoelkopf and Alexander Smola},
  title     = {A Kernel Two-Sample Test},
  journal   = jmlr,
  year      = {2012},
  volume    = {13},
  pages     = {723--773},
  publisher = {{MIT} Press}
}

@inproceedings{GreBouSmoSch05,
  author    = {A. Gretton and O. Bousquet and A.J. Smola and B.
               Sch\"olkopf},
  title     = {Measuring Statistical Dependence with {H}ilbert-{S}chmidt
               Norms},
  booktitle = {ALT},
  year      = {2005},
  pages     = {63--77},
  editor    = {S. Jain and H. U. Simon and E. Tomita},
  publisher = {Springer-Verlag}
}

@inproceedings{GreBouSmoSch05b,
  author    = {A. Gretton and O. Bousquet and A. Smola and B.
               Sch\"{o}lkopf},
  title     = {Measuring Statistical Dependence with {H}ilbert-{S}chmidt
               Norms},
  booktitle = {Algorithmic Learning Theory: 16th International
               Conference},
  year      = {2005},
  pages     = {63--78}
}

@book{GreChoKee91,
  title     = {Hands: {A} pattern theoretic study of biological shapes},
  publisher = {Springer},
  year      = {1991},
  author    = {U. Grenander and Y. Chow and D.~M. Keenan}
}

@inproceedings{GreDavDouRay01,
  author    = {A. Gretton and M. Davy and A. Doucet and P. J. W. Rayner},
  title     = {Nonstationary Signal Classification using Support Vector
               Machines},
  booktitle = {IEEE Workshop on Statistical Signal Processing
               Proceedings},
  year      = {2001},
  pages     = {305--308},
  publisher = {IEEE Signal Processing Society}
}

@inproceedings{GreDouHerRayetal01,
  author    = {A. Gretton and A. Doucet and R. Herbrich and P. Rayner and
               B. Sch\"olkopf},
  title     = {Support Vector Regression for Black-Box System
               Identification},
  booktitle = {the 11th IEEE Workshop on Statistical Signal Processing},
  year      = {2001}
}

@inproceedings{GreDviRub02,
  author    = {H. Greenspan and G. Dvir and Y. Rubner},
  title     = {Context-based Image Modeling},
  booktitle = {ICPR 2002},
  year      = {2002}
}

@book{Greenacre84,
  title     = {Theory and Applications of Correspondence Analysis},
  publisher = {Academic Press},
  year      = {1984},
  author    = {M. Greenacre},
  address   = {London}
}

@book{Greene70,
  title     = {Isometric Embeddings of Riemannian and Pseudo-Riemannian
               Manifolds},
  publisher = {American Mathematical Society},
  year      = {1970},
  author    = {R.~E. Greene}
}

@article{Grefenstette88,
  author    = {John J. Grefenstette},
  title     = {Credit Assignment in Rule Discovery Systems Based on
               Genetic Algorithms},
  journal   = {Machine Learning},
  year      = {1988},
  volume    = {3},
  pages     = {225--245},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Grefenstette93,
  author    = {J. Grefenstette},
  title     = {Genetic algorithms and machine learning},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {3--4},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{GreFukHarSri09,
  author    = {A. Gretton and K. Fukumizu and Z. Harchaoui and B.
               Sriperumbudur},
  title     = {A Fast, Consistent Kernel Two-Sample Test},
  year      = {2009},
  booktitle = {Advances in Neural Information Processing Systems 22},
  publisher = {Curran Associates Inc.},
  address   = {Red Hook, NY}
}

@techreport{GreFukTeoetal08b,
  author      = {A. Gretton and K. Fukumizu and C.-H. Teo and L. Song and
                 B. Sch{\"o}lkopf and A. Smola},
  title       = {A Kernel Statistical Test of Independence},
  institution = {MPI for Biological Cybernetics},
  year        = {2008},
  number      = {168}
}

@inproceedings{GreFukTeoSonetal08,
  author    = {A. Gretton and K. Fukumizu and C.-H. Teo and L. Song and
               B. Sch{\"o}lkopf and A. Smola},
  title     = {A Kernel Statistical Test of Independence},
  booktitle = nips,
  year      = {2008},
  pages     = {585--592},
  publisher = {MIT Press},
  address   = {Cambridge, MA}
}

@article{Gregor94,
  author  = {J. Gregor},
  title   = {Data-driven inductive inference of finite-state automata},
  journal = {International Journal of Pattern Recognition and
             Artificial Intelligence},
  year    = {1994},
  volume  = {8},
  pages   = {305--322},
  number  = {1}
}

@article{Gregory_UpperSemicontinuity_1980,
  title    = {Upper semi-continuity of subdifferential mappings},
  volume   = {23},
  issn     = {0008-4395},
  doi      = {10.4153/cmb-1980-002-9},
  abstract = {Characterizations of the upper semi-continuity of the
              subdifferential mapping of a continuous convex function are
              given.},
  number   = {1},
  journal  = {Canadian Mathematical Bulletin},
  author   = {Gregory, David A.},
  year     = {1980},
  pages    = {11--19},
  file     = {Gregory - Upper semi-continuity of subdifferential
              mappings
              (1980).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Gregory - Upper semi-continuity of subdifferential
              mappings (1980).pdf:application/pdf}
}

@inproceedings{GreGroKog96,
  author    = {Russell Greiner and Adam J. Grove and Alexander Kogan},
  title     = {Exploiting the omission of irrelevant data},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {216--224},
  publisher = {Morgan Kaufmann}
}

@article{GreGroKog97,
  author    = {Russell Greiner and Adam J. Grove and Alexander Kogan},
  title     = {Knowing what doesn't matter: exploiting the omission of
               irrelevant data},
  journal   = {Artificial Intelligence},
  year      = {1997},
  volume    = {97},
  pages     = {345--380},
  number    = {1--2},
  publisher = {Elsevier Science}
}

@inproceedings{GreGroRot96,
  author    = {Russell Greiner and Adam J. Grove and D. Roth},
  title     = {Learning active classifiers},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {207--215},
  publisher = {Morgan Kaufmann}
}

@inproceedings{GreGyo08,
  author    = {A. Gretton and L. Gy{\"o}rfi},
  title     = {Nonparametric Independence Tests: Space Partitioning and
               Kernel Approaches},
  booktitle = {Algorithmic Learning Theory: 19th International
               Conference},
  pages     = {183--198},
  year      = {2008},
  publisher = {Springer}
}

@techreport{GreHerChaSchetal01,
  author      = {A. Gretton and R. Herbrich and O. Chapelle and B.
                 Sch\"olkopf and P. J. W. Rayner},
  title       = {Estimating the Leave-One-Out Error for Classification
                 Learning with {SVMs}},
  institution = {Cambridge University Engineering Department},
  year        = {2001},
  number      = {CUED/F-INFENG/TR.424}
}

@misc{GreHerSchRay01,
  author = {Arthur Gretton and Ralf Herbrich and Bernhard
            Sch{\"o}lkopf and Peter J.~W. Rayner},
  title  = {Bound on the Leave-One-Out Error for $\nu$-{SVMs}},
  year   = {2001},
  note   = {submitted to NIPS*2001}
}

@inproceedings{GreHerSmo03,
  author    = {A. Gretton and R. Herbrich and A. J. Smola},
  title     = {The Kernel Mutual Information},
  booktitle = {{ICASSP}},
  year      = {2003}
}

@article{GreHerSmoBouetal05,
  author  = {A. Gretton and R. Herbrich and A. Smola and O. Bousquet
             and B. Sch{\"o}lkopf},
  title   = {Kernel Methods for Measuring Independence},
  journal = {J. Mach. Learn. Res.},
  year    = {2005},
  volume  = {6},
  pages   = {2075--2129}
}

@inproceedings{Greiner92,
  author    = {Russell Greiner},
  title     = {Probabilistic Hill-Climbing: Theory and Applications},
  booktitle = {Proc. of the Ninth Canadian Conference on Artificial
               Intelligence},
  year      = {1992},
  pages     = {60--67},
  address   = {Vancouver},
  month     = jun,
  publisher = {Morgan Kaufmann},
  note      = {(Awarded ``AIJ Best Paper Prize'')}
}

@inproceedings{Greiner95,
  author    = {Russell Greiner},
  title     = {The challenge of revising an impure theory},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {269--277},
  publisher = {Morgan Kaufmann}
}

@inproceedings{Greiner95b,
  author    = {Russell Greiner},
  title     = {The Complexity of Theory Revision},
  booktitle = {Proc. of IJCAI-95},
  year      = {1995},
  addres    = {Montreal}
}

@article{Greiner96,
  author    = {Russell Greiner},
  title     = {{PALO}: {A} probabilistic hill-climbing algorithm},
  journal   = {Artificial Intelligence},
  year      = {1996},
  volume    = {84},
  pages     = {177--208},
  number    = {1--2},
  month     = jul,
  publisher = {Elsevier Science}
}

@article{GreIsu96,
  author  = {Russell Greiner and Ramana Isukapalli},
  title   = {Learning to Select Useful Landmarks},
  journal = {IEEE Transactions on Systems, Man and Cybernetics, Part
             B},
  year    = {1996},
  volume  = {26},
  number  = {3},
  month   = jun,
  note    = {Special issue on Learning Approaches to Autonomous Robots
             Control (ed., Marco Dorigo). Prior version in AAAI-94}
}

@inproceedings{GreJur92,
  author    = {Russell Greiner and Igor Juri\v{s}ica},
  title     = {A Statistical Approach to Solving the {EBL} Utility
               Problem},
  booktitle = {Proc. AAAI-92},
  year      = {1992},
  address   = {San Jose}
}

@article{Grenander51,
  author  = {U. Grenander},
  title   = {On empirical spectral analysis of empirical processes},
  journal = {Ark.\ Matemat.},
  year    = {1951},
  volume  = {1},
  pages   = {503--531}
}

@book{Grenander81,
  title     = {Abstract Inference},
  publisher = {John Wiley and Sons, Inc.},
  year      = {1981},
  author    = {U. Grenander}
}

@inproceedings{GreOrp91,
  author    = {Russell Greiner and Pekka Orponen},
  title     = {Probably Approximately Optimal Derivation Strategies},
  booktitle = {Proc. of the Second International Joint Conference on
               Knowledge Representation},
  year      = {1991},
  editor    = {J. A. Allen and R. Fikes and E. Sandewall},
  address   = {Rochester, NJ},
  month     = apr,
  publisher = {Morgan Kaufmann}
}

@article{GreOrp96,
  author  = {Russell Greiner and Pekka Orponen},
  title   = {Probably Approximately Optimal Satisficing Strategies},
  journal = {Artificial Intelligence},
  year    = {1996},
  volume  = {82},
  pages   = {21--44},
  number  = {1--2},
  month   = apr
}

@article{GrePorSeh86,
  author  = {D. M. Greig and B. T. Porteous and A. H. Seheult},
  title   = {Discussion on \emph{On the statistical analysis of dirty
             pictures (by 9. E. Besag)}},
  pages   = {282-284},
  journal = {Journal of the Royal Statistical Society B},
  volume  = {48},
  year    = {1986}
}

@article{GrePorSeh89,
  author  = {D. M. Greig and B. T. Porteous and A. H. Seheult},
  title   = {Exact Maximum A Posteriori Estimation for Binary Images},
  pages   = {271--279},
  journal = {Journal of the Royal Statistical Society B},
  volume  = {51},
  number  = {2},
  year    = {1989}
}

@article{GreRamSch90,
  author    = {John J. Grefenstette and Connie Loggia Ramsey and Alan C.
               Schultz},
  title     = {Learning Sequential Decision Rules Using Simulation Models
               and Competition},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  pages     = {355--381},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{GreSch92,
  author    = {Russell Greiner and Dale Schuurmans},
  title     = {Learning useful {H}orn approximations},
  booktitle = {Proc. of the Third International Joint Conference on
               Knowledge Representation},
  year      = {1992},
  address   = {Cambridge, MA},
  month     = oct
}

@incollection{GreSch94,
  author    = {R. Greiner and D. Schuurmans},
  title     = {Learning an Optimally Accurate Representation System},
  booktitle = {Foundations of Knowledge Representation and Reasoning},
  publisher = {Springer-Verlag},
  year      = {1994},
  editor    = {G. Lakemeyer and B. Nebel},
  volume    = {810},
  pages     = {145--59},
  month     = jun,
  note      = {Extended from article in {\em ECAI Workshop on Theoretical
               Foundations of Knowledge Representation and Reasoning},
               Vienna, August 1992.}
}

@book{GreSil94,
  title     = {Nonparametric Regression and Generalized Linear Models: a
               Roughness Penalty Approach},
  publisher = {Chapman and Hall},
  year      = {1994},
  author    = {P. Green and B. Silverman},
  address   = {New York}
}

@article{GreSilBecGru88,
  author    = {R. Greiner and B. Silver and S. Becker and M.
               Gr{\"u}ninger},
  title     = {A Review of Machine Learning at {AAAI}-87},
  journal   = {Machine Learning},
  year      = {1988},
  volume    = {3},
  pages     = {79--92},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{GreSmi93,
  author    = {David Perry Greene and Stephen F. Smith},
  title     = {Competition-Based Induction of Decision Models from
               Examples},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {13},
  pages     = {229--257},
  publisher = {Kluwer Academic Publishers, Boston}
}

@techreport{GreSmoBouHer04,
  author      = {A. Gretton and A. Smola and O. Bousquet and R. Herbrich},
  title       = {Behaviour and Convergence of the Constrained Covariance},
  institution = {{MPI} for Biological Cybernetics},
  year        = {2004},
  number      = {130}
}

@inproceedings{GreSmoBouHeretal05,
  author    = {A. Gretton and A. Smola and O. Bousquet and R. Herbrich
               and A. Belitski and M. Augath and Y. Murayama and J. Pauls
               and B. Sch\"olkopf and N. Logothetis},
  editor    = {Robert G. Cowell and Zoubin Ghahramani},
  title     = {Kernel Constrained Covariance for Dependence Measurement},
  booktitle = {the Tenth International Workshop on Artificial
               Intelligence and Statistics},
  publisher = {Society for Artificial Intelligence and Statistics},
  address   = {New Jersey},
  pages     = {112--119},
  year      = {2005}
}

@inproceedings{GreSmoBouHeretal05b,
  author    = {A. Gretton and A. Smola and O. Bousquet and R. Herbrich
               and A. Belitski and M. Augath and Y. Murayama and J. Pauls
               and B. Sch\"olkopf and N. Logothetis},
  title     = {Kernel Constrained Covariance for Dependence Measurement},
  booktitle = {AISTATS 10},
  pages     = {112--119},
  year      = {2005}
}

@inproceedings{GreSmoHuaSchetal08,
  author    = {A. Gretton and A. Smola and J. Huang and M. Schmittfull
               and K. Borgwardt and B. Sch{\"o}lkopf},
  title     = {Dataset Shift in Machine Learning},
  booktitle = {Covariate Shift and Local Learning by Distribution
               Matching},
  publisher = {MIT Press},
  year      = {2008},
  address   = {Cambridge, MA},
  editor    = {J. Qui{\~n}onero-Candela and M. Sugiyama and A.
               Schwaighofer and N. Lawrence},
  pages     = {131--160}
}

@inproceedings{GreTho96,
  author    = {J. Gregor and M. G. Thomason},
  title     = {A disagreement count scheme for inference of constrained
               {M}arkov networks},
  booktitle = {Grammatical Inference: {L}earning Syntax from Sentences,
               3rd International Colloquium, ICGI-96, Montpellier, France,
               September 25--27, 1996, Proceedings},
  year      = {1996},
  volume    = {1147},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {168--178},
  publisher = {Springer, Berlin}
}

@article{gretton2012kernel,
  title   = {A kernel two-sample test},
  author  = {Gretton, Arthur and Borgwardt, Karsten M and Rasch, Malte
             J and Sch{\"o}lkopf, Bernhard and Smola, Alexander},
  journal = {Journal of Machine Learning Research},
  volume  = {13},
  number  = {Mar},
  pages   = {723--773},
  year    = {2012}
}

@inproceedings{gretton2012optimal,
  title     = {Optimal kernel choice for large-scale two-sample tests},
  author    = {Gretton, Arthur and Sejdinovic, Dino and Strathmann, Heiko
               and Balakrishnan, Sivaraman and Pontil, Massimiliano and
               Fukumizu, Kenji and Sriperumbudur, Bharath K},
  booktitle = {Advances in neural information processing systems},
  pages     = {1205--1213},
  year      = {2012}
}

@inproceedings{grettonetal05,
  author    = {Arthur Gretton and Olivier Bousquet and Alexander Smola
               and Bernhard Schoelkopf},
  title     = {Measuring statistical dependence with {Hilbert-Schmidt}
               norms},
  booktitle = {ALT},
  year      = {2005},
  pages     = {63--77}
}

@inproceedings{GreYan85,
  author    = {P. Green and B. Yandell},
  title     = {Semi-parametric Generalized Linear Models},
  booktitle = {Proceedings 2nd International GLIM Conference},
  year      = {1985},
  number    = {32},
  series    = {Lecture Notes in Statist.},
  pages     = {44-55},
  publisher = {Springer Verlag},
  address   = {New York}
}

@incollection{Grieser95,
  author    = {Gunter Grieser},
  title     = {{T\kern-.1em\lower.7ex\hbox{L}\kern-.31emP\kern-.1em\raise.4ex\hbox{S}}
               - a Term Rewriting Laboratory (not only) for Experiments in
               Automatic Program Synthesis},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {467--481}
}

@inproceedings{Grieser96,
  author    = {Gunter Grieser},
  title     = {Reflecting inductive inference machines and its
               improvement by therapy},
  booktitle = alt-ws,
  year      = {1996},
  volume    = {1160},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {325--336},
  publisher = {Springer-Verlag}
}

@book{Griewank00,
  title     = {Evaluating Derivatives: Principles and Techniques of
               Algorithmic Differentiation},
  publisher = {SIAM},
  year      = {2000},
  author    = {Andreas Griewank},
  series    = {Frontiers in Applied Mathematics},
  address   = {Philadelphia}
}

@inproceedings{GriJanLan98,
  author    = {Gunter Grieser and Klaus P. Jantke and Steffen Lange},
  title     = {Characterizing Sufficient Expertise for Learning System
               Validation},
  booktitle = {Proc. 11th International Florida Artificial Intelligence
               Research Symposium Conference - FLAIRS '98},
  year      = {1998},
  pages     = {452--456},
  publisher = {AAAI Press, Menlo Park}
}

@inproceedings{GriJanLan98b,
  author    = {G. Grieser and K. P. Jantke and S. Lange},
  title     = {Towards the Validation of Inductive Learning Systems},
  booktitle = alt,
  year      = {1998},
  volume    = {1501},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {409--423},
  publisher = {Springer-Verlag}
}

@inproceedings{GriJanLanTho00,
  author    = {Gunter Grieser and Klaus P. Jantke and Steffen Lange and
               Bernd Thomas},
  title     = {A Unifying Approach to {HTML} Wrapper Representation and
               Learning},
  booktitle = {Discovery Science, Third International Conference, DS
               2000, Kyoto, Japan, DEcember 2000, Proceedings},
  year      = {2000},
  volume    = {1967},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {50--64},
  publisher = {Springer, Berlin}
}

@inproceedings{GriLanZeu00,
  author    = {Gunter Grieser and Steffen Lange and T. Zeugmann},
  title     = {Learning Recursive Concepts with Anomalies},
  address   = {Sydney, Australia},
  booktitle = alt,
  month     = dec,
  year      = {2000},
  volume    = {1968},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {101--115},
  publisher = {Springer, Berlin}
}

@incollection{GriLoz85,
  author    = {W.~E.~L. Grimson and T. Lozano-Perez},
  title     = {Model-{B}ased {R}ecognition and {L}ocalization from
               {S}parse {R}ange {D}ata},
  booktitle = {Techniques for 3-D Machine Perception},
  publisher = {North-Holland, Amsterdam},
  year      = {1985},
  editor    = {A. Rosenfeld}
}

@book{Grimson81,
  title     = {From Images to Surfaces},
  publisher = {{MIT} Press},
  year      = {1981},
  author    = {W.~E.~L. Grimson},
  address   = {Cambridge, MA}
}

@article{Grimson82,
  author  = {W.~E.~L. Grimson},
  title   = {A Computational Theory of Visual Surface Interpolation},
  journal = {the Royal Society of London B},
  year    = {1982},
  volume  = {298},
  pages   = {395--427}
}

@book{Grimson90,
  title     = {Object Recognition by Computer},
  publisher = {{MIT} Press},
  year      = {1990},
  author    = {W.~E.~L. Grimson},
  address   = {Cambridge, MA}
}

@article{GriRob96,
  author  = {M. Gribskov and N.~L. Robinson},
  title   = {Use of receiver operating characteristic ({ROC}) analysis
             to evaluate sequence matching},
  journal = {Computers and Chemistry},
  year    = {1996},
  volume  = {20},
  pages   = {25--33},
  number  = {1}
}

@article{GriSte04,
  author  = {T.L. Griffiths and M. Steyvers},
  title   = {Finding scientific topics},
  journal = {the National Academy of Sciences},
  year    = 2004,
  volume  = 101,
  pages   = {5228-5235}
}

@article{GriSte06,
  author  = {J.E. Griffn and M.F.J. Steel},
  journal = {Journal of the American Statistical Association},
  title   = {Order-based dependent Dirichlet processes},
  year    = {2006},
  volume  = {101},
  number  = {473},
  pages   = {1566--1581}
}

@book{GriSti01,
  title     = {Probability and Random Processes},
  publisher = {Oxford University Press},
  year      = {2001},
  author    = {G.~R. Grimmet and D.~R. Stirzaker},
  address   = {Oxford},
  edition   = {Third}
}

@book{GriSti92,
  title     = {Probability and Random Processes},
  publisher = {Oxford University Press},
  year      = {1992},
  author    = {G.~R. Grimmet and D.~R. Stirzaker},
  address   = {Oxford},
  edition   = {Second}
}

@proceedings{GroBayBen05,
  editor    = {Robert Grossman and Roberto Bayardo and Kristin P.
               Bennett},
  title     = {the Eleventh ACM SIGKDD International Conference on
               Knowledge Discovery and Data Mining, Chicago, Illinois,
               USA, August 21--24, 2005},
  booktitle = {KDD},
  publisher = {ACM},
  year      = {2005},
  isbn      = {1-59593-135-X}
}

@book{Grobner65,
  title     = {Matrizenrechnung},
  publisher = {{BI} Hochschultaschenb\"ucher},
  year      = {1965},
  author    = {W. {Gr\"obner}}
}

@inproceedings{GroGan96,
  author    = {Stephan Grolimund and Jean-Gabriel Ganascia},
  title     = {Speeding-up nearest neighbour memories: the template tree
               case memory organisation},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {225--233},
  publisher = {Morgan Kaufmann}
}

@inproceedings{GroIta93,
  author    = {R. Grossi and G. Italiano },
  title     = {Suffix Trees and their Applications in String Algorithms},
  booktitle = {Procedings of $1^{\mathrm{st}}$ South American Workshop on
               String Processing (WSP 1993)},
  year      = {1993},
  pages     = {57--76},
  month     = {September}
}

@inproceedings{GroLes16,
  title        = {node2vec: Scalable feature learning for networks},
  author       = {Grover, Aditya and Leskovec, Jure},
  booktitle    = kdd,
  pages        = {855--864},
  year         = {2016},
  organization = {ACM}
}

@article{GroLitSch01,
  author    = {Adam J. Grove and Nick Littlestone and Dale Schuurmans},
  title     = {General Convergence Results for Linear Discriminant
               Updates},
  journal   = {Machine Learning},
  year      = {2001},
  volume    = {43},
  pages     = {173--210},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{GroLitSch97,
  author    = {Adam J. Grove and Nick Littlestone and Dale Schuurmans},
  title     = {General Convergence Results for Linear Discriminant
               Updates},
  booktitle = {Proc. 10th Annu. Conf. on Comput. Learning Theory},
  year      = {1997},
  pages     = {171--183},
  publisher = {ACM Press, New York, NY}
}

@book{GroLusTha99,
  title     = {Using {MPI-2}},
  publisher = {{MIT} Press},
  year      = {1999},
  author    = {W. Gropp and E. Lusk and R. Thakur}
}

@article{GroRot01,
  author    = {Adam J. Grove and D. Roth},
  title     = {Linear Concepts and Hidden Variables},
  journal   = {Machine Learning},
  year      = {2001},
  volume    = {42},
  pages     = {123--141},
  number    = {1/2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{GroSch98,
  author    = {A. Grove and D. Schuurmans},
  title     = {Boosting in the limit: Maximizing the margin of learned
               ensembles},
  booktitle = {the Fifteenth National Conference on Artificial
               Intelligence},
  year      = {1998},
  pages     = {692--699}
}

@book{Grosswald85,
  title     = {{R}epresentation of {I}ntegers as {S}ums of {S}quares},
  publisher = {Springer},
  year      = {1985},
  author    = {E. Grosswald},
  address   = {N.\,Y.}
}

@inproceedings{Grover_BoostedGenerative_2018,
  address   = {New Orleans, LA, USA},
  title     = {Boosted generative models},
  booktitle = {Proceedings of the 32nd {AAAI} conference on artificial
               intelligence},
  publisher = {AAAI Press},
  author    = {Grover, Aditya and Ermon, Stefano},
  month     = feb,
  year      = {2018},
  keywords  = {⛔ No DOI found},
  pages     = {3077--3084},
  file      = {Grover, Ermon - Boosted generative models
               (2018).pdf:/Users/zcranko/Documents/Library/Conference
               Paper/Grover, Ermon - Boosted generative models
               (2018).pdf:application/pdf}
}

@inproceedings{GroveSchuurmans98,
  author    = {Adam J. Grove and Dale Schuurmans},
  title     = {Boosting in the Limit: Maximizing the Margin of Learned
               Ensembles},
  booktitle = aaai,
  year      = {1998}
}

@book{GroWel92,
  title     = {Information Bounds and Nonparametric Maximum Likelihood
               Estimation},
  publisher = {Springer},
  year      = {1992},
  author    = {P. Groeneboom and J.~A. Wellner},
  volume    = {19},
  series    = {{DMV}}
}

@article{Gruber89,
  author    = {T. R. Gruber},
  title     = {Automated Knowledge Acquisition for Strategic Knowledge},
  journal   = {Machine Learning},
  year      = {1989},
  volume    = {4},
  pages     = {293--336},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{GruDaw04,
  author  = {P. Grunwald and A. Dawid},
  journal = {Annals of Statistics},
  number  = {4},
  pages   = {1367--1433},
  title   = {Game theory, maximum entropy, minimum discrepancy, and
             robust {B}ayesian decision theory},
  volume  = {32},
  year    = {2004}
}

@book{Grunwald07,
  title     = {The Minimum Description Length Principle},
  publisher = mitpr,
  year      = {2007},
  author    = {Peter D. Gr\"{u}nwald}
}

@article{grunwald2004game,
  author  = {Peter D. Gr\"unwald and A. Phillip Dawid},
  title   = {Game Theory, Maximum Entropy, Minimum Discrepancy, and
             Robust {B}ayesian Decision Theory},
  journal = {Annals of Statistics},
  year    = {2004},
  volume  = {32},
  pages   = {1367--1433}
}

@inproceedings{Grunwald99,
  author    = {Peter Gr{\"u}nwald},
  title     = {Viewing all models as `probabilistic'},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {171--182},
  publisher = {ACM Press, New York, NY}
}

@article{Grunwald_GameTheory_2004,
  title    = {Game theory, maximum entropy, minimum discrepancy and
              robust {Bayesian} decision theory},
  volume   = {32},
  issn     = {0090-5364},
  doi      = {10.1214/009053604000000553},
  abstract = {We describe and develop a close relationship between two
              problems that have customarily been regarded as distinct:
              that of maximizing entropy, and that of minimizing
              worst-case expected loss. Using a formulation grounded in
              the equilibrium theory of zero-sum games between Decision
              Maker and Nature, these two problems are shown to be dual
              to each other, the solution to each providing that to the
              other. Although Topsoe described this connection for the
              Shannon entropy over 20 years ago, it does not appear to be
              widely known even in that important special case. We here
              generalize this theory to apply to arbitrary decision
              problems and loss functions. We indicate how an appropriate
              generalized definition of entropy can be associated with
              such a problem, and we show that, subject to certain
              regularity conditions, the above-mentioned duality
              continues to apply in this extended context. This
              simultaneously provides a possible rationale for maximizing
              entropy and a tool for finding robust Bayes acts. We also
              describe the essential identity between the problem of
              maximizing entropy and that of minimizing a related
              discrepancy or divergence between distributions. This leads
              to an extension, to arbitrary discrepancies, of a
              well-known minimax theorem for the case of Kullback-Leibler
              divergence (the "redundancy-capacity theorem" of
              information theory). For the important case of families of
              distributions having certain mean values specified, we
              develop simple sufficient conditions and methods for
              identifying the desired solutions. We use this theory to
              introduce a new concept of "generalized exponential family"
              linked to the specific decision problem under
              consideration, and we demonstrate that this shares many of
              the properties of standard exponential families. Finally,
              we show that the existence of an equilibrium in our game
              can be rephrased in terms of a "Pythagorean property" of
              the related divergence, thus generalizing previously
              announced results for Kullback-Leibler and Bregman
              divergences.},
  language = {en},
  number   = {4},
  journal  = {The Annals of Statistics},
  author   = {Grünwald, Peter D. and Dawid, A. Philip},
  month    = aug,
  year     = {2004},
  keywords = {Additive model, Bayes act, Bregman divergence, Brier
              score, convexity, duality, equalizer rule, exponential
              family, Gamma-minimax, generalized exponential family,
              Kullback-Leibler divergence, logarithmic score, maximin,
              mean-value constraints, minimax, mutual information,
              Pythagorean property, redundancy-capacity theorem, relative
              entropy, saddle-point, scoring rule, specific entropy,
              uncertainty function, zero-one loss},
  pages    = {1367--1433},
  file     = {Grünwald, Dawid - Game theory, maximum entropy, minimum
              discrepancy and robust Bayesian decision
              (2004).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Grünwald, Dawid - Game theory, maximum entropy,
              minimum discrepancy and robust Bayesian decision
              (2004).pdf:application/pdf}
}

@article{GruRinChePanetal01,
  author  = {S. Gruvberger and M. Ringner and Y. Chen and S. Panavally
             and L. H. Saal and A. Borg and M. Ferno and C. Peterson and
             P. S. Meltzer},
  title   = {Estrogen receptor status in breast cancer is associated
             with remarkably distinct gene expression patterns},
  journal = {Cancer Res},
  year    = {2001},
  volume  = {61},
  pages   = {5979-5984},
  number  = {16},
  month   = {Aug}
}

@article{GruRinChePanetal01b,
  author  = {S. Gruvberger and M. Ringner and Y.Chen and S.Panavally
             and L.H. Saal and A.Borg, M. Ferno, C. Peterson and
             P.S.Meltzer },
  title   = {Estrogen receptor status in breast cancer is associated
             with remarkably distinct gene expression patterns},
  journal = {Cancer Research},
  volume  = {61},
  year    = {2001}
}

@inproceedings{GruUng00,
  author    = {Gregory Z. Grudic and Lyle H. Ungar},
  title     = {Localizing Policy Gradient Estimates to Action
               Transitions},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {343--350},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{GueEliPau00,
  author    = {Yann Guermeur and Andre Elisseeff and H. Paugam-Moisy},
  title     = {A new multi-class {SVM} based on a uniform convergence
               result},
  booktitle = {IJCNN 2000},
  year      = {2000}
}

@inproceedings{GueKraSin05,
  author    = {C. Guestrin and A. Krause and A. Singh},
  title     = {Near-Optimal Sensor Placements in Gaussian Processes},
  booktitle = {International Conference on Machine Learning ICML'05},
  year      = {2005}
}

@inproceedings{GueLagPar02,
  author    = {Carlos Guestrin and Michail Lagoudakis and Ronald Parr},
  title     = {Coordinated Reinforcement Learning},
  booktitle = {ICML},
  year      = {2002}
}

@article{GuelatMarcotte86,
  author  = {J. Gu\'elat and P. Marcotte},
  title   = {Some comments on {W}olfe's `away step'},
  journal = {Mathematical Programming},
  year    = {1986},
  volume  = {35},
  pages   = {110--119}
}

@article{GueMar86,
  author  = {Gu{\'e}Lat, Jacques and Marcotte, Patrice},
  title   = {Some comments on {W}olfe's `away step'},
  journal = {Mathematical Programming},
  year    = {1986},
  volume  = {35},
  number  = {1},
  pages   = {110--119}
}

@inproceedings{GuhKumRag04,
  author    = {R. Guha and R. Kumar and P. Raghavan and A. Tomkins},
  title     = {Propagation of trust and distrust},
  booktitle = {World Wide Web {WWW}},
  year      = {2004},
  isbn      = {1-58113-844-X},
  pages     = {403--412},
  location  = {New York, NY, USA},
  doi       = {http://doi.acm.org/10.1145/988672.988727},
  publisher = {ACM},
  address   = {New York, NY, USA}
}

@article{GuiLavRag01,
  author  = {David Guijarro and Victor Lavin and Vijay Raghavan},
  title   = {Monotone term decision lists},
  journal = {Theoret. Comput. Sci.},
  year    = {2001},
  volume  = {259},
  pages   = {549--575},
  number  = {1--2}
}

@inproceedings{GuiLavRag97,
  author    = {David Guijarro and Victor Lavin and Vijay Raghavan},
  title     = {Learning monotone term decision lists},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  pages     = {16--26},
  publisher = {Springer-Verlag}
}

@article{GuiLavRag99,
  author  = {D. Guijarro and V. Lavin and V. Raghavan},
  title   = {Exact learning when irrelevant variables abound},
  journal = {Inform. Process. Lett.},
  year    = {1999},
  volume  = {70},
  pages   = {233--239},
  number  = {5}
}

@inproceedings{Guimaraes00,
  author    = {Gabriela Guimar{\~{a}}es},
  title     = {The Induction of Temporal Grammatical Rules from
               Multivariate Time Series},
  booktitle = {Grammatical Inference: {A}lgorithms and {A}pplications,
               5th International Colloquium, ICGI 2000, Lisbon, Portugal,
               September 11--13, 2000 ; Proceedings},
  year      = {2000},
  volume    = {1891},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {127--140},
  publisher = {Springer, Berlin}
}

@inproceedings{GuiRakCan05,
  author    = {V. Guigue and A. Rakotomamonjy and S. Canu},
  year      = {2005},
  title     = {Kernel Basis Pursuit},
  booktitle = {Proc. of European Conf. on Machine Learning},
  pages     = {146--157}
}

@inproceedings{GuLilSutetal16,
  title     = {Continuous Deep Q-Learning with Model-Based Acceleration},
  booktitle = icml,
  year      = {2016},
  pages     = {2829--2838},
  author    = {Gu, Shixiang and Lillicrap, Timothy and Sutskever, Ilya
               and Levine, Sergey}
}

@article{GunFerTek98,
  author  = {B. G\"unsel and A. M. Ferman and A. M. Tekalp},
  title   = {Temporal video segmentation using unsupervised clustering
             and semantic object tracking},
  journal = {Journal of Electronic Imaging},
  volume  = {7},
  number  = {3},
  year    = {1998},
  pages   = {592-604}
}

@techreport{GunKru90,
  author      = {S. Gunnarsson and P. Krus},
  title       = {Modelling of a flexible mechanical system containing
                 hydraulic actuators},
  institution = {Link\"oping University},
  year        = {1990},
  number      = {S-581 83},
  address     = {Link\"oping, Sweden}
}

@inproceedings{GunMahAcePla05,
  author    = {Asela Gunawardana and M. Mahajan and Alex Acero and John
               C. Platt},
  title     = {Hidden Conditional Random Fields for Phone
               Classification},
  booktitle = {Proc. Annual Conference of the International Speech
               Communication Association (INTERSPEECH)},
  year      = {2005},
  pages     = {1117--1120},
  url       = {http://research.microsoft.com/en-us/um/redmond/groups/srg/papers/2005-aselag-eurospeech.pdf}
}

@article{guo2017countering,
  title   = {Countering adversarial images using input
             transformations},
  author  = {Guo, Chuan and Rana, Mayank and Cisse, Moustapha and van
             der Maaten, Laurens},
  journal = {arXiv:1711.00117},
  year    = {2017}
}

@article{Guo_BoostingVariational_2017,
  title    = {Boosting variational inference},
  abstract = {Variational inference (VI) provides fast approximations of
              a Bayesian posterior in part because it formulates
              posterior approximation as an optimization problem: to find
              the closest distribution to the exact posterior over some
              family of distributions. For practical reasons, the family
              of distributions in VI is usually constrained so that it
              does not include the exact posterior, even as a limit
              point. Thus, no matter how long VI is run, the resulting
              approximation will not approach the exact posterior. We
              propose to instead consider a more flexible approximating
              family consisting of all possible finite mixtures of a
              parametric base distribution (e.g., Gaussian). For
              efficient inference, we borrow ideas from gradient boosting
              to develop an algorithm we call boosting variational
              inference (BVI). BVI iteratively improves the current
              approximation by mixing it with a new component from the
              base distribution family and thereby yields progressively
              more accurate posterior approximations as more computing
              time is spent. Unlike a number of common VI variants
              including mean-field VI, BVI is able to capture
              multimodality, general posterior covariance, and
              nonstandard posterior shapes.},
  journal  = {arXiv:1611.05559 [cs, stat]},
  author   = {Guo, Fangjian and Wang, Xiangyu and Fan, Kai and
              Broderick, Tamara and Dunson, David B.},
  month    = mar,
  year     = {2017},
  note     = {arXiv: 1611.05559},
  keywords = {⛔ No DOI found, Computer Science - Machine Learning,
              Statistics - Machine Learning}
}

@inproceedings{GuoBarShaWil99,
  author    = {Y.~G. Guo and P.~L. Bartlett and J. Shawe-Taylor and R.~C.
               Williamson},
  title     = {Covering Numbers for Support Vector Machines},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1999}
}

@unpublished{GuoBarSmoWil00,
  author = {Y. Guo and P. Bartlett and A. J. Smola and R.~C.
            Williamson},
  title  = {Norm-Based Regularization of Boosting},
  note   = {manuscript, in preparation},
  year   = {2000}
}

@techreport{GuoBarSmoWil01,
  author      = {Y. Guo and P.~L. Bartlett and A. J. Smola and R.~C.
                 Williamson},
  title       = {Norm-Based Regularization of Boosting},
  institution = {Australian National University, RSISE},
  year        = {2001}
}

@inproceedings{GuoHanFuXi07,
  author    = {F. Guo and S. Hanneke and W. Fu and E.~P. Xing},
  title     = {Recovering Temporally Rewiring Networks: A Model-based
               Approach.},
  booktitle = icml,
  year      = {2007}
}

@article{GuoRanCisVan18,
  title     = {Countering adversarial images using input
               transformations},
  author    = {Guo, Chuan and Rana, Mayank and Cisse, Moustapha and van
               der Maaten, Laurens},
  booktitle = iclr,
  year      = {2018}
}

@inproceedings{GuoZhuMin15,
  author    = {Hongyu Guo and Xiaodan Zhu and Renqiang Min},
  title     = {A Deep Learning Model for Structured Outputs with
               High-order Interaction},
  booktitle = {NIPS Workshop on Representation and Learning Methods for
               Complex Outputs},
  year      = 2015
}

@inbook{Gurevich01,
  author    = {Yuri Gurevich},
  title     = {From invariants to canonization},
  book      = {Current trends in theoretical computer science: entering
               the 21st centuary},
  year      = {2001},
  isbn      = {981-02-4473-8},
  pages     = {327--331},
  publisher = {World Scientific Publishing Co., Inc.},
  address   = {River Edge, NJ, USA}
}

@article{Gurevich97,
  author  = {Yuri Gurevich},
  title   = {From Invariants to Canonization.},
  journal = {Bulletin of the EATCS},
  volume  = {63},
  year    = {1997}
}

@article{Gurevich97b,
  author  = {Yuri Gurevich},
  title   = {From Invariants to Canonization.},
  journal = {Bulletin of the EATCS},
  volume  = {63},
  year    = {1997}
}

@incollection{GurKoi95,
  author    = {L. Gurvits and P. Koiran},
  title     = {Approximation and Learning of Convex Superpositions},
  booktitle = {the European Conference on Computational Learning Theory},
  publisher = {Springer},
  year      = {1995},
  editor    = {Paul Vitanyi},
  volume    = {904},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {222--236},
  address   = {Berlin}
}

@article{GurKoi97,
  author  = {L. Gurvits and P. Koiran},
  title   = {Approximation and learning of convex superpositions},
  journal = {Journal of Computer and System Sciences},
  year    = {1997},
  volume  = {55},
  pages   = {161--170},
  number  = {1}
}

@inproceedings{GurSah99,
  author    = {Venkatesan Guruswami and Amit Sahai},
  title     = {Multiclass learning, boosting, and error-correcting
               codes},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {145--155},
  publisher = {ACM Press, New York, NY}
}

@article{Gurvits01,
  author  = {Leonid Gurvits},
  title   = {A note on a scale-sensitive dimension of linear bounded
             functionals in {B}anach spaces},
  journal = {Theoret. Comput. Sci.},
  year    = {2001},
  volume  = {261},
  pages   = {81--90},
  number  = {1},
  note    = {Special issue for ALT '97}
}

@inproceedings{Gurvits97,
  author    = {L. Gurvits},
  title     = {A note on a scale-sensitive dimension of linear bounded
               functionals in {Banach} spaces},
  booktitle = alt,
  year      = {1997},
  editor    = {M. Li and A. Maruoka},
  series    = {LNAI-1316},
  pages     = {352--363},
  address   = {Berlin},
  publisher = {Springer}
}

@book{Gusfield97,
  title     = {Algorithms on Strings, Trees and Sequences: Computer
               Science and Computational Biology},
  publisher = {Cambridge University Press},
  year      = {1997},
  author    = {D. Gusfield},
  month     = {June},
  note      = {ISBN 0--521--58519--8}
}

@article{GusTar94,
  title   = {A Faster Parametric Minimum-Cut Algorithm},
  author  = {D. Gusfield and {\'E}. Tardos},
  journal = {Algorithmica},
  pages   = {278--290},
  year    = {1994},
  volume  = {11},
  number  = {3}
}

@article{Gutierrez_ChainRule_2005,
  title    = {A chain rule for ɛ-subdifferentials with applications to
              approximate solutions in convex {Pareto} problems},
  volume   = {310},
  issn     = {0022247X},
  doi      = {10.1016/j.jmaa.2005.02.017},
  language = {en},
  number   = {1},
  journal  = {Journal of Mathematical Analysis and Applications},
  author   = {Gutiérrez, César and Jiménez, Bienvenido and Novo,
              Vicente},
  month    = oct,
  year     = {2005},
  pages    = {309--327},
  file     = {A chain rule for ɛ-subdifferentials with applications to
              approximate solutions in convex Pareto
              problems:/Users/zcranko/Documents/Zotero/storage/7XM5KWRS/10.1016@j.jmaa.2005.02.017.html:text/html;Full
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              et al. - 2005 - A chain rule for ɛ-subdifferentials with
              applicati.pdf:application/pdf}
}

@misc{Gutman94,
  author = {I. Gutman},
  title  = {Graph Theory Notes New York},
  year   = {1994}
}

@misc{Gutman94b,
  author = {I. Gutman},
  title  = {Graph Theory Notes New York},
  year   = {1994}
}

@inproceedings{GutMut04,
  author    = {Carsten Gutwenger and Petra Mutzel},
  title     = {Graph Embedding with Minimum Depth and Maximum External
               Face},
  pages     = {259--272},
  editor    = {G. Liotta},
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  series    = {Lecture Notes in Computer Science},
  publisher = {Springer Verlag},
  volume    = 2912,
  year      = 2004
}

@article{GutRusTriWil75,
  author  = {I. Gutman and B. Russic and N. Trinajstic and C. F. Wilcox
             Jr.},
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@article{GutRusTriWil75b,
  author  = {I. Gutman and B. Russic and N. Trinajstic and C. F. Wilcox
             Jr.},
  title   = {Graph theory and molecular orbitals. XII. Acyclic
             polyenes},
  journal = {Journal of Chemical Physics},
  year    = {1975},
  volume  = {62},
  number  = {9},
  pages   = {3399-3405},
  month   = {May}
}

@article{GutTri72,
  author  = {I. Gutman and N. Trinajstic},
  title   = {Graph theory and molecular orbitals. Total small $\phi$,
             Greek-electron energy of alternant hydrocarbons},
  journal = {Chemical Physics Letters},
  year    = {1972},
  volume  = {17},
  pages   = {535-538},
  number  = {4},
  month   = {December}
}

@article{GutTri72b,
  author  = {I. Gutman and N. Trinajstic},
  title   = {Graph theory and molecular orbitals. Total small $\phi$,
             Greek-electron energy of alternant hydrocarbons},
  journal = {Chemical Physics Letters},
  year    = {1972},
  volume  = {17},
  number  = {4},
  pages   = {535-538},
  month   = {December}
}

@inproceedings{GutVisWil05,
  author    = {O. Guttman and S.~V.~N. Vishwanathan and R. C.
               Williamson},
  title     = {Probabilistic Automata Learning via Oracles},
  booktitle = {ALT 2005},
  year      = {2005},
  editor    = {Sanjay Jain and Hans Ulrich Simon and Etsuji Tomita},
  number    = {3734},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {171--182},
  address   = {Singapore},
  month     = {October},
  publisher = {Springer-Verlag}
}

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  volume    = {23},
  pages     = {47--67},
  number    = {1},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{GuWah93,
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  year    = {1993},
  volume  = {55},
  pages   = {353--368}
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@article{GuWanKueetal15,
  author     = {Gu, Jiuxiang and Wang, Zhenhua and Kuen, Jason and Ma,
                Lianyang and Shahroudy, Amir and Shuai, Bing and Liu, Ting
                and Wang, Xingxing and Wang, Gang and Cai, Jianfei and
                Chen, Tsuhan},
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  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {199--209},
  address   = {San Mateo, CA},
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  author    = {T. Hancock},
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  year      = {1993},
  pages     = {352--360},
  publisher = {ACM Press, New York, NY}
}

@article{Hancock96,
  author    = {T. Hancock},
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  journal   = {Machine Learning},
  year      = {1996},
  volume    = {25},
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@techreport{Handcock03,
  author      = {Mark S. Handcock},
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  institution = {Center for Statistics and the Social Sciences, University
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  year        = {2003},
  type        = {Working Paper},
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}

@article{HanGolMar94,
  author  = {T. R. Hancock and Mostefa Golea and Mario Marchand},
  title   = {Learning nonoverlapping perceptron networks from examples
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  journal = {Machine Learning},
  year    = {1994},
  volume  = {16},
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  number  = {3},
  annote  = {Was Harvard TR-26-91}
}

@inproceedings{HanHel91,
  author    = {T. Hancock and L. Hellerstein},
  title     = {Learning read-once formulas over fields and extended
               bases},
  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {326--336},
  publisher = {Morgan Kaufmann, San Mateo, CA}
}

@article{HanJiaLiTro96,
  author  = {T. Hancock and Tao Jiang and Ming Li and John Tromp},
  title   = {Lower bound on learning decision lists and trees},
  journal = {Inform. Comput.},
  year    = {1996},
  volume  = {126},
  pages   = {114--122},
  number  = {2}
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  author    = {T. Hancock and Y. Mansour},
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  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {179--183},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@book{HanRemRiv93,
  title     = {Machine Learning: From Theory to Applications; Cooperative
               Research at Siemens and {MIT}},
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  year      = {1993},
  editor    = {S. J. Hanson and W. Remmele and Ronald L. Rivest},
  booktitle = {Lecture Notes in Computer Science Vol. 661}
}

@article{Hansen92,
  author  = {P.~C. Hansen},
  title   = {Analysis of Discrete Ill-Posed Problems by Means of the
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  journal = {{SIAM} Review},
  year    = {1992},
  volume  = {34},
  pages   = {561--580},
  number  = {4},
  month   = dec
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@article{HanSom90,
  author  = {D. Hansel and H. Sompolinsky},
  title   = {Learning from Examples in a Single-Layer Neural Network},
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  year    = {1990},
  volume  = {11},
  pages   = {687--692},
  number  = {7}
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@article{Hantoute_SubdifferentialCalculus_2008,
  title      = {Subdifferential calculus rules in convex analysis: a
                unifying approach via pointwise supremum functions},
  volume     = {19},
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  shorttitle = {Subdifferential {Calculus} {Rules} in {Convex}
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  doi        = {10.1137/070700413},
  language   = {en},
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  year       = {2008},
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@inproceedings{Har-Peled01,
  author    = {S. {Har-Peled}},
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  year      = {2001},
  pages     = {84--93}
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@inproceedings{Haraguchi92,
  author    = {M. Haraguchi},
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@article{Haralick80,
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  year      = {1986},
  volume    = {265},
  series    = {Lecture Notes in Computer Science},
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}

@book{Harary69,
  title     = {Graph Theory},
  publisher = {Addison-Wesley},
  year      = {1969},
  author    = {F. Harary},
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@inproceedings{HarBac07,
  author    = {Z. Harchaoui and F. Bach},
  title     = {Image classification with segmentation graph kernels},
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               (CVPR)},
  year      = {2007}
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@incollection{HarBacMou08,
  title     = {Testing for Homogeneity with Kernel Fisher Discriminant
               Analysis},
  author    = {Z. Harchaoui and F. Bach and E. Moulines},
  booktitle = {NIPS 20},
  publisher = {MIT Press},
  year      = {2008}
}

@techreport{HarBai96,
  author      = {Mance E. Harmon and Baird, III, Leemon C.},
  title       = {Multi-Player Residual Advantage Learning With General
                 Function Approximation},
  institution = {Wright Laboratory, WL/AACF},
  year        = {1996},
  number      = {WL-TR-1065},
  address     = {Wright-Patterson Air Force Base, OH 45433-7308},
  note        = {{\small\url{http://www.leemon.com/papers/sim_tech/sim_tech.pdf}}}
}

@inproceedings{Harchaouietal12,
  author    = {Zaid Harchaoui and Matthijs Douze and Mattis Paulin and
               Miroslav Dudik and J\'{e}r\^{o}me Malick},
  title     = {Large-scale Image Classification with Trace-norm
               Regularization},
  booktitle = cvpr,
  year      = 2012
}

@article{Harchaouietal13,
  year    = {2014},
  journal = {Mathematical Programming},
  title   = {Conditional gradient algorithms for norm-regularized
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  author  = {Zaid Harchaoui and Anatoli Juditsky and Arkadi
             Nemirovski}
}

@article{HarDes72,
  author  = {R.~L. Harder and R.~M. Desmarais},
  title   = {Interpolation Using Surface Splines},
  journal = {J. Aircraft},
  year    = {1972},
  volume  = {9},
  pages   = {189--191}
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@book{Hardle90,
  title     = {Applied nonparametric regression},
  publisher = {Cambridge University Press},
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  author    = {W. H\"ardle},
  volume    = {19},
  series    = {Econometric Society Monographs}
}

@book{Hardle91,
  title     = {Smoothing Techniques, With Implementations in {S}},
  publisher = {Springer},
  year      = {1991},
  author    = {W. H\"ardle},
  address   = {New York}
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@article{hardoonetal04,
  author  = {David R. Hardoon and Sandor Szedmak and John
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  journal = {Neural Computation},
  year    = {2004},
  volume  = {16},
  pages   = {2639--2664},
  issue   = {12}
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@inproceedings{HarPriSre16,
  title     = {Equality of opportunity in supervised learning},
  author    = {Hardt, Moritz and Price, Eric and Srebro, Nati},
  booktitle = nips,
  pages     = {3315--3323},
  year      = {2016}
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@article{Hardy71,
  author  = {R.~L. Hardy},
  title   = {Multiquadric equations of topography and other irregular
             surfaces},
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  year    = {1971},
  volume  = {76},
  pages   = {1905--1915}
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@inproceedings{Hardy75,
  author       = {S. Hardy},
  title        = {Synthesis of {LISP} programs from examples},
  booktitle    = {Proc. 4th IJCAI},
  year         = {1975},
  pages        = {268--273},
  organization = {IJCAI}
}

@article{Hardy90,
  author  = {R.~L. Hardy},
  title   = {Theory and applications of the multiquadric-biharmonic
             method},
  journal = {Computers Math.\ Applic.},
  year    = {1990},
  volume  = {19},
  pages   = {163--208},
  number  = {8/9}
}

@book{Hardy_Inequalities_2001,
  address   = {Cambridge, UK},
  title     = {Inequalities},
  isbn      = {978-0-521-35880-4 978-0-521-05206-1},
  language  = {English},
  publisher = {Cambridge University Press},
  author    = {Hardy, G. H and Littlewood, John E and Pólya, George},
  year      = {2001},
  note      = {OCLC: 48507127},
  file      = {(Cambridge Mathematical Library) Hardy G.H., Littlewood
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@book{Hardyetal52,
  title     = {Inequalities},
  publisher = {Cambridge University Press},
  year      = {1952},
  author    = {Godfrey H. Hardy and John E. Littlewood and George
               P\'olya},
  edition   = {2nd}
}

@article{HarJudNem15,
  year    = {2015},
  journal = {Mathematical Programming},
  title   = {Conditional gradient algorithms for norm-regularized
             smooth convex optimization},
  author  = {Zaid Harchaoui and Anatoli Juditsky and Arkadi
             Nemirovski},
  volume  = {152},
  pages   = {75--112}
}

@inproceedings{HarKah07,
  author    = {Richard Hartley and Fredrik Kahl},
  title     = {Optimal Algorithms in Multiview Geometry},
  pages     = {13--34},
  volume    = 1,
  booktitle = {Proc.\ 8th Asian Conf.\ Computer Vision (ACCV)},
  address   = {Tokyo, Japan},
  year      = 2007
}

@article{HarKeeKow90,
  author  = {E. Hartman and K. Keeler and J.~M. Kowalski},
  title   = {Layered neural networks with {G}aussian hidden units as
             universal approximators},
  journal = {Neural Computation},
  year    = {1990},
  volume  = {2},
  pages   = {210--215},
  number  = {2}
}

@article{HarKocLuo90,
  author  = {J.~G. Harris and C. Koch and J. Luo},
  title   = {A two-dimensional analog {VLSI} circuit for detecting
             discontinuities in early vision},
  journal = {Science},
  year    = {1990},
  volume  = {248},
  pages   = {1209--1211},
  month   = jun
}

@incollection{HarKocLuoWya89,
  author    = {J.~G. Harris and C. Koch and J. Luo and J. Wyatt},
  title     = {Resistive fuses: analog hardware for detecting
               discontinuities in early vision},
  booktitle = {{VLSI} implementations of neural systems},
  publisher = {Kluwer Academic},
  year      = {1989},
  editor    = {C. Mead and M. Ismail},
  address   = {Norwell, MA}
}

@article{HarKocStaLuo90,
  author  = {J.~G. Harris and C. Koch and E. Staats and J.~G. Luo},
  title   = {Analog hardware for detecting discontinuities in early
             vision},
  journal = {Intl.\ Journal of Comp.\ Vision},
  year    = {1990},
  volume  = {4},
  pages   = {211--223}
}

@inproceedings{HarKor01,
  author    = {D. Harel and Y. Koren},
  title     = {Clustering spatial data using random walks},
  booktitle = {Knowledge Discovery and Data Mining ({KDD}01)},
  year      = {2001},
  pages     = {281--286}
}

@inproceedings{HarLiuMat91,
  author    = {J.~G. Harris and S. Liu and S. Mathur},
  title     = {Discarding outliers in a nonlinear resistive network},
  booktitle = {the International Joint Conference on Neural Networks},
  year      = {1991},
  address   = {Seattle, WA.},
  month     = jul
}

@article{HarMouBraSha07,
  author  = {D. Hardoon and J. {Mour\~ao-Miranda} and M. Brammer and
             John Shawe-Taylor},
  title   = {Unsupervised analysis of fMRI data using kernel canonical
             correlation},
  journal = {NeuroImage},
  year    = {2007},
  volume  = {37},
  number  = {4},
  pages   = {1250--1259}
}

@article{HarNilRap68,
  author  = {P.~E. Hart and N.~J. Nilsson and B. Raphael},
  title   = {A formal basis for the heuristic determination of minimum
             cost paths},
  journal = {{IEEE} Trans.\ Systems, Man and Cybernetics},
  year    = {1968},
  volume  = {{SSC-4}},
  pages   = {100--107},
  number  = {2}
}

@article{HarPan90,
  title   = {Finite-dimensional variational inequality and nonlinear
             complementarity problems: a survey of theory, algorithms
             and applications},
  year    = {1990},
  author  = {P. T. Harker and J.-S. Pang},
  journal = {Mathematical Programming},
  volume  = 48,
  number  = {1-3},
  pages   = {161--220}
}

@article{HarPra96,
  author  = {G.~F. Harpur and R.~W. Prager},
  title   = {Development of low entropy coding in a recurrent network},
  journal = {Network},
  year    = {1996},
  volume  = {7},
  pages   = {277--284}
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@article{HarRam18,
  author  = {G.H. Hardy and S. Ramanujan},
  title   = {Asymptotic Formulae in Combinatory Analysis},
  journal = {Proc.\ London Mathematical Society},
  year    = {1918},
  volume  = {17},
  pages   = {75-115}
}

@inproceedings{Harries99,
  author    = {M. Harries},
  title     = {Boosting a strong learner: evidence against the minimum
               margin},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {171--180},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{Harrington03,
  author    = {E.F. Harrington},
  title     = {Online ranking/Collaborative Filtering using the
               perceptron algorithm},
  booktitle = {International Conference on Machine Learning ICML 20},
  year      = {2003}
}

@inproceedings{Harris89,
  author    = {J.~G. Harris},
  title     = {An Analog {VLSI} Chip for Thin-Plate Surface
               Interpolation},
  booktitle = {Advances in Neural Information Processing Systems 1},
  year      = {1989},
  editor    = {D.~S. Touretzky},
  pages     = {687--694},
  address   = {Carnegie Mellon University},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{Harris91,
  author    = {J.~G. Harris},
  title     = {Continuous-time segmentation networks},
  booktitle = {SPIE Technical Symposia on Optical Engineering and
               Photonics in Aerospace Sensing},
  year      = {1991},
  address   = {Orlando, FL},
  month     = apr,
  note      = {Visual Information Processing: From Neurons to Chips}
}

@phdthesis{Harris91b,
  author  = {J.~G. Harris},
  title   = {Analog Models for Early Vision},
  school  = {California Institute of Technology},
  year    = {1991},
  address = {Pasadena, CA},
  month   = may
}

@inproceedings{HarRotZim07,
  author    = {Sariel Har-Peled and Dan Roth and Dav Zimak},
  title     = {Maximum Margin Coresets for Active and Noise Tolerance
               Learning},
  booktitle = {International Joint Conference on Artificial
               Intelligence},
  pages     = {836--841},
  year      = 2007
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@inproceedings{HarRub78,
  author    = {D. Harrison and D.~L. Rubinfeld},
  title     = {Hedonic prices and the demand for clean air},
  booktitle = {J. Environ.\ Economics \& Management},
  year      = {1978},
  volume    = {5},
  pages     = {81--102},
  note      = {Original source of the Boston Housing data, actually from
               ftp://ftp.ics.uci.com/pub/machine-learning-databases/housing}
}

@article{HarSag09,
  author  = {Hare, Warren and Sagastiz{\'a}bal, Claudia},
  title   = {Computing proximal points of nonconvex functions},
  journal = {Mathematical Programming},
  year    = {2009},
  volume  = {116},
  number  = {1},
  pages   = {221--258}
}

@inproceedings{HarSanHarLovetal12,
  title     = {Sparse Coding and Dictionary Learning for Symmetric
               Positive Definite Matrices: A Kernel Approach},
  booktitle = eccv,
  year      = {2012},
  author    = {Harandi, Mehrtash T. and Sanderson, Conrad and Hartley,
               Richard and Lovell, Brian C.", editor="Fitzgibbon, Andrew
               and Lazebnik, Svetlana and Perona, Pietro and Sato, Yoichi
               and Schmid, Cordelia}
}

@article{HarShiOoy99,
  author    = {Masahiko Haruno and Satoshi Shirai and Yoshifumi Ooyama},
  title     = {Using Decision Trees to Construct a Practical Parser},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {34},
  pages     = {131--149},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{HarSzeSha04,
  author  = {D. Hardoon and S. Szedmak and J. Shawe-Taylor},
  title   = {Canonical Correlation Analysis: An Overview with
             Application to Learning Methods},
  journal = {Neural Computation},
  year    = {2004},
  volume  = {16},
  pages   = {2639--2664}
}

@techreport{Hart85,
  author      = {G. W. Hart},
  title       = {Prototype Nonintrusive Appliance Load Monitor},
  institution = {MIT Energy Laboratory},
  year        = {1985},
  number      = {Progress Report 2},
  month       = sep,
  comment     = {Finite-state automaton inference to determine appliances
                 present.}
}

@phdthesis{Hart87,
  author  = {G. W. Hart},
  title   = {Minimum Information Estimation of Structure},
  school  = {MIT Dept.\ of Electrical Engineering and Computer Science},
  year    = {1987},
  month   = apr,
  note    = {Appears as LIDS-TH-1664.},
  comment = {Studies and applies Rissanen's MDLP.}
}

@book{Hartigan75,
  title     = {Clustering Algorithms},
  publisher = {John Wiley and Sons},
  year      = {1975},
  author    = {J.~A. Hartigan},
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@article{Hartigan87,
  author  = {J.~A. Hartigan},
  title   = {Estimation of a Convex Density Contour in Two Dimensions},
  journal = {J. Amer. Statist. Assoc.},
  year    = {1987},
  volume  = {82},
  pages   = {267--270}
}

@book{Harville97,
  title     = {Matrix Algebra From a Statistician's Perspective},
  publisher = {Springer},
  year      = {1997},
  author    = {David A. Harville},
  address   = {New York}
}

@techreport{HarZieKawMul02,
  author      = {S. Harmeling and A. Ziehe and M. Kawanabe and K.-M.
                 M\"uller},
  title       = {Kernel-Based Nonlinear Blind Source Separation},
  institution = {Fraunhofer FIRST.IDA},
  year        = {2002},
  number      = {1/2002},
  address     = {Kekul\'estr. 7, 12489 Berlin Germany}
}

@article{HasImaIsh95,
  author  = {Susumu Hasegawa and Hiroshi Imai and Masaki Ishiguro},
  title   = {$\varepsilon$-approximations of $k$-label spaces},
  journal = {Theoret.\ Comput.\ Sci.},
  year    = {1995},
  volume  = {137},
  pages   = {145--157},
  number  = {1},
  note    = {Special Issue for ALT '93}
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@inproceedings{HasKai94,
  author    = {B. Hassibi and T. Kailath},
  title     = {${H}^{\infty}$ Bounds for the recursive-least-squares
               algorithm},
  booktitle = {Proc. of the Thirtythird IEEE Conference on Decision and
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  year      = {1994},
  pages     = {3927--3929},
  month     = dec,
  publisher = {IEEE},
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@article{HasRosTibZhu04,
  author    = {Hastie, Trevor and Rosset, Saharon and Tibshirani, Rob and
               Zhu, Ji},
  journal   = {JMLR},
  pages     = {1391--1415},
  publisher = mitpr,
  address   = {Cambridge, MA, USA},
  title     = {The Entire Regularization Path for the Support Vector
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  volume    = {5},
  year      = {2004}
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@article{HasSayKai96,
  author  = {B. Hassibi and A.~H. Sayed and T. Kailath},
  title   = {${H}^{\infty}$ Optimality of the {LMS} Algorithm},
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  year    = {1996},
  volume  = {44}
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@article{HasSayKai96b,
  author  = {B. Hassibi and A. H. Sayed and T. Kailath},
  title   = {Linear estimation in {K}rein spaces - part {I}: {T}heory},
  journal = {IEEE Trans. on Automatic Control},
  year    = {1996},
  volume  = {41},
  pages   = {18--33},
  number  = {1}
}

@article{HasStu89,
  author  = {T. Hastie and W. Stuetzle},
  title   = {Principal curves},
  journal = {Journal of the American Statistical Association},
  year    = {1989},
  volume  = {84},
  pages   = {502--516},
  number  = {406}
}

@inproceedings{HasTib02,
  author    = {T. Hastie and R. Tibshirani},
  title     = {Independent Components Analysis through Product Density
               Estimation},
  booktitle = {Advances in Neural Information Processing Systems 15},
  year      = {2002},
  editor    = {S. Becker and S. Thrun and K. Obermayer},
  volume    = {15},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{HasTib86,
  author  = {T. Hastie and R. Tibshirani},
  title   = {Generalized additive models},
  journal = {Statistical Science},
  year    = {1986},
  volume  = {1},
  pages   = {297--318}
}

@article{HasTib87,
  author  = {T. Hastie and R. Tibshirani},
  title   = {Generalized additive models: some applications},
  journal = {Journal of the American Statistical Association},
  year    = {1987},
  volume  = {82},
  pages   = {371--386}
}

@book{HasTib90,
  title     = {Generalized Additive Models},
  publisher = {Chapman and Hall},
  year      = {1990},
  author    = {T.~J. Hastie and R.~J. Tibshirani},
  volume    = {43},
  series    = {Monographs on Statistics and Applied Probability},
  address   = {London}
}

@techreport{HasTib96,
  author      = {Trevor Hastie and Robert Tibshirani},
  title       = {Classification by Pairwise Coupling},
  institution = {Department of Public Health Sciences and Statistics,
                 University of Toronto},
  year        = {1996},
  address     = {Canada}
}

@inproceedings{HasTib98,
  author    = {T. Hastie and R. Tibshirani},
  title     = {Classification by Pairwise Coupling},
  booktitle = {Advances in Neural Information Processing Systems 10},
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@article{HasTibBuj94,
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@article{HasTibEisAlietal00,
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@book{HasTibFri01,
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@book{HasTibFri09,
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@book{hastieetal09,
  title     = {The Elements of Statistical Learning},
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@inproceedings{HatWar03,
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@article{HauCoi97,
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  year      = {1997},
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@article{HauKeaLitWar91,
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@article{HauKeaSch94,
  author  = {David Haussler and Micheal Kearns and Robert Schapire},
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  year    = {1994},
  volume  = {14},
  pages   = {88--113}
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@inproceedings{HauKeaSeuTis94,
  author    = {D. Haussler and M. Kearns and H. S. Seung and N. Tishby},
  title     = {Rigorous learning curve bounds from statistical
               mechanics},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {76--87}
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@article{HauKeaSeuTis96,
  author  = {David Haussler and Micheal Kearns and H. Sebastian Seung
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  journal = {Machine Learning},
  year    = {1996},
  volume  = {25},
  pages   = {195--236}
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@inproceedings{HauKivWar95,
  author    = {David Haussler and J. Kivinen and M. K. Warmuth},
  title     = {Tight worst-case loss bounds for predicting with expert
               advice},
  booktitle = {Computational Learning Theory: EuroColt '95},
  year      = {1995},
  pages     = {69--83},
  publisher = {Springer-Verlag},
  note      = {Early version in U. of CA, Santa Cruz, TR UCSC-CRL-94-36}
}

@article{HauLitWar94,
  author  = {D. Haussler and N. Littlestone and M. K. Warmuth},
  title   = {Predicting $\{0,1\}$ Functions on Randomly Drawn Points},
  journal = {Inform. Comput.},
  year    = {1994},
  volume  = {115},
  pages   = {284--293},
  number  = {2},
  note    = {Was in FOCS88, COLT88, and Univ. of California at Santa
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}

@article{HauLon95,
  author  = {D. Haussler and P. Long},
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  journal = {J. Comb. Theory (A)},
  year    = {1995},
  volume  = {71},
  pages   = {219--240},
  number  = {2},
  note    = {Was Univ. of CA. at Santa Cruz TR UCS-CRL-90-15}
}

@inproceedings{HauOpp95,
  author    = {David Haussler and Manfred Opper},
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  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {402--411},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{HauOpp95b,
  author    = {David Haussler and Manfred Opper},
  title     = {Mutual Information and {B}ayes Methods for Learning a
               Distribution},
  booktitle = {Proc. Workshop on the Theory of Neural Networks: The
               Statistical Mechanics Perspective},
  year      = {1995},
  publisher = {World Scientific}
}

@article{HauOpp97,
  author    = {David Haussler and Manfred Opper},
  title     = {Metric Entropy and Minimax Risk in Classification},
  journal   = {Studies in Logic and Computer Science, a selection of
               essays in honor of Andrzej Ehrenfeucht},
  year      = {1997},
  volume    = {1261},
  pages     = {212--235},
  address   = {Berlin},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science}
}

@article{HauOpp97b,
  author  = {David Haussler and Manfred Opper},
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  journal = {The Annals of Statistics},
  year    = {1997},
  volume  = {25},
  pages   = {2451--2492},
  number  = {6}
}

@proceedings{HauPit88,
  title     = {the First Annu. Workshop on Comput. Learning Theory},
  year      = {1988},
  editor    = {D. Haussler and L. Pitt},
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  publisher = {Morgan Kaufmann, Inc.}
}

@techreport{Haussler87,
  author      = {D. Haussler},
  title       = {Applying Valiant's Learning Framework to {AI} Concept
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  institution = {UCSC},
  year        = {1987},
  number      = {UCSC-CRL-87-11}
}

@inproceedings{Haussler87b,
  author    = {D. Haussler},
  title     = {Bias, Version Spaces, and {V}aliant's Learning Framework},
  booktitle = {Proc. of the 4th International Workshop on Machine
               Learning},
  year      = {1987},
  pages     = {324--336},
  address   = {San Mateo, CA},
  month     = jun,
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@article{Haussler88,
  author  = {David Haussler},
  title   = {Quantifying Inductive Bias: {AI} Learning Algorithms and
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  year    = {1988},
  volume  = {38},
  pages   = {177--221}
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@techreport{Haussler88b,
  author      = {D. Haussler},
  title       = {Space Efficient Learning Algorithms},
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  year        = {1988},
  number      = {UCSC-CRL-88-2},
  address     = {Santa Cruz, CA}
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@techreport{Haussler89,
  author      = {D. Haussler},
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  year        = {1989},
  type        = {Technical Report},
  number      = {UCSC-CRL-91-02}
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@article{Haussler89b,
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@inproceedings{Haussler90,
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@article{Haussler92,
  author  = {D. Haussler},
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@article{Haussler95,
  author  = {D. Haussler},
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@inproceedings{Haussler97,
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  year      = {1997},
  pages     = {109--113},
  publisher = {ACM Press, New York, NY}
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@techreport{Haussler99,
  author      = {David Haussler},
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  institution = {UC Santa Cruz},
  year        = {1999},
  type        = {Technical Report},
  number      = {UCS-CRL-99-10}
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@incollection{HauWar93,
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  year      = {1993},
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@misc{Heckerman96,
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@book{Heinonen_LecturesAnalysis_2012,
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  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {278--285},
  publisher = {Morgan Kaufmann}
}

@inproceedings{Hekanaho96,
  author    = {Jukka Hekanaho},
  title     = {Background knowledge in {GA}-based concept learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {234--242},
  publisher = {Morgan Kaufmann}
}

@inproceedings{helawrence11,
  author    = {Jingrui He and Rick Lawrence},
  title     = {A Graph-based Framework for Multi-Task Multi-View Learning
               },
  booktitle = icml,
  year      = {2011}
}

@article{Held65,
  author  = {R. Held},
  title   = {Plasticity in sensory-motor systems},
  journal = {Scientific American},
  year    = {1965},
  volume  = {213},
  pages   = {84--94},
  note    = {Cited after E.~B. Goldstein. {\em Sensation and
             Perception}. Wadsworth, Belmont, California, 1984}
}

@inproceedings{HelKar89,
  author    = {L. Hellerstein and M. Karpinski},
  title     = {Learning read-once formulas using membership queries},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1989},
  pages     = {146--161},
  publisher = {Morgan Kaufmann},
  annote    = {Preliminary version appeared as ICSI TR-89-021. Expanded
               version with Angluin as a co-author apperaed in {\it J. ACM
               } in 1993}
}

@techreport{HelKar91,
  author      = {L. Hellerstein and M. Karpinski},
  title       = {Computational complexity of learning read-once formulas
                 over different bases},
  institution = {International Computer Science Institute, Berkeley, CA,
                 TR-91-014},
  year        = {1991}
}

@inproceedings{HelKivWar96,
  author    = {D. P. Helmbold and J. Kivinen and M. K. Warmuth},
  title     = {Worst-Case Loss Bounds for Sigmoided Linear Neurons},
  booktitle = {Proc. 1996 Neural Information Processing Conference},
  year      = {1996},
  note      = {To appear}
}

@techreport{HelKivWar96b,
  author      = {D. P. Helmbold and J. Kivinen and M. K. Warmuth},
  title       = {Worst-case Loss Bounds for Single Neurons},
  institution = {Univ. of Calif. Computer Research Lab},
  year        = {1996},
  number      = {UCSC-CRL-96-2},
  address     = {Santa Cruz, CA},
  note        = {In preparation.}
}

@article{HelKivWar99,
  author  = {D. P. Helmbold and J. Kivinen and M. K. Warmuth},
  title   = {Relative Loss Bounds for Single Neurons},
  journal = {IEEE Transactions on Neural Networks},
  year    = {1999},
  volume  = {10},
  pages   = {1291--1304},
  number  = {6},
  month   = {November}
}

@inproceedings{HelKwePit97,
  author    = {David Helmbold and Stephen Kwek and Leonard Pitt},
  title     = {Learning when to trust which experts},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  pages     = {134--149},
  publisher = {Springer-Verlag}
}

@article{Helland88,
  author  = {I. Helland},
  title   = {On the Structure of Partial Least Squares Regression},
  journal = {Commun. Statist. Simula.},
  year    = {1988},
  volume  = {17},
  pages   = {581--607},
  number  = {2}
}

@phdthesis{Hellerstein89,
  author = {L. Hellerstein},
  title  = {On {C}haracterizing and {L}earning {S}ome {C}lasses of
            {R}ead-once {F}unctions},
  school = {University of California, Berkeley},
  year   = {1989}
}

@article{Hellerstein93,
  author    = {Lisa Hellerstein},
  title     = {Book {R}eview: Machine Learning: {A} Theoretical
               Approach},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {13},
  pages     = {145--149},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Hellerstein94,
  author    = {Lisa Hellerstein},
  title     = {Guest Editor's Introduction},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {17},
  pages     = {111--114},
  number    = {2/3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{HelLitLon00,
  author  = {David P. Helmbold and Nicholas Littlestone and P. M.
             Long},
  title   = {Apple Tasting},
  journal = {Inform. Comput.},
  year    = {2000},
  volume  = {161},
  pages   = {85--139},
  number  = {2},
  month   = sep
}

@article{HelLitLon00b,
  author  = {David P. Helmbold and Nicholas Littlestone and P. M.
             Long},
  title   = {On-Line Learning with Linear Loss Constraints},
  journal = {Inform. Comput.},
  year    = {2000},
  volume  = {161},
  pages   = {140--171},
  number  = {2},
  month   = sep
}

@inproceedings{HelLitLon92,
  author    = {D. P. Helmbold and N. Littlestone and P. M. Long},
  title     = {Apple tasting and nearly one-sided learning},
  booktitle = {Proc. of the 33rd Symposium on the Foundations of Comp.
               Sci.},
  year      = {1992},
  pages     = {493--502},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@inproceedings{HelLon91,
  author    = {D. P. Helmbold and P. M. Long},
  title     = {Tracking drifting concepts using random examples},
  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {13--23},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{HelLon94,
  author    = {David P. Helmbold and P. M. Long},
  title     = {Tracking drifting concepts by minimizing disagreements},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {14},
  pages     = {27--45},
  number    = {1},
  annote    = {Was in COLT 1991 and was UC Santa Cruz TR 91-26},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{HelPan97,
  author    = {David Helmbold and Sandra Panizza},
  title     = {Some Label Efficient Learning Results},
  booktitle = {Proc. 10th Annu. Conf. on Comput. Learning Theory},
  year      = {1997},
  pages     = {218--230},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{HelPanWar99,
  author    = {David P. Helmbold and Sandra Panizza and M. K. Warmuth},
  title     = {Direct and Indirect Algorithms or On-line Learning of
               Disjunctions},
  booktitle = {Proc. 4th European Conference on Computational Learning
               Theory},
  year      = {1999},
  volume    = {1572},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {138--152},
  publisher = {Springer-Verlag}
}

@inproceedings{HelPilRagWil95,
  author    = {Lisa Hellerstein and Krishnan Pillaipakkamnatt and Vijay
               Raghavan and Dawn Wilkins},
  title     = {How many queries are needed to learn?},
  booktitle = {Proc. 27th Annual ACM Symposium on Theory of Computing},
  year      = {1995},
  pages     = {190--199},
  publisher = {ACM Press, New York, NY}
}

@article{HelSch97,
  author  = {D. P. Helmbold and R. E. Schapire},
  title   = {Predicting nearly as well as the best pruning of a
             decision tree},
  journal = {Machine Learning},
  year    = {1997},
  pages   = {51--68},
  note    = {Earlier version in COLT95}
}

@inproceedings{HelSchSinWar96,
  author    = {David P. Helmbold and Robert E. Schapire and Yoram Singer
               and M. K. Warmuth},
  title     = {On-line portfolio selection using multiplicative updates},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {243--251},
  publisher = {Morgan Kaufmann}
}

@article{HelSchSinWar97,
  author  = {D. Helmbold and R. E. Schapire and Y. Singer and M. K.
             Warmuth},
  title   = {A comparison of new and old algorithms for a mixture
             estimation problem},
  journal = {Machine Learning},
  year    = {1997},
  pages   = {97--119},
  note    = {Earlier versions appeared as techreport UCSC-CRL-95-50,
             Dep. of Comp. Sc, UC Santa Cruz, October 95, and in
             COLT95}
}

@article{HelSchSinWar98,
  author  = {David P. Helmbold and Robert E. Schapire and Yoram Singer
             and M. K. Warmuth},
  title   = {On-line Portofolio Selection Using Multiplicative
             Updates},
  journal = {Mathematical Finance},
  year    = {1998},
  volume  = {8},
  pages   = {325-347},
  number  = {4}
}

@article{HelSinWar97,
  author    = {David P. Helmbold and Robert E. Schapire andYoram Singer
               and M. K. Warmuth},
  title     = {A Comparison of New and Old Algorithms for a Mixture
               Estimation Problem},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {27},
  pages     = {97--119},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{HelSloWar89,
  author    = {D. Helmbold and R. Sloan and M. K. Warmuth},
  title     = {Learning nested differences of intersection-closed concept
               classes},
  booktitle = {Proc. 2nd Annu. Workshop on Comput. Learning Theory},
  year      = {1989},
  pages     = {41--56},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{HelSloWar90,
  author    = {D. Helmbold and R. Sloan and M. K. Warmuth},
  title     = {Learning Nested Differences of Intersection Closed Concept
               Classes},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  pages     = {165--196},
  number    = {2},
  note      = {Special Issue on Computational Learning Theory; first
               appeared in 2nd COLT conference (1989)},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{HelSloWar92,
  author  = {D. Helmbold and R. Sloan and M. K. Warmuth},
  title   = {Learning Integer Lattices},
  journal = {SIAM J. Comput.},
  year    = {1992},
  volume  = {21},
  pages   = {240--266},
  number  = {2},
  annote  = {Earlier version in COLT90}
}

@incollection{HelTom56,
  author    = {I. Heller and C. Tompkins},
  title     = {An extension of a theorem of Dantzig's},
  booktitle = {Linear Inequalities and Related Systems},
  year      = {1956},
  editor    = {H.W. Kuhn and A.W. Tucker},
  volume    = {38},
  series    = {Annals of Mathematics Studies},
  publisher = {AMS}
}

@inproceedings{HelWar92,
  author    = {D. P. Helmbold and M. K. Warmuth},
  title     = {Some weak learning results},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {399--412},
  publisher = {ACM Press, New York, NY}
}

@article{HelWar95,
  author  = {D. Helmbold and M. K. Warmuth},
  title   = {On Weak Learning},
  journal = {Journal of Computer and System Sciences},
  year    = {1995},
  volume  = {50},
  pages   = {551--573},
  number  = {3},
  month   = jun
}

@article{Hemminger69,
  author  = {R.~L. Hemminger},
  title   = {On Reconstructing a Graph},
  journal = {the American Mathematical Society},
  year    = {1969},
  volume  = {20},
  number  = {1},
  pages   = {185-187},
  month   = {January}
}

@article{Hendel79,
  author  = {R. J. Hendel},
  title   = {Mathematical learning theory: a formalized, axiomatic,
             abstract approach},
  journal = {Inform. Control},
  year    = {1979},
  volume  = {41},
  pages   = {67--117}
}

@article{Hendrickson_ProperScores_1971,
  title    = {Proper scores for probability forecasters},
  volume   = {42},
  issn     = {00034851},
  doi      = {10.1214/aoms/1177693057},
  abstract = {[A probability forecaster is asked to give a density p of
              a random variable ω. In return he gets a reward (or score)
              depending on p and on a subsequently observed value of ω.
              A scoring rule is called proper if the expected score is
              maximized when the true density is chosen. The present
              paper uses convex analysis to generalize McCarthy's
              characterization of proper scoring rules.]},
  number   = {6},
  journal  = {The Annals of Mathematical Statistics},
  author   = {Hendrickson, Arlo D. and Buehler, Robert J.},
  year     = {1971},
  pages    = {1916--1921},
  file     = {Hendrickson, Buehler - Proper scores for probability
              forecasters
              (1971).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Hendrickson, Buehler - Proper scores for
              probability forecasters (1971).pdf:application/pdf}
}

@inproceedings{HenDut09,
  author    = {Chase Hensel and Haimonti Dutta},
  title     = {{GADGET SVM: a Gossip-bAseD sub-GradiEnT SVM Solver}},
  booktitle = {ICML, Numerical Mathematics in Machine Learning Workshop},
  year      = 2009
}

@article{Henkin64,
  author  = {G.~M. Henkin},
  title   = {Linear superpositions of continuously differentiable
             functions},
  journal = {Dokl.\ Akad.\ Nauk SSSR},
  year    = {1964},
  volume  = {157},
  pages   = {288--290}
}

@inproceedings{HenKraHen94,
  author    = {R. Hengstenberg and H. Krapp and B. Hengstenberg},
  title     = {Visual sensation of self-motions in the blowfly {\em
               Calliphora}},
  booktitle = {Biocybernetics of Vision: Integrative and Cognitive
               Processes (October 16--21, 1994, Ischia, Italy)},
  year      = {1994},
  editor    = {C. Taddei-Ferretti}
}

@article{HenPen99,
  author  = {N. Henze and M. Penrose},
  title   = {On the multivariate runs test},
  journal = {The Annals of Statistics},
  year    = {1999},
  volume  = {27},
  pages   = {290--298},
  number  = {1}
}

@book{Henrici74,
  title     = {Applied and computational complex analysis. Vol.~1},
  publisher = {John Wiley and Sons},
  year      = {1974},
  author    = {P. Henrici},
  address   = {New York}
}

@book{Henrici77,
  title     = {Applied and computational complex analysis. Vol.~2},
  publisher = {John Wiley and Sons},
  year      = {1977},
  author    = {P. Henrici},
  address   = {New York}
}

@phdthesis{Herbrich00,
  author = {R. Herbrich},
  title  = {Learning Linear Classifiers\,---\,Theory and Algorithms},
  school = {Technische Universit{\"a}t Berlin},
  year   = {2000}
}

@book{Herbrich02,
  title     = {Learning Kernel Classifiers: Theory and Algorithms},
  publisher = {{MIT} Press},
  address   = {Cambridge, MA},
  year      = {2002},
  author    = {R. Herbrich}
}

@misc{Herbrich97,
  author      = {R. Herbrich},
  title       = {Segmentierung mit {G}aborfiltern zur {I}nduktion
                 struktureller {K}lassifikatoren auf {B}ilddaten},
  year        = {1997},
  note        = {Master's thesis (in german).},
  institution = {Technical University of Berlin}
}

@misc{Herbrich99,
  author = {Ralf Herbrich},
  title  = {Exact Tail Bound for Binomial Distributed Variables},
  year   = {1999},
  note   = {internal note}
}

@inproceedings{Herbst00,
  author    = {Joachim Herbst},
  title     = {A Machine Learning Approach to Workflow Management},
  booktitle = {Machine Learning: Proc.\ 11th European Conference (ECML)},
  address   = {Barcelona, Catalonia, Spain},
  month     = {May 31 - June 2},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {183--194},
  publisher = {Springer, Berlin}
}

@inproceedings{Herbster01,
  author    = {M. Herbster},
  title     = {Learning Additive Models Online With Fast Evaluating
               Kernels},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2001},
  editor    = {D.~P. Helmbold and R.~C. Williamson},
  volume    = {2111},
  series    = {Lecture Notes in Computer Science},
  pages     = {444--460},
  publisher = {Springer}
}

@inbook{HerFinZim95,
  chapter   = {17},
  pages     = {323--343},
  title     = {Improving model selection by dynamic regularization
               methods},
  publisher = {{MIT} Press},
  year      = {1995},
  author    = {Ferdinand Hergert and William Finnoff and Hans-Georg
               Zimmermann},
  volume    = {III: Selecting Good Models},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@inproceedings{HerGra01,
  author    = {Ralf Herbrich and Thore Graepel},
  title     = {A {PAC}-{B}ayesian Margin Bound for Linear Classifiers:
               Why {SVMs} work},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {224--230},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{HerGra01b,
  author    = {Ralf Herbrich and Thore Graepel},
  title     = {Large Scale {B}ayes Point Machines},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {528--534},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@misc{HerGra01c,
  author = {Ralf Herbrich and Thore Graepel},
  title  = {A {PAC}-{B}ayesian Margin Bound for Linear Classifiers},
  year   = {2001},
  note   = {submitted to IEEE Transactions on Information Theory}
}

@inproceedings{HerGraBolObe98,
  author    = {R. Herbrich and T. Graepel and P. Bollmann-Sdorra and K.
               Obermayer},
  title     = {Learning a Preference Relation in {IR}},
  booktitle = {Proceedings Workshop Text Categorization and Machine
               Learning, International Conference on Machine Learning
               1998},
  year      = {1998},
  pages     = {80--84}
}

@inproceedings{HerGraBolObe98b,
  author    = {Ralf Herbrich and Thore Graepel and Peter Bollmann-Sdorra
               and Klaus Obermayer},
  title     = {Supervised Learning of Preference Relations},
  booktitle = {Proceedings Fachgruppentreffen Maschinelles Lernen},
  year      = {1998},
  pages     = {43--47}
}

@inproceedings{HerGraCam00,
  author    = {Ralf Herbrich and Thore Graepel and Colin Campbell},
  title     = {Robust {B}ayes Point Machines},
  booktitle = {ESANN 2000},
  year      = {2000},
  pages     = {49--54}
}

@article{HerGraCam01,
  author  = {Ralf Herbrich and Thore Graepel and Colin Campbell},
  title   = {Bayes Point Machines},
  journal = {Journal of Machine Learning Research},
  year    = {2001},
  volume  = {1},
  pages   = {245--279}
}

@inproceedings{HerGraCam99,
  author    = {Ralf Herbrich and Thore Graepel and Colin Campbell},
  title     = {Bayes Point Machines: Estimating the {B}ayes Point in
               Kernel Space},
  booktitle = {IJCAI Workshop Support Vector Machines},
  year      = {1999},
  pages     = {23--27}
}

@techreport{HerGraCam99b,
  author      = {Ralf Herbrich and Thore Graepel and Colin Campbell},
  title       = {Bayesian Learning in Reproducing Kernel {H}ilbert Spaces},
  institution = {Technical University of Berlin},
  year        = {1999},
  note        = {TR 99-11}
}

@inproceedings{HerGraObe00,
  author    = {R. Herbrich and T. Graepel and K. Obermayer},
  title     = {Large margin rank boundaries for ordinal regression},
  booktitle = {Advances in Large Margin Classifiers},
  year      = {2000},
  editor    = {A. J. Smola and P.~L. Bartlett and B. Sch\"olkopf and D.
               Schuurmans},
  pages     = {115--132},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@techreport{HerGraObe99,
  author      = {Ralf Herbrich and Thore Graepel and Klaus Obermayer},
  title       = {Regression Models for Ordinal Data: A Machine Learning
                 Approach},
  institution = {TU Berlin},
  year        = {1999},
  note        = {TR-99/03}
}

@inproceedings{HerGraObe99b,
  author    = {Ralf Herbrich and Thore Graepel and Klaus Obermayer},
  title     = {Support Vector Learning for Ordinal Regression},
  booktitle = {the International Conference on Artificial Neural
               Networks},
  year      = {1999},
  pages     = {97--102}
}

@inproceedings{HerGraSha00,
  author    = {R. Herbrich and T. Graepel and J. Shawe-Taylor},
  title     = {Sparsity vs.\ large margins for linear classifiers},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2000},
  pages     = {304--308}
}

@misc{HerGraWil01,
  author = {Ralf Herbrich and Thore Graepel and R.~C. Williamson},
  title  = {The Structure of Version Space},
  year   = {2001},
  note   = {submitted to Journal of Machine Learning Research}
}

@inbook{HerGraWil05,
  chapter   = {The Structure of Version Space},
  title     = {Innovations in Machine Learning},
  publisher = {Springer},
  year      = {2005},
  author    = {R. Herbrich and T. Graepel and R.~C. Williamson},
  address   = {New York},
  month     = {January},
  editor    = {D. Holmes and L.~C. Jain}
}

@inbook{HerGraWil05b,
  chapter   = {The Structure of Version Space},
  title     = {Innovations in Machine Learning},
  publisher = {Springer},
  year      = {2005},
  author    = {R. Herbrich and T. Graepel and R.~C. Williamson},
  month     = {January},
  editor    = {D. Holmes and L.~C. Jain}
}

@techreport{HerHey97,
  author      = {Ralf Herbrich and Eric Heymann},
  title       = {Animation von flexiblen {O}bjekte},
  institution = {Technical University of Berlin},
  year        = {1997},
  note        = {in german}
}

@inproceedings{HerJacOliCuretal01,
  author    = {A. Hertzmann and C. Jacobs and N. Oliver and B. Curless
               and D. Salesin},
  title     = {Image Analogies},
  booktitle = {SIGGRAPH 2001 Conference Proceedings},
  pages     = {327--340},
  year      = {2001},
  address   = {New York, NY, USA},
  publisher = {ACM Press}
}

@inbook{HerKeiGraBoletal98,
  chapter   = {3, Neural Networks in Economics: Background, Applications
               and New Developments},
  pages     = {42},
  title     = {Computational Techniques for Modelling Learning in
               Economics},
  publisher = {Kluwer Academic},
  year      = {1998},
  author    = {Ralf Herbrich and Max Keilbach and Thore Graepel and Peter
               Bollmann-Sdorra and Klaus Obermayer},
  note      = {accepted for publication}
}

@article{HerKeiGraBoletal99,
  author  = {R. Herbrich and M. Keilbach and T. Graepel and P.
             Bollmann-Sdorra and K. Obermayer},
  title   = {Neural Networks in Economics: Background, Applications,
             and New Developments},
  journal = {Advances in Computational Economics},
  year    = {1999},
  volume  = {11},
  pages   = {169--196}
}

@book{HerKroPal91,
  title     = {Introduction to the Theory of Neural Computation},
  publisher = {Addison-Wesley},
  year      = {1991},
  author    = {J.~A. Hertz and A. Krogh and R. Palmer},
  address   = {Redwood City, CA}
}

@inproceedings{HerLav99,
  author    = {Montserrat Hermo and Vitor Lavin},
  title     = {Learning Minimal Covers of Functional Dependencies with
               Queries},
  booktitle = alt,
  year      = {1999},
  volume    = {1720},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {291--300},
  publisher = {Springer-Verlag}
}

@inproceedings{HerMinGra07,
  author    = {Ralf Herbrich and Tom Minka and Thore Graepel},
  title     = {TrueSkill{$^{\text{TM}}$}: A {B}ayesian Skill Ranking
               System},
  year      = 2007,
  booktitle = {NIPS}
}

@inproceedings{HerSch97,
  author    = {Ralf Herbrich and Tobias Scheffer},
  title     = {Generation of Task Specific Segmentation Procedures as a
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}

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@article{HinDenYuetal12,
  title        = {Deep {{Neural Networks}} for {{Acoustic Modeling}} in
                  {{Speech Recognition}}: {{The Shared Views}} of {{Four
                  Research Groups}}},
  volume       = {29},
  issn         = {1053-5888},
  doi          = {10.1109/MSP.2012.2205597},
  shorttitle   = {Deep {{Neural Networks}} for {{Acoustic Modeling}} in
                  {{Speech Recognition}}},
  abstract     = {Most current speech recognition systems use hidden Markov
                  models (HMMs) to deal with the temporal variability of
                  speech and Gaussian mixture models (GMMs) to determine how
                  well each state of each HMM fits a frame or a short window
                  of frames of coefficients that represents the acoustic
                  input. An alternative way to evaluate the fit is to use a
                  feed-forward neural network that takes several frames of
                  coefficients as input and produces posterior probabilities
                  over HMM states as output. Deep neural networks (DNNs) that
                  have many hidden layers and are trained using new methods
                  have been shown to outperform GMMs on a variety of speech
                  recognition benchmarks, sometimes by a large margin. This
                  article provides an overview of this progress and
                  represents the shared views of four research groups that
                  have had recent successes in using DNNs for acoustic
                  modeling in speech recognition.},
  number       = {6},
  journaltitle = {IEEE Signal Processing Magazine},
  year         = {2012-11},
  pages        = {82-97},
  keywords     = {acoustic modeling,Acoustics,Automatic speech
                  recognition,Data models,deep neural networks,feed-forward
                  neural network,feedforward neural nets,Gaussian mixture
                  models,Gaussian processes,hidden Markov models,Hidden
                  Markov models,HMM states,Neural networks,posterior
                  probabilities,speech recognition,Speech
                  recognition,temporal variability,Training},
  author       = {Hinton, G. and Deng, L. and Yu, D. and Dahl, G. E. and
                  Mohamed, A. and Jaitly, N. and Senior, A. and Vanhoucke, V.
                  and Nguyen, P. and Sainath, T. N. and Kingsbury, B.}
}

@article{HinOsiTeh06,
  author  = {Geoffrey E. Hinton and Simon Osindero and Yee-Whye Teh},
  title   = {A fast Learning Algorithm for Deep Belief Nets},
  journal = {Neural Computation},
  volume  = {18},
  pages   = {1527--1554},
  year    = {2006}
}

@techreport{HinSejAck84,
  author      = {G. E. Hinton and T. J. Sejnowski and D. H. Ackley},
  title       = {Boltzmann Machines: Constraint Satisfaction Networks that
                 Learn},
  institution = {CMU Computer Science Department},
  year        = {1984},
  number      = {CMS-CS-84-119},
  month       = may,
  comment     = {Uses simulated annealing to update propositional states in
                 a connectionist Hopfield-like neural network.}
}

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  title   = {Improving neural networks by preventing co-adaptation of
             feature detectors},
  author  = {Hinton, Geoffrey E. and Srivastava, Nitish and Krizhevsky,
             Alex and Sutskever, Ilya and R. Salakhutdinov, Ruslan},
  journal = {arXiv:1207.0580},
  year    = {2012}
}

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  year    = {2002},
  volume  = {14},
  number  = {8},
  pages   = {1771--1800}
}

@article{hinton2012deep,
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  author  = {Hinton, Geoffrey and Deng, Li and Yu, Dong and Dahl,
             George and Mohamed, Abdel-rahman and Jaitly, Navdeep and
             Senior, Andrew and Vanhoucke, Vincent and Nguyen, Patrick
             and Kingsbury, Brian and others},
  journal = {IEEE Signal processing magazine},
  volume  = {29},
  year    = {2012}
}

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             Senior and Vincent Vanhoucke and Patrick Nguyen and Tara N
             Sainath},
  title   = {Deep neural networks for acoustic modeling in speech
             recognition: The shared views of four research groups},
  journal = {IEEE Signal Processing Magazine},
  year    = {2012},
  volume  = {29},
  pages   = {82--97},
  number  = {6}
}

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@book{Hiriart-Urruty_ConvexAnalysis_2011,
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  author    = {Hiriart-Urruty, Jean-Baptiste and Lemaréchal, Claude},
  year      = {2011},
  note      = {OCLC: 751526498},
  file      = {Hiriart-Urruty, Lemaréchal - Convex analysis and
               minimization algorithms I
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@incollection{Hiriart-Urruty_ConvexOptimization_1989,
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  isbn      = {978-1-4757-6019-4},
  abstract  = {Nonconvex minimization problems form an old subject which
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               difference of convex functions, maximizing a convex
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  booktitle = {Nonsmooth {Optimization} and {Related} {Topics}},
  publisher = {Springer},
  author    = {Hiriart-Urruty, Jean-Baptiste},
  editor    = {Clarke, Francis H. and Dem’yanov, V. F. and Giannessi,
               F.},
  year      = {1989},
  doi       = {10.1007/978-1-4757-6019-4_13},
  keywords  = {DC programming},
  pages     = {219--239},
  file      = {Hiriart-Urruty - From convex optimization to nonconvex
               optimization
               (1989).pdf:/Users/zcranko/Documents/Library/Book
               Section/Hiriart-Urruty - From convex optimization to
               nonconvex optimization (1989).pdf:application/pdf}
}

@article{Hiriart-Urruty_ConvexSolutions_2007,
  title    = {Convex solutions of a functional equation arising in
              information theory},
  volume   = {328},
  issn     = {0022247X},
  doi      = {10.1016/j.jmaa.2006.06.035},
  language = {en},
  number   = {2},
  journal  = {Journal of Mathematical Analysis and Applications},
  author   = {Hiriart-Urruty, J.-B. and Martínez-Legaz, J.-E.},
  month    = apr,
  year     = {2007},
  pages    = {1309--1320},
  file     = {Convex solutions of a functional equation arising in
              information
              theory:/Users/zcranko/Documents/Zotero/storage/TWLWIVBF/hiriart-urruty2007.html:text/html;Full
              Text:/Users/zcranko/Documents/Zotero/storage/XGMXGIN8/Hiriart-Urruty
              and Martínez-Legaz - 2007 - Convex solutions of a
              functional equation arising .pdf:application/pdf}
}

@book{Hiriart-Urruty_FundamentalsConvex_2004,
  address   = {Berlin, Germany},
  edition   = {2},
  series    = {Grundlehren {Text} {Editions}},
  title     = {Fundamentals of convex analysis},
  isbn      = {978-3-540-42205-1},
  publisher = {Springer-Verlag},
  author    = {Hiriart-Urruty, Jean-Baptiste and Lemaréchal, Claude},
  year      = {2004},
  note      = {OCLC: 249796506},
  file      = {Hiriart-Urruty, Lemaréchal - Fundamentals of convex
               analysis
               (2004).pdf:/Users/zcranko/Documents/Library/Book/Hiriart-Urruty,
               Lemaréchal - Fundamentals of convex analysis
               (2004).pdf:application/pdf}
}

@article{Hiriart-Urruty_GeneralFormula_1986,
  title    = {A general formula on the conjugate of the difference of
              functions},
  volume   = {29},
  issn     = {0008-4395, 1496-4287},
  doi      = {10.4153/cmb-1986-076-7},
  abstract = {Abstract
              
              Given an arbitrary function g :X → (-∞, +∞] and a
              lowersemicontinuous convex function h:X → (-∞, +∞],
              we give the general expression of the conjugate
              
              (g — h) *
              
              of g - h in terms of
              
              g *
              
              and
              
              h *
              
              . As a consequence, we get Toland's duality theorem:},
  language = {en},
  number   = {4},
  journal  = {Canadian Mathematical Bulletin},
  author   = {Hiriart-Urruty, J.-B.},
  month    = dec,
  year     = {1986},
  keywords = {Toland duality formula, DC programming},
  pages    = {482--485},
  file     = {Hiriart-Urruty - A general formula on the conjugate of the
              difference of functions
              (1986).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Hiriart-Urruty - A general formula on the conjugate
              of the difference of functions (1986).pdf:application/pdf}
}

@article{Hiriart-Urruty_LimitingBehaviour_1982,
  title    = {Limiting behaviour of the approximate first order and
              second order directional derivatives for a convex
              function},
  volume   = {6},
  issn     = {0362546X},
  doi      = {10.1016/0362-546x(82)90106-7},
  language = {en},
  number   = {12},
  journal  = {Nonlinear Analysis: Theory, Methods \& Applications},
  author   = {Hiriart-Urruty, Jean-Baptiste},
  month    = dec,
  year     = {1982},
  pages    = {1309--1326},
  file     = {Hiriart-Urruty - Limiting behaviour of the approximate
              first order and second order directional
              (1982).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Hiriart-Urruty - Limiting behaviour of the
              approximate first order and second order directional
              (1982).pdf:application/pdf}
}

@article{Hiriart-Urruty_NewFormulas_2003,
  title    = {New formulas for the {Legendre}–{Fenchel} transform},
  volume   = {288},
  issn     = {0022247X},
  doi      = {10.1016/j.jmaa.2003.09.012},
  language = {en},
  number   = {2},
  journal  = {Journal of Mathematical Analysis and Applications},
  author   = {Hiriart-Urruty, J.-B. and Martı́nez-Legaz, J.-E.},
  month    = dec,
  year     = {2003},
  pages    = {544--555},
  file     = {Hiriart-Urruty, Martı́nez-Legaz - New formulas for the
              Legendre–Fenchel transform
              (2003).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Hiriart-Urruty, Martı́nez-Legaz - New formulas
              for the Legendre–Fenchel transform
              (2003).pdf:application/pdf}
}

@inproceedings{Hiriart-Urruty_NoteLegendreFenchel_2006,
  address   = {Boston, MA, USA},
  title     = {A note on the {Legendre}-{Fenchel} transform of convex
               composite functions},
  volume    = {12},
  isbn      = {978-0-387-29195-6},
  abstract  = {In this note we present a short and clear-cut proof of the
               formula giving the Legendre-Fenchel transform of a convex
               composite function g o (f1 ..., fm) in terms of the
               transform of g and those of the fi′s.},
  booktitle = {Nonsmooth {Mechanics} and {Analysis}},
  publisher = {Springer Science+Business Media},
  author    = {Hiriart-Urruty, Jean-Baptiste},
  editor    = {Alart, P. and Maisonneuve, O. and Rockafellar, Ralph
               Tyrrell},
  year      = {2006},
  pages     = {35--46},
  file      = {Hiriart-Urruty - 2006 - A note on the legendre-fenchel
               transform of
               convex.pdf:/Users/zcranko/Documents/Zotero/storage/94SX8KA3/Hiriart-Urruty
               - 2006 - A note on the legendre-fenchel transform of
               convex.pdf:application/pdf}
}

@article{Hiriart-Urruty_SubdifferentialCalculus_1993,
  title   = {Subdifferential calculus using ε-subdifferentials},
  volume  = {118},
  issn    = {0022-1236},
  doi     = {10.1006/jfan.1993.1141},
  number  = {1},
  journal = {Journal of Functional Analysis},
  author  = {Hiriart-Urruty, Jean-Baptiste and Phelps, Robert R.},
  year    = {1993},
  note    = {tex.fjournal: Journal of Functional Analysis tex.mrclass:
             49J52 (46N10) tex.mrnumber: 1245600 tex.mrreviewer:
             Dominikus Noll},
  pages   = {154--166},
  file    = {Hiriart-Urruty, Phelps - Subdifferential calculus using
             ε-subdifferentials
             (1993).pdf:/Users/zcranko/Documents/Library/Journal
             Article/Hiriart-Urruty, Phelps - Subdifferential calculus
             using ε-subdifferentials (1993).pdf:application/pdf}
}

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@book{HirLem01,
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  volume    = {305 and 306}
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              constants to limit the susceptibility of neural networks to
              adversarial examples. We analyze recently proposed methods
              for computing the Lipschitz constant. We show that the
              Lipschitz constant may indeed enable adversarially robust
              neural networks. However, the methods currently employed
              for computing it suffer from theoretical and practical
              limitations. We argue that addressing this shortcoming is a
              promising direction for future research into certified
              adversarial defenses.},
  journal  = {arXiv:1807.09705 [cs, stat]},
  author   = {Huster, Todd and Chiang, Cho-Yu Jason and Chadha, Ritu},
  month    = jul,
  year     = {2018},
  note     = {arXiv: 1807.09705},
  keywords = {⛔ No DOI found}
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             matrix for {L}aplacian smoothing splines},
  journal = {Commun.~Statist.-Simula.},
  year    = {1989},
  volume  = {18},
  pages   = {1059--1076}
}

@article{HutLeg06,
  author  = {M. Hutter and Shane Legg},
  title   = {Fitness Uniform Optimization},
  journal = {IEEE Transactions on Evolutionary Computation},
  volume  = {10},
  number  = {5},
  pages   = {568--589},
  month   = oct,
  year    = {2006},
  pdf     = {http://www.hutter1.net/ai/fuo.pdf},
  ps      = {http://www.hutter1.net/ai/fuo.ps},
  doi     = {10.1109/TEVC.2005.863127},
  issn    = {1089-778X}
}

@article{HutLegVit07,
  author   = {Marcus Hutter and Shane Legg and Paul M. B. Vit{\'a}nyi},
  title    = {Algorithmic Probability},
  journal  = {Scholarpedia},
  volume   = {2},
  number   = {8},
  pages    = {2572},
  month    = aug,
  year     = {2007},
  bibtex   = {http://www.hutter1.net/official/bib.htm#algprob},
  http     = {http://www.scholarpedia.org/article/Algorithmic_Probability},
  pdf      = {http://www.hutter1.net/ai/algprob.pdf},
  ps       = {http://www.hutter1.net/ai/algprob.ps},
  project  = {http://www.hutter1.net/official/projects.htm#ait},
  keywords = {algorithmic information theory, algorithmic complexity,
              discrete/continuous algorithmic probability, Bayes, Occam,
              Epicurus, applications, references},
  abstract = {Algorithmic ``Solomonoff'' Probability (AP) assigns to
              objects an a priori probability that is in some sense
              universal. This prior distribution has theoretical
              applications in a number of areas, including inductive
              inference theory and the time complexity analysis of
              algorithms. Its main drawback is that it is not computable
              and thus can only be approximated in practice}
}

@article{HutLoPog94,
  author  = {J.~Hutchinson and A.~Lo and T.~Poggio},
  title   = {A nonparametric approach to pricing and hedging derivative
             securities via learning networks},
  journal = {The Journal of Finance},
  year    = {1994},
  volume  = {XLIX},
  number  = {3},
  month   = jul
}

@article{HutMuc07,
  author   = {Marcus Hutter and Andrej A. Muchnik},
  title    = {On Semimeasures Predicting {Martin-L{\"o}f} Random
              Sequences},
  journal  = {Theoretical Computer Science},
  volume   = {382},
  number   = {3},
  pages    = {247--261},
  month    = sep,
  year     = {2007},
  bibtex   = {http://www.hutter1.net/official/bib.htm#mlconvxx},
  xhttp    = {http://www.hutter1.net/ai/mlconvxx.htm},
  url      = {http://arxiv.org/abs/0708.2319},
  pdf      = {http://www.hutter1.net/ai/mlconvxx.pdf},
  ps       = {http://www.hutter1.net/ai/mlconvxx.ps},
  latex    = {http://www.hutter1.net/ai/mlconvxx.tex},
  slides   = {http://www.hutter1.net/ai/smlconvx.pdf},
  project  = {http://www.hutter1.net/official/projects.htm#ait},
  doi      = {10.1016/j.tcs.2007.03.040},
  issn     = {0304-3975},
  keywords = {Sequence prediction; Algorithmic Information Theory;
              universal enumerable semimeasure; mixture distributions;
              posterior convergence; Martin-L{\"o}f randomness;
              quasimeasures.},
  abstract = {Solomonoff's central result on induction is that the
              posterior of a universal semimeasure M converges rapidly
              and with probability 1 to the true sequence generating
              posterior mu, if the latter is computable. Hence, M is
              eligible as a universal sequence predictor in case of
              unknown mu. Despite some nearby results and proofs in the
              literature, the stronger result of convergence for all
              (Martin-Loef) random sequences remained open. Such a
              convergence result would be particularly interesting and
              natural, since randomness can be defined in terms of M
              itself. We show that there are universal semimeasures M
              which do not converge for all random sequences, i.e. we
              give a partial negative answer to the open problem. We also
              provide a positive answer for some non-universal
              semimeasures. We define the incomputable measure D as a
              mixture over all computable measures and the enumerable
              semimeasure W as a mixture over all enumerable
              nearly-measures. We show that W converges to D and D to mu
              on all random sequences. The Hellinger distance measuring
              closeness of two distributions plays a central role.},
  support  = {SNF grant 2100-67712 and RFBF grants N04-01-00427 and
              N02-01-22001}
}

@proceedings{HutSerTak07,
  editor    = {Marcus Hutter and Rocco A. Servedio and Eiji Takimoto},
  title     = {Algorithmic Learning Theory},
  subtitle  = {18th International Conference ({ALT'07})},
  publisher = {Springer, Berlin},
  address   = {Sendai},
  series    = {LNAI},
  volume    = {4754},
  month     = oct,
  year      = {2007},
  bibtex    = {http://www.hutter1.net/official/bib.htm#altproc},
  http      = {http://www.springer.com/west/home?SGWID=4-102-22-173760307-0},
  pdf       = {http://www.hutter1.net/ai/altproc.pdf},
  project   = {http://www.hutter1.net/official/projects.htm#other},
  xdoi      = {??},
  issn      = {0302-9743},
  isbn      = {3-540-75224-2},
  keywords  = {algorithmic learning theory, query models, online
               learning, inductive inference, boosting, kernel methods,
               complexity and learning, reinforcement learning,
               unsupervised learning, grammatical inference, algorithmic
               forecasting.},
  abstract  = {The LNAI series reports state-of-the-art results in
               artificial intelligence research, development, and
               education. This volume (LNAI 4754) contains research papers
               presented at the 18th International Conference on
               Algorithmic Learning Theory (ALT 2007), which was held in
               Sendai (Japan) during October 1-4, 2007. The main objective
               of the conference was to provide an interdisciplinary forum
               for high-quality talks with a strong theoretical background
               and scientific interchange in areas such as query models,
               online learning, inductive inference, boosting, kernel
               methods, complexity and learning, reinforcement learning,
               unsupervised learning, grammatical inference, and
               algorithmic forecasting. The conference was co-located with
               the 10th International Conference on Discovery Science (DS
               2007). The volume includes 25 technical contributions that
               were selected from 50 submissions, and five invited talks
               presented to the audience of ALT and DS. Longer versions of
               the DS invited papers are available in the proceedings of
               DS 2007.},
  znote     = {Acceptance rate: 25/50 = 50\%}
}

@inproceedings{HutSerTak07b,
  author    = {Marcus Hutter and Rocco A. Servedio and Eiji Takimoto},
  title     = {Algorithmic Learning Theory 2007: Editors' Introduction},
  booktitle = alt,
  address   = {Sendai},
  series    = {LNAI},
  volume    = {4754},
  editor    = {Marcus Hutter and Rocco A. Servedio and Eiji Takimoto},
  publisher = {Springer, Berlin},
  pages     = {1--8},
  month     = oct,
  year      = {2007},
  bibtex    = {http://www.hutter1.net/official/bib.htm#altintro},
  pdf       = {http://www.hutter1.net/ai/altintro.pdf},
  ps        = {http://www.hutter1.net/ai/altintro.ps},
  latex     = {http://www.hutter1.net/ai/altintro.tex},
  slides    = {http://www.hutter1.net/ai/saltintro.pdf},
  project   = {http://www.hutter1.net/official/projects.htm#other},
  issn      = {0302-9743},
  isbn      = {3-540-75224-2},
  doi       = {??},
  keywords  = {algorithmic learning theory, query models, online
               learning, inductive inference, boosting, kernel methods,
               complexity and learning, reinforcement learning,
               unsupervised learning, grammatical inference, algorithmic
               forecasting.},
  abstract  = {Learning theory is an active research area that
               incorporates ideas, problems, and techniques from a wide
               range of disciplines including statistics, artificial
               intelligence, information theory, pattern recognition, and
               theoretical computer science. The research reported at the
               18th International Conference on Algorithmic Learning
               Theory (ALT 2007) ranges over areas such as unsupervised
               learning, inductive inference, complexity and learning,
               boosting and reinforcement learning, query learning models,
               grammatical inference, online learning and defensive
               forecasting, and kernel methods. In this introduction we
               give an overview of the five invited talks and the regular
               contributions of ALT 2007.}
}

@article{Hutter06,
  author  = {M. Hutter},
  title   = {On Generalized Computable Universal Priors and their
             Convergence},
  journal = {Theoretical Computer Science},
  volume  = {364},
  number  = {1},
  pages   = {27--41},
  month   = nov,
  year    = {2006},
  bibtex  = {http://www.hutter1.net/official/bib.htm#unipriorx},
  pdf     = {http://www.hutter1.net/ai/unipriorx.pdf},
  ps      = {http://www.hutter1.net/ai/unipriorx.ps},
  doi     = {10.1016/j.tcs.2006.07.039},
  issn    = {0304-3975}
}

@inproceedings{Hutter06discount,
  author    = {M. Hutter},
  title     = {General Discounting versus Average Reward},
  booktitle = {Proc. 17th International Conf. on Algorithmic Learning
               Theory ({ALT'06})},
  address   = {Barcelona},
  series    = {LNAI},
  volume    = {4264},
  editor    = {Jose L. Balcázar and Phil Long and Frank Stephan},
  publisher = {Springer, Berlin},
  pages     = {244--258},
  month     = oct,
  year      = {2006},
  pdf       = {http://www.hutter1.net/ai/discount.pdf},
  ps        = {http://www.hutter1.net/ai/discount.ps},
  issn      = {0302-9743},
  isbn      = {3-540-46649-5},
  doi       = {10.1007/11894841_21}
}

@article{Hutter07,
  author   = {Marcus Hutter},
  title    = {Exact {B}ayesian Regression of Piecewise Constant
              Functions},
  journal  = {Bayesian Analysis},
  volume   = {2},
  number   = {4},
  pages    = {635--664},
  month    = dec,
  year     = {2007},
  bibtex   = {http://www.hutter1.net/official/bib.htm#pcregx},
  url      = {http://arxiv.org/abs/math.ST/0606315},
  pdf      = {http://www.hutter1.net/ai/pcregx.pdf},
  ps       = {http://www.hutter1.net/ai/pcregx.ps},
  latex    = {http://www.hutter1.net/ai/pcregx.tex},
  slides   = {http://www.hutter1.net/ai/spcreg.pdf},
  project  = {http://www.hutter1.net/official/projects.htm#bayes},
  code     = {http://www.hutter1.net/ai/cpcreg.zip},
  keywords = {Bayesian regression, exact polynomial algorithm,
              non-parametric inference, piecewise constant function,
              dynamic programming, change point problem.},
  abstract = {We derive an exact and efficient Bayesian regression
              algorithm for piecewise constant functions of unknown
              segment number, boundary locations, and levels. The
              derivation works for any noise and segment level prior,
              e.g.\ Cauchy which can handle outliers. We derive simple
              but good estimates for the in-segment variance. We also
              propose a Bayesian regression curve as a better way of
              smoothing data without blurring boundaries. The Bayesian
              approach also allows straightforward determination of the
              evidence, break probabilities and error estimates, useful
              for model selection and significance and robustness
              studies. We discuss the performance on synthetic and
              real-world examples. Many possible extensions are
              discussed.}
}

@article{Hutter07b,
  author   = {Marcus Hutter},
  title    = {On Universal Prediction and {B}ayesian Confirmation},
  journal  = {Theoretical Computer Science},
  volume   = {384},
  number   = {1},
  pages    = {33--48},
  month    = sep,
  year     = {2007},
  bibtex   = {http://www.hutter1.net/official/bib.htm#uspx},
  url      = {http://arxiv.org/abs/0709.1516},
  pdf      = {http://www.hutter1.net/ai/uspx.pdf},
  ps       = {http://www.hutter1.net/ai/uspx.ps},
  latex    = {http://www.hutter1.net/ai/uspx.tex},
  slides   = {http://www.hutter1.net/ai/susp.pdf},
  project  = {http://www.hutter1.net/official/projects.htm#uai},
  doi      = {10.1016/j.tcs.2007.05.016},
  issn     = {0304-3975},
  keywords = {Sequence prediction, Bayes, Solomonoff prior, Kolmogorov
              complexity, Occam's razor, prediction bounds, model
              classes, philosophical issues, symmetry principle,
              confirmation theory, reparametrization invariance,
              old-evidence/updating problem, (non)computable
              environments.},
  abstract = {The Bayesian framework is a well-studied and successful
              framework for inductive reasoning, which includes
              hypothesis testing and confirmation, parameter estimation,
              sequence prediction, classification, and regression. But
              standard statistical guidelines for choosing the model
              class and prior are not always available or fail, in
              particular in complex situations. Solomonoff completed the
              Bayesian framework by providing a rigorous, unique, formal,
              and universal choice for the model class and the prior. We
              discuss in breadth how and in which sense universal
              (non-i.i.d.) sequence prediction solves various
              (philosophical) problems of traditional Bayesian sequence
              prediction. We show that Solomonoff's model possesses many
              desirable properties: Strong total and weak instantaneous
              bounds, and in contrast to most classical continuous prior
              densities has no zero p(oste)rior problem, i.e. can confirm
              universal hypotheses, is reparametrization and regrouping
              invariant, and avoids the old-evidence and updating
              problem. It even performs well (actually better) in
              non-computable environments.}
}

@inproceedings{Hutter07c,
  author    = {Marcus Hutter},
  title     = {{B}ayesian Regression of Piecewise Constant Functions},
  booktitle = {Proc. ISBA 8th International Meeting on Bayesian
               Statistics},
  address   = {Benidorm},
  editor    = {J.M. Bernardo and M.J. Bayarri and J.O. Berger and A.P.
               David and D. Heckerman and A.F.M. Smith and M. West},
  publisher = {Oxford University Press},
  pages     = {607--612},
  month     = jul,
  year      = {2007},
  bibtex    = {http://www.hutter1.net/official/bib.htm#pcreg},
  url       = {http://arxiv.org/abs/math.ST/0606315},
  pdf       = {http://www.hutter1.net/ai/pcreg.pdf},
  ps        = {http://www.hutter1.net/ai/pcreg.ps},
  latex     = {http://www.hutter1.net/ai/pcreg.tex},
  slides    = {http://www.hutter1.net/ai/spcreg.pdf},
  project   = {http://www.hutter1.net/official/projects.htm#bayes},
  ccode     = {http://www.hutter1.net/ai/pcreg.cpp},
  rcode     = {http://www.hutter1.net/ai/cpcreg.zip},
  xdoi      = {http://www.oup.com/uk/catalogue/?ci=9780199214655},
  isbn      = {978-0-19-921465-5},
  abstract  = {We derive an exact and efficient Bayesian regression
               algorithm for piecewise constant functions of unknown
               segment number, boundary location, and levels. It works for
               any noise and segment level prior, e.g.\ Cauchy which can
               handle outliers. We derive simple but good estimates for
               the in-segment variance. We also propose a Bayesian
               regression curve as a better way of smoothing data without
               blurring boundaries. The Bayesian approach also allows
               straightforward determination of the evidence, break
               probabilities and error estimates, useful for model
               selection and significance and robustness studies. We
               briefly mention the performance on synthetic and real-world
               examples. The full version of the paper contains detailed
               derivations, more motivation and discussion, the complete
               algorithm, the experiments, and various extensions.},
  keywords  = {Bayesian regression, exact polynomial algorithm,
               non-parametric inference, piecewise constant function,
               dynamic programming, change point problem.},
  znote     = {Acceptance rate: 19/326 = 6\%}
}

@inproceedings{Hutter07d,
  author    = {Marcus Hutter},
  title     = {The Loss Rank Principle for Model Selection},
  booktitle = {Proc. 20th Annual Conf. on Learning Theory ({COLT'07})},
  address   = {San Diego},
  series    = {LNAI},
  volume    = {4539},
  editor    = {N. Bshouty and C. Gentile},
  publisher = {Springer, Berlin},
  pages     = {589--603},
  month     = jun,
  year      = {2007},
  bibtex    = {http://www.hutter1.net/official/bib.htm#lorp},
  url       = {http://arxiv.org/abs/math.ST/0702804},
  pdf       = {http://www.hutter1.net/ai/lorp.pdf},
  ps        = {http://www.hutter1.net/ai/lorp.ps},
  latex     = {http://www.hutter1.net/ai/lorp.tex},
  slides    = {http://www.hutter1.net/ai/slorp.pdf},
  project   = {http://www.hutter1.net/official/projects.htm#mdl},
  doi       = {10.1007/978-3-540-72927-3_42},
  issn      = {0302-9743},
  keywords  = {Model selection, loss rank principle, non-parametric
               regression, classification general loss function, k nearest
               neighbors.},
  abstract  = {A key issue in statistics and machine learning is to
               automatically select the ``right'' model complexity, e.g.\
               the number of neighbors to be averaged over in k nearest
               neighbor (kNN) regression or the polynomial degree in
               regression with polynomials. We suggest a novel principle
               (LoRP) for model selection in regression and
               classification. It is based on the loss rank, which counts
               how many other (fictitious) data would be fitted better.
               LoRP selects the model that has minimal loss rank. Unlike
               most penalized maximum likelihood variants (AIC,BIC,MDL),
               LoRP only depends on the regression functions and the loss
               function. It works without a stochastic noise model, and is
               directly applicable to any non-parametric regressor, like
               kNN.},
  znote     = {Acceptance rate: 41/92 = 45\%}
}

@article{Hutter07e,
  author   = {Marcus Hutter},
  title    = {Algorithmic Information Theory: a Brief Non-Technical
              Guide to the Field},
  journal  = {Scholarpedia},
  volume   = {2},
  number   = {3},
  pages    = {2519},
  month    = mar,
  year     = {2007},
  bibtex   = {http://www.hutter1.net/official/bib.htm#ait},
  http     = {http://www.scholarpedia.org/article/Algorithmic_Information_Theory},
  url      = {http://arxiv.org/abs/cs.IT/0703024},
  pdf      = {http://www.hutter1.net/ai/ait.pdf},
  ps       = {http://www.hutter1.net/ai/ait.ps},
  latex    = {http://www.hutter1.net/ai/ait.zip},
  project  = {http://www.hutter1.net/official/projects.htm#ait},
  keywords = {Algorithmic information theory, algorithmic ``Kolmogorov''
              complexity, algorithmic ``Solomonoff'' probability,
              universal ``Levin'' search, algorithmic ``Martin-Loef''
              randomness, applications, history, references, notation,
              nomenclature, map.},
  abstract = {This article is a brief guide to the field of algorithmic
              information theory (AIT), its underlying philosophy, and
              the most important concepts. AIT arises by mixing
              information theory and computation theory to obtain an
              objective and absolute notion of information in an
              individual object, and in so doing gives rise to an
              objective and robust notion of randomness of individual
              objects. This is in contrast to classical information
              theory that is based on random variables and communication,
              and has no bearing on information and randomness of
              individual objects. After a brief overview, the major
              subfields, applications, history, and a map of the field
              are presented.}
}

@incollection{Hutter07f,
  author     = {Marcus Hutter},
  title      = {Universal Algorithmic Intelligence: A Mathematical
                Top$\rightarrow$Down Approach},
  booktitle  = {Artificial General Intelligence},
  editor     = {B. Goertzel and C. Pennachin},
  publisher  = {Springer},
  address    = {Berlin},
  series     = {Cognitive Technologies},
  pages      = {227--290},
  month      = jan,
  year       = {2007},
  bibtex     = {http://www.hutter1.net/official/bib.htm#aixigentle},
  http       = {http://www.hutter1.net/ai/aixigentle.htm},
  url        = {http://arxiv.org/abs/cs.AI/0701125},
  ftp        = {http://www.idsia.ch/idsiareport/IDSIA-01-03.ps.gz},
  pdf        = {http://www.hutter1.net/ai/aixigentle.pdf},
  ps         = {http://www.hutter1.net/ai/aixigentle.ps},
  latex      = {http://www.hutter1.net/ai/aixigentle.tex},
  slides     = {http://www.hutter1.net/ai/skcunai.pdf},
  project    = {http://www.hutter1.net/official/projects.htm#uai},
  press      = {http://www.hutter1.net/official/press.htm#uaibook},
  isbn       = {3-540-23733-X},
  categories = {I.2. [Artificial Intelligence]},
  keywords   = {Artificial intelligence; algorithmic probability;
                sequential decision theory; rational agents; value
                function; Solomonoff induction; Kolmogorov complexity;
                reinforcement learning; universal sequence prediction;
                strategic games; function minimization; supervised
                learning.},
  abstract   = {Decision theory formally solves the problem of rational
                agents in uncertain worlds if the true environmental prior
                probability distribution is known. Solomonoff's theory of
                universal induction formally solves the problem of sequence
                prediction for unknown prior distribution. We combine both
                ideas and get a parameter-free theory of universal
                Artificial Intelligence. We give strong arguments that the
                resulting AIXI model is the most intelligent unbiased agent
                possible. We outline for a number of problem classes,
                including sequence prediction, strategic games, function
                minimization, reinforcement and supervised learning, how
                the AIXI model can formally solve them. The major drawback
                of the AIXI model is that it is uncomputable. To overcome
                this problem, we construct a modified algorithm AIXI$tl$
                that is still effectively more intelligent than any other
                time $t$ and length $l$ bounded agent. The computation time
                of AIXI$tl$ is of the order $t \cdot 2^l$. Other discussed
                topics are formal definitions of intelligence order
                relations, the horizon problem and relations of the AIXI
                theory to other AI approaches.}
}

@inproceedings{HuWel00,
  author    = {Junling Hu and M. P. Wellman},
  title     = {Experimental Results on {Q}-Learning for General-Sum
               Stochastic Games},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {407--414},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{HuWel98,
  author    = {Junling Hu and M. P. Wellman},
  title     = {Multiagent reinforcement learning: theoretical framework
               and an algorithm},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {242--250},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{HuYanHuaHanetal05,
  author  = {H. Hu and X. Yan and Y. Huang and J. Han and X. J. Zhou},
  title   = {Mining coherent dense subgraphs across massive biological
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  journal = {Bioinformatics},
  year    = {2005},
  volume  = {21 Suppl 1},
  pages   = {i213-i221},
  month   = {Jun}
}

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  author  = {A. Hyv\"arinen},
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  journal = {{IEEE} Transactions on Neural Networks},
  year    = {2001},
  note    = {To Appear}
}

@inproceedings{Hyvarinen97,
  author    = {A. Hyv\"arinen},
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@inproceedings{Hyvarinen98,
  author    = {A. Hyv\"arinen},
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  booktitle = {Advances in Neural Information Processing Systems 11},
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  editor    = {M.~S. Kearns and S.~A. Solla and D.~A. Cohn},
  volume    = {10},
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  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{Hyvarinen99,
  author  = {A. Hyv{\"a}rinen},
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  year    = {1999},
  volume  = {2},
  pages   = {94--128}
}

@book{HyvKarOja01,
  title     = {Independent Component Analysis},
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  year      = {2001},
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}

@article{HyvOja00,
  author  = {A. Hyv\"arinen and E. Oja},
  title   = {Independent Component Analysis: Algorithms and
             Applications},
  journal = {Neural Networks},
  year    = {2000},
  volume  = {13},
  pages   = {411--430},
  number  = {4-5}
}

@article{HyvOja98,
  author  = {A. Hyv\"arinen and E. Oja},
  title   = {Independent Component Analysis by General Nonlinear
             Hebbian-Like Learning Rules},
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  year    = {1998},
  volume  = {64},
  pages   = {301--313},
  number  = {3}
}

@article{HyvPaj99,
  author  = {A. Hyv\"arinen and P. Pajunen},
  title   = {Nonlinear Independent Component Analysis: Existence and
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@article{Ioffe_DualityConvex_1968,
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  abstract = {Let be a real linear topological space and its conjugate.
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              element . For real functions on we introduce two
              operations: the ordinary sum
              
              and the convolution
              
              and also the transformation associating with its dual
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              The following propositions hold.1) The operation is
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              if and only if is a convex function and lower
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@inproceedings{JaaHau99,
  author    = {T.~S. Jaakkola and D.~Haussler},
  editors   = {D. Heckerman and J. Whittaker},
  title     = {Probabilistic Kernel Regression Models},
  booktitle = {the 7th International Workshop on {AI} and Statistics},
  publisher = {Morgan Kaufmann},
  address   = {San Francisco, CA},
  year      = {1999}
}

@inproceedings{JaaHau99b,
  author    = {T.~S. Jaakkola and D.~Haussler},
  title     = {Exploiting generative models in discriminative
               classifiers},
  booktitle = {Advances in Neural Information Processing Systems 11},
  year      = {1999},
  editor    = {M.~S. Kearns and S.~A. Solla and D.~A. Cohn},
  pages     = {487--493},
  publisher = {{MIT} Press}
}

@inproceedings{JaaJor96,
  author    = {T.~S. Jaakkola and M.~I. Jordan},
  title     = {Computing upper and lower bounds on likelihoods in
               untractable networks},
  booktitle = {the 12th Conference on Uncertainty in {AI}},
  year      = {1996},
  publisher = {Morgan Kaufmann Publishers}
}

@article{JaaJor99,
  author  = {T. S. Jaakkola and M.~I. Jordan},
  title   = {Variational Probabilistic Inference and the {QMR-DT}
             Network},
  journal = {Journal of Artificial Intelligence Research},
  year    = {1999},
  volume  = {10},
  pages   = {291--322}
}

@inproceedings{JaaMeiJeb00,
  author    = {T. Jaakkola and M. Meila and T. Jebara},
  title     = {Maximum Entropy Discrimination},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A.~Solla and T.~K.~Leen and K.-R.~M{\"u}ller},
  pages     = {470--476},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@techreport{JaaMeiJeb99,
  author      = {T.~Jaakkola and M.~Meil{\u{a}} and T.~Jebara},
  title       = {Maximum entropy discrimination},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1999},
  number      = {AITR-1668}
}

@inproceedings{JacCra96,
  author    = {Jeffrey C.~Jackson and Mark W.~Craven},
  title     = {Learning Sparse Perceptrons},
  booktitle = {Advances in Neural Information Processing Systems 8},
  year      = {1996},
  editor    = {D.~S.~Touretzky and M.~C.~Mozer and M.~E.~Hasselmo},
  pages     = {654--660},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@techreport{JacJorBar90,
  author      = {R. A. Jacobs and M. A. Jordan and A. G. Barto},
  title       = {Task decomposition through competition in a modular
                 connectionist architecture: The what and where vision
                 tasks},
  institution = {COINS},
  year        = {1990},
  number      = {90-27},
  month       = mar
}

@article{JacJorNowHin91,
  author  = {R.~A.~Jacobs and M.~I.~Jordan and S.~J.~Nowlan and
             G.~F.~Hinton},
  title   = {Adaptive Mixtures of Local Experts},
  journal = {Neural Computation},
  year    = {1991},
  volume  = {3},
  pages   = {79--87}
}

@inproceedings{Jackson00,
  author    = {Jeffrey Jackson},
  title     = {On the Efficiency of Noise-Tolerant {PAC} Algorithms
               Derived from Statistical Queries},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {7--15},
  publisher = {Morgan Kaufmann, San Francisco}
}

@article{Jackson88,
  author  = {I.~R.~Jackson},
  title   = {Convergence Properties of radial basis functions},
  journal = {Constructive approximation},
  year    = {1988},
  volume  = {4},
  pages   = {243--264}
}

@phdthesis{Jackson88b,
  author  = {I.~R.~H.~Jackson},
  title   = {Radial Basis Functions methods for multivariate
             approximation},
  school  = {University of Cambridge},
  year    = {1988},
  type    = {Ph.D.~Thesis},
  address = {U.K.}
}

@article{Jackson97,
  author  = {Jeffrey C. Jackson},
  title   = {An efficient membership-query algorithm for learning {DNF}
             with respect to the uniform distribution},
  journal = {J. Comput. Syst. Sci.},
  year    = {1997},
  volume  = {55},
  pages   = {414--440},
  number  = {3},
  note    = {Earlier version appeared in FOCS '94}
}

@inproceedings{JacOboVer09,
  author    = {Laurent Jacob and Guillaume Obozinski and Jean P. Vert},
  title     = {Group Lasso with Overlap and Graph Lasso},
  booktitle = icml,
  year      = {2009}
}

@article{Jacobs88,
  author  = {Jacobs, R.A.},
  title   = {Increased Rates of Convergence through Learning Rate
             Adaptation},
  journal = {Neural Networks},
  year    = {1988},
  volume  = {1},
  pages   = {295-307}
}

@book{JacPro00,
  title     = {Probability Essentials},
  publisher = {Springer},
  year      = {2000},
  author    = {J. Jacod and P. Protter},
  address   = {New York}
}

@inproceedings{JacTom92,
  author    = {J. Jackson and A. Tomkins},
  title     = {A computational model of teaching},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {319--326},
  publisher = {ACM Press, New York, NY}
}

@article{Jagerman69,
  author  = {D.~Jagerman},
  title   = {$\varepsilon$-Entropy and Approximation of Bandlimited
             Functions},
  journal = {SIAM Journal on Applied Mathematics},
  year    = {1969},
  volume  = {17},
  pages   = {362--377},
  number  = {2}
}

@inproceedings{Jaggi13,
  author    = {Martin Jaggi},
  title     = {Revisiting {F}rank-{W}olfe: Projection-Free Sparse Convex
               Optimization},
  booktitle = icml,
  year      = 2013
}

@inproceedings{JaggiSulovsky10,
  author    = {Martin Jaggi and Marek Sulovsky},
  title     = {A Simple Algorithm for Nuclear Norm Regularized Problems},
  booktitle = icml,
  year      = {2010}
}

@book{JaiDub88,
  title     = {Algorithms for Clustering Data},
  publisher = {Prentice Hall},
  year      = {1988},
  author    = {A.~K.~Jain and R.~C.~Dubes},
  address   = {Englewood Cliffs, NJ}
}

@inproceedings{JaiHin11,
  author    = {N. {Jaitly} and G. {Hinton}},
  booktitle = {2011 IEEE International Conference on Acoustics, Speech
               and Signal Processing (ICASSP)},
  title     = {Learning a better representation of speech soundwaves
               using restricted boltzmann machines},
  year      = {2011}
}

@article{JaiKinLanWieetal00,
  author  = {Sanjay Jain and Efim Kinber and Steffen Lange and Rolf
             Wiehagen and T. Zeugmann},
  title   = {Learning languages and functions by erasing},
  journal = {Theoret. Comput. Sci.},
  year    = {2000},
  volume  = {241},
  pages   = {143--189},
  number  = {1--2},
  note    = {Special issue for ALT '96}
}

@inproceedings{JaiKinWie00,
  author    = {Sanjay Jain and Efim Kinber and Rolf Wiehagen},
  title     = {Language Learning From Texts: {D}egrees of Instrinsic
               Complexity and Their Characterizations},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {47--58},
  publisher = {Morgan Kaufmann, San Francisco}
}

@inproceedings{JaiKinWie96,
  author    = {Sanjay Jain and Efim Kinber and Rolf Wiehagen},
  title     = {On Learning and Co-Learning of Minimal Programs},
  booktitle = alt-ws,
  year      = {1996},
  volume    = {1160},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {242--255},
  publisher = {Springer-Verlag}
}

@inproceedings{JaiKulDhiGra09,
  author    = {Prateek Jain and Brian Kulis and Inderjit S. Dhillon and
               Kristen Grauman},
  title     = {Online Metric Learning and Fast Similarity Search},
  booktitle = nips,
  year      = {2009}
}

@article{JaiLanNes01,
  author  = {Sanjay Jain and Steffen Lange and Jochen Nessel},
  title   = {On the Learnability of Recursively Enumerable Languages
             from Good Examples},
  journal = {Theoret. Comput. Sci.},
  year    = {2001},
  volume  = {261},
  pages   = {3--29},
  number  = {1},
  note    = {Special issue for ALT '97}
}

@inproceedings{JaiLanNes97,
  author    = {Sanjay Jain and Steffen Lange and Jochen Nessel},
  title     = {Learning of {r.e.} Languages from Good Examples},
  booktitle = alt-ws,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {32--47},
  publisher = {Springer-Verlag}
}

@phdthesis{Jain90,
  author = {S. Jain},
  title  = {Learning in the Presence of Additional Information and
            Inaccurate Information},
  school = {University of Rochester},
  year   = {1990}
}

@article{Jain95,
  author  = {S. Jain},
  title   = {On a Question about Learning Nearly Minimal Programs},
  journal = {Inform. Proc. Lett.},
  year    = {1995},
  volume  = {53},
  pages   = {1--4},
  number  = {1}
}

@article{Jain96,
  author  = {Sanjay Jain},
  title   = {Program Synthesis in the Presence of Infinite Number of
             Inaccuracies},
  journal = {J. Comput. Syst. Sci.},
  year    = {1996},
  volume  = {53},
  pages   = {583--591},
  number  = {3},
  note    = {First appeared in ALT '94}
}

@article{Jain97,
  author  = {Sanjay Jain},
  title   = {Strong monotonic and set-driven inductive inference},
  journal = {J. of Experimental and Theoret. Artif. Intell.},
  year    = {1997},
  volume  = {9},
  pages   = {137--143},
  number  = {1}
}

@article{Jain98,
  author  = {Sanjay Jain},
  title   = {Learning with Refutation},
  journal = {J. Comput. Syst. Sci.},
  year    = {1998},
  volume  = {57},
  pages   = {256--365},
  number  = {3},
  note    = {First appeared in ALT '98}
}

@article{Jain98b,
  author  = {Sanjay Jain},
  title   = {Minimal Concept Identification and Reliability},
  journal = {International Journal of Foundations of Computer Science},
  year    = {1998},
  volume  = {9},
  pages   = {315--320},
  number  = {3}
}

@article{Jain99,
  author  = {S. Jain},
  title   = {On a Question of Nearly Minimal Identification of
             Functions},
  journal = {Inform. Process. Lett.},
  year    = {1999},
  volume  = {70},
  pages   = {113--117},
  number  = {3}
}

@article{Jain99b,
  author  = {S. Jain},
  title   = {Robust Behaviorally Correct Learning},
  journal = {Inform. Comput.},
  year    = {1999},
  volume  = {153},
  pages   = {238--248},
  number  = {2}
}

@book{JaiOshRoySha99,
  title     = {Systems that Learn: An Introduction to Learning Theory,
               second edition},
  publisher = {{MIT} Press},
  year      = {1999},
  author    = {Sanjay Jain and Daniel Osherson and James S. Royer and
               Arun Sharma},
  address   = {Cambridge, Massachusetts},
  isbn      = {0-262-10077-0}
}

@article{JaiSha00,
  author  = {Sanjay Jain and Arun Sharma},
  title   = {Team Learning of Computable Languages},
  journal = {Theory of Computing Systems},
  year    = {2000},
  volume  = {33},
  pages   = {35--58},
  number  = {1}
}

@techreport{JaiSha89,
  author      = {S. Jain and A. Sharma},
  title       = {Knowledge of an Upper Bound on Grammar Size Helps Language
                 Learning},
  institution = {University of Rochester},
  year        = {1989},
  number      = {283}
}

@techreport{JaiSha89b,
  author      = {S. Jain and A. Sharma},
  title       = {Recursion Theoretic Characterizations of Language
                 Learning},
  institution = {University of Rochester},
  year        = {1989},
  number      = {281}
}

@inproceedings{JaiSha90,
  author    = {S. Jain and A. Sharma},
  title     = {Finite learning by a {\sl team}},
  booktitle = {Proc. 3rd Annu. Workshop on Comput. Learning Theory},
  year      = {1990},
  pages     = {163--177},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@inproceedings{JaiSha90b,
  author    = {S. Jain and A. Sharma},
  title     = {Hypothesis Formation and Language Acquisition with an
               Infinitely Often Correct Teacher},
  booktitle = {Proc. of the Third Conference on Theoretical Aspects of
               Reasoning About Knowledge},
  year      = {1990},
  pages     = {225--239},
  month     = mar,
  publisher = {Morgan Kaufmann, Inc.}
}

@inproceedings{JaiSha90c,
  author    = {S. Jain and A. Sharma},
  title     = {Language Learning by a Team},
  booktitle = {Proc. of the 17th International Colloquium on Automata,
               Languages and Programming},
  year      = {1990},
  pages     = {153--166},
  month     = jul,
  publisher = {Springer-Verlag}
}

@techreport{JaiSha90d,
  author      = {S. Jain and A. Sharma},
  title       = {Program Size Restrictions in Inductive Learning},
  institution = {University of Delaware, Newark, Delaware},
  year        = {1990},
  number      = {90-06}
}

@article{JaiSha91,
  author  = {S. Jain and A. Sharma},
  title   = {Learning in the Presence of Partial Explanations},
  journal = {Inform. Comput.},
  year    = {1991},
  volume  = {95-2},
  pages   = {162--191},
  annote  = {Also Univ. of Rochester TR 282 in 1989}
}

@inproceedings{JaiSha91b,
  author    = {S. Jain and A. Sharma},
  title     = {Restrictions on Grammar Size in Language Identification},
  booktitle = {Proc. MLNLO'91, Machine Learning of Natural Language and
               Ontology, Stanford University, California. Document {\bf
               D91-09}, DFKI: Kaiserslautern FRG, 1991},
  year      = {1991},
  pages     = {87--92},
  month     = mar
}

@techreport{JaiSha93,
  author      = {S. Jain and A. Sharma},
  title       = {Computational Limits on Team Identification of Languages},
  institution = {School of Computer Science and Engineering; University of
                 New South Wales},
  year        = {1993},
  number      = {9301}
}

@article{JaiSha93b,
  author  = {S. Jain and A. Sharma},
  title   = {Learning with the Knowledge of an Upper Bound on Program
             Size},
  journal = {Inform. Comput.},
  year    = {1993},
  volume  = {102-1},
  pages   = {118--166},
  note    = {Presented at the Workshop on Computational Learning Theory
             and Natural Learning Systems}
}

@article{JaiSha93c,
  author  = {S. Jain and A. Sharma},
  title   = {On the non-existence of maximal inference degrees for
             language identification},
  journal = {Inform. Proc. Lett.},
  year    = {1993},
  volume  = {47-2},
  pages   = {81--88}
}

@inproceedings{JaiSha93d,
  author    = {S. Jain and A. Sharma},
  title     = {Probability is more powerful than team for language
               identification from positive data},
  booktitle = {Proc. 6th Annu. Conference on Comput. Learning Theory},
  year      = {1993},
  pages     = {192--198},
  address   = {New York, New York},
  publisher = {ACM Press}
}

@article{JaiSha94,
  author  = {Sanjay Jain and Arun Sharma},
  title   = {Characterizing language identification by standardizing
             operations},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1994},
  volume  = {49},
  pages   = {96--107},
  number  = {1},
  annote  = {Was in {\sl Advances in Computing and Information, the
             International Conference on Computing and Information,
             Niagara Falls, Canada} in 1990}
}

@inproceedings{JaiSha94b,
  author    = {S. Jain and A. Sharma},
  title     = {On the intrinsic complexity of language identification},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {278--286},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{JaiSha94c,
  author    = {S. Jain and A. Sharma},
  title     = {On monotonic strategies for learning r.e.\ languages},
  booktitle = {Algorithmic Learning Theory, the 4th International
               Workshop on Analogical and Inductive Inference, AII'94 and
               5th International Workshop on Algorithmic Learning Theory,
               ALT'94, Reinhardsbrunn Castle, Germany},
  year      = {1994},
  volume    = {872},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {349--364},
  month     = oct,
  publisher = {Springer-Verlag}
}

@article{JaiSha94d,
  author  = {S. Jain and A. Sharma},
  title   = {Program Size Restrictions in Computational Learning},
  journal = {Theoret. Comput. Sci. A},
  year    = {1994},
  volume  = {127},
  pages   = {351--386}
}

@article{JaiSha95,
  author  = {S. Jain and A. Sharma},
  title   = {On Aggregating Teams of Learning Machines},
  journal = {Theoret. Comput. Sci.},
  year    = {1995},
  volume  = {137},
  pages   = {85--108},
  number  = {1},
  note    = {Special Issue for ALT '93}
}

@incollection{JaiSha95b,
  author    = {Sanjay Jain and Arun Sharma},
  title     = {On identification by teams and probabilistic machines},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {108--145}
}

@article{JaiSha95c,
  author  = {S. Jain and A. Sharma},
  title   = {Prudence in Vacillatory Language Identification},
  journal = {Math. Syst. Theory},
  year    = {1995},
  volume  = {28},
  annote  = {Earlier version in {\it Proc. of the Third Workshop on
             Algorithmic Learning Theory} in 1992}
}

@inproceedings{JaiSha95d,
  author    = {S. Jain and A. Sharma},
  title     = {The Structure of Intrinsic Complexity of Learning},
  booktitle = {Proc. of the Second European Conference on Computational
               Learning Theory, (EUROCOLT) Barcelona, Spain},
  year      = {1995},
  month     = mar,
  publisher = {Springer-Verlag}
}

@article{JaiSha97,
  author  = {Sanjay Jain and Arun Sharma},
  title   = {Elementary formal systems, intrinsic complexity, and
             procrastination},
  journal = {Inform. Comput.},
  year    = {1997},
  volume  = {132},
  pages   = {65--84},
  number  = {1},
  note    = {first in Colt '96}
}

@article{JaiSha98,
  author  = {Sanjay Jain and Arun Sharma},
  title   = {Generalization and specialization strategies for learning
             r.e. languages},
  journal = {Annals of Mathematics and Artificial Intelligence},
  year    = {1998},
  volume  = {23},
  pages   = {1--26},
  number  = {1/2},
  note    = {Special issue for AII '94 and ALT '94}
}

@inproceedings{JaiSha99,
  author    = {Sanjay Jain and Arun Sharma},
  title     = {On a generalized notion of mistake bounds},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {249--256},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{JaiSha99b,
  author    = {Sanjay Jain and Arun Sharma},
  title     = {Mind Change Complexity of Learning Logic Programs},
  booktitle = {Proc. 4th European Conference on Computational Learning
               Theory},
  year      = {1999},
  volume    = {1572},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {198--213},
  publisher = {Springer-Verlag}
}

@inproceedings{JaiShaCas89,
  author    = {S. Jain and A. Sharma and J. Case},
  title     = {Convergence to nearly minimal size grammars by vacillating
               learning machines},
  booktitle = {Proc. 2nd Annu. Workshop on Comput. Learning Theory},
  year      = {1989},
  pages     = {189--199},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{JaiShaVel95,
  author  = {Sanjay Jain and Arun Sharma and Mahendran Velauthapillai},
  title   = {Finite Identification of Functions by Teams with Success
             Ratio $\frac{1}{2}$ and Above},
  journal = {Inform. Comput.},
  year    = {1995},
  volume  = {121-2},
  pages   = {201--213}
}

@article{JaiSmiWie01,
  author  = {Sanjay Jain and Carl Smith and Rolf Wiehagen},
  title   = {Robust Learning Is Rich},
  journal = {J. of Comput. Syst. Sci.},
  year    = {2001},
  volume  = {62},
  pages   = {178--212},
  number  = {1}
}

@inproceedings{JaiSmiWie98,
  author    = {Sanjay Jain and Carl Smith and Rolf Wiehagen},
  title     = {On the power of learning robustly},
  booktitle = {Proc. 11th Annu. Conf. on Comput. Learning Theory},
  year      = {1998},
  pages     = {187--197},
  publisher = {ACM Press, New York, NY}
}

@article{JakSchWic09,
  author  = {F. J\"akel and B. Sch\"olkopf and F. Wichmann},
  title   = {Does Cognitive Science Need Kernels?},
  year    = {2009},
  volume  = {13},
  pages   = {381--388},
  number  = {9},
  journal = {Trends in Cognitive Sciences}
}

@book{Jameetal13,
  title     = { An Introduction to Statistical Learning with Applications
               in {R}},
  publisher = {Springer},
  year      = {2013},
  edition   = {2},
  author    = {Gareth James and Daniela Witten and Trevor Hastie and
               Robert Tibshirani},
  address   = {New York}
}

@inproceedings{JamSte60,
  author    = {W.~James and C.~Stein},
  title     = {Estimation with Quadratic Loss},
  booktitle = {the Fourth Berkeley Symposium on Mathematics, Statistics
               and Probability},
  year      = {1960},
  volume    = {1},
  pages     = {361--380},
  address   = {Berkeley},
  publisher = {University of California Press}
}

@article{JanBei81,
  author  = {K. Jantke and H. Beick},
  title   = {Combining Postulates of Naturalness in Inductive
             Inference},
  journal = {Elektronische Informationsverarbeitung und Kybernetik},
  year    = {1981},
  volume  = {17},
  pages   = {465--484},
  number  = {8/9}
}

@article{Janikow93,
  author    = {Cezary Z. Janikow},
  title     = {A Knowledge-Intensive Genetic Algorithm for Supervised
               Learning},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {13},
  pages     = {189--228},
  publisher = {Kluwer Academic Publishers, Boston}
}

@proceedings{JanKobTomYok93,
  title     = alt-ws,
  address   = {Tokyo, Japan},
  year      = {1993},
  editor    = {K. P. Jantke and S. Kobayashi and E. Tomita and T.
               Yokomori},
  volume    = {744},
  series    = {Lecture Notes in Artificial Intelligence},
  publisher = {Springer-Verlag, Berlin},
  month     = nov,
  isbn      = {3-540-57370-4}
}

@article{JanKulDavDhi12,
  title   = {Metric and kernel learning using a linear transformation},
  author  = {Jain, Prateek and Kulis, Brian and Davis, Jason V and
             Dhillon, Inderjit S},
  journal = jmlr,
  volume  = {13},
  number  = {Mar},
  pages   = {519--547},
  year    = {2012}
}

@article{JanLan95,
  author  = {K. P. Jantke and S. Lange},
  title   = {Case-Based Representation and Learning of Pattern
             Languages},
  journal = {Theoret. Comput. Sci.},
  year    = {1995},
  volume  = {137},
  pages   = {25--51},
  number  = {1},
  note    = {Special Issue for ALT '93}
}

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  year      = {2001},
  editor    = {C. Brodley and A. Danyluk},
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}

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  pages   = {27-59},
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}

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               Riezler},
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}

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}

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  author  = {Valen E.~Johnson},
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  year    = {1996},
  volume  = {91},
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}

@unpublished{Johnstone00,
  author = {I.~M.~Johnstone},
  title  = {On the distribution of the largest principal component},
  note   = {Presented at the 5th world congress of the Bernoulli
            society},
  year   = {2000}
}

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}

@inproceedings{JohZha13,
  title     = {Accelerating Stochastic Gradient Descent using Predictive
               Variance Reduction},
  author    = {Rie Johnson and Tong Zhang},
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}

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  author    = {V. Jojic and S. Saria and D. Koller},
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               Case against Premature Envelopment},
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  year      = {2011}
}

@book{jolliffe86,
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}

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  author = {R. Jones},
  title  = {Learning to Extract Entities from Labeled and Unlabeled
            Text},
  school = {Carnegie Mellon University},
  year   = {2005}
}

@article{Jones92,
  author  = {L.~K.~Jones},
  title   = {A simple lemma on greedy approximation in {Hilbert} space
             and convergence rates for {Projection Pursuit Regression}
             and neural network training},
  journal = {Annals of Statistics},
  year    = {1992},
  volume  = {20},
  pages   = {608--613},
  number  = {1},
  month   = mar
}

@phdthesis{Jones92b,
  author  = {M.~Jones},
  title   = {Using recurrent networks for dimensionality reduction},
  school  = {Massachussetts Institute of Technology},
  year    = {1992},
  address = {Cambridge, MA},
  month   = sep
}

@article{Jones99,
  author  = {David T. Jones},
  title   = {Protein Secondary Structure Prediction Based on
             Position-specific Scoring Matrices},
  journal = {J. Mol. Biol.},
  year    = {1999},
  volume  = {292},
  pages   = {195--202}
}

@article{Jong88,
  author    = {Kenneth De Jong},
  title     = {Learning with Genetic Algorithms: An Overview},
  journal   = {Machine Learning},
  year      = {1988},
  volume    = {3},
  pages     = {121--138},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Jong90,
  author    = {K. D. Jong},
  title     = {Special Issue on Genetic Algorithms},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  number    = {4},
  month     = oct,
  publisher = {Kluwer Academic}
}

@article{Jong93,
  author  = {S. de Jong},
  title   = {SIMPLS: An Alternative Approach to Partial Least Squares
             Regression},
  journal = {Chemometrics and Intelligent Laboratory Systems},
  year    = {1993},
  volume  = {18},
  pages   = {251--263}
}

@inproceedings{JonKan96,
  author    = {Dick de Jongh and Makoto Kanazawa},
  title     = {Angluin's theorem for indexed families of r.e. sets and
               applications},
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  year      = {1996},
  pages     = {193--204},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{JonPog95,
  author    = {M.~Jones and T.~Poggio},
  title     = {Model-based matching of line drawings by linear
               combination of prototypes},
  booktitle = {the International Conference on Computer Vision},
  year      = {1995},
  pages     = {531--536},
  month     = jun,
  publisher = {IEEE}
}

@article{JonSib87,
  author  = {M.~C.~Jones and R.~Sibson},
  title   = {What is projection pursuit?},
  journal = {J. Roy. Statist. Soc. Ser. A},
  year    = {1987},
  volume  = {150},
  pages   = {1--36},
  number  = {1}
}

@article{JonSpeGor93,
  author    = {Kenneth A. De Jong and William M. Spears and Diana F.
               Gordon},
  title     = {Using Genetic Algorithms for Concept Learning},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {13},
  pages     = {161--188},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{JonVan94,
  author    = {Randolph M. Jones and Kurt Vanlehn},
  title     = {Acquisition of Children's Addition Strategies: {A} Model
               of Impasse-Free, Knowledge-Level Learning},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {16},
  pages     = {11--36},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{JonVol87,
  author  = {R. Jonker and A. Volgenant},
  title   = {A Shortest Augmenting Path Algorithm for Dense and Sparse
             Linear Assignment Problems},
  journal = {Computing},
  year    = {1987},
  volume  = {38},
  pages   = {325-340}
}

@article{JonWigWri86,
  author  = {P.~D.~Jones and T.~M.~W.~Wigley and P.~B.~Wright},
  title   = {Global temperature variations between 1861 and 1984},
  journal = {Nature},
  year    = {1986},
  volume  = {322},
  pages   = {430--434}
}

@article{JorBarMcA06,
  author  = {Peter L. Bartlett and Michael I. Jordan and Jon D.
             McAuliffe},
  title   = {Convexity, classification, and risk bounds},
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  year    = {2006},
  volume  = {101},
  number  = {473},
  pages   = {138--156}
}

@book{JorBis02,
  title     = {An Introduction to Probabilistic Graphical Models},
  publisher = {{MIT} Press},
  year      = {2002},
  author    = {M.~I.~Jordan and C. M. Bishop}
}

@book{Jordan02,
  title     = {An Introduction to Probabilistic Graphical Models},
  publisher = {{MIT} Press},
  year      = {2008},
  author    = {M.~I.~Jordan},
  note      = {To Appear}
}

@techreport{Jordan88,
  author      = {M.~I.~Jordan},
  title       = {Supervised learning and systems with excess degrees of
                 freedom},
  institution = {COINS},
  year        = {1988},
  number      = {88-27},
  month       = may,
  comments    = {Working paper}
}

@inproceedings{Jordan94,
  author    = {M.~I.~Jordan},
  title     = {A statistical approach to decision tree modeling},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {13--20},
  publisher = {ACM Press, New York, NY},
  annote    = {Also in Proc. 11th Int.Conf. ML, Learning,1994, 363--370}
}

@incollection{JorGhaJaaSau98,
  author    = {M.~I.~Jordan and Z.~Gharamani and T.~S.~Jaakkola and L.~K.
               Saul},
  title     = {An introduction to variational methods for graphical
               models},
  booktitle = {Learning in Graphical Models},
  publisher = {Kluwer Academic},
  year      = {1998},
  editor    = {M.~I.~Jordan},
  pages     = {105--162}
}

@article{JorGhaJaaSau99,
  author    = {M.~I.~Jordan and Z.~Ghahramani and T. S. Jaakkola and
               L.~K.~Saul},
  title     = {An Introduction to Variational Methods for Graphical
               Models},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {37},
  pages     = {183--233},
  number    = {2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{JorJac94,
  author  = {M.~I.~Jordan and R.~Jacobs},
  title   = {Hierarchical mixtures of experts and the {EM} algorithm},
  journal = {Neural Computation},
  year    = {1994},
  volume  = {6},
  pages   = {181--214}
}

@book{JorKeaSol98,
  title     = {Advances in Neural Information Processing Systems 10},
  publisher = {{MIT} Press},
  year      = {1998},
  author    = {M.~I.~Jordan and M.~J.~Kearns and S.~A.~Solla},
  address   = {Cambridge, MA}
}

@article{JorRum92,
  author  = {M.~I.~Jordan and D.~Rumelhart},
  title   = {Forward models: Supervised learning with a distal
             teacher},
  journal = {Cognitive Science},
  year    = {1992},
  volume  = {16},
  pages   = {307--354}
}

@article{JorXu95,
  author  = {M.~I.~Jordan and L. Xu},
  title   = {Convergence results for the {EM} approach to Mixtures of
             Experts Architectures},
  journal = {Neural Networks},
  year    = {1995},
  volume  = {8},
  pages   = {1409--1431},
  number  = {9}
}

@article{Jose_ScoringRules_2008,
  title    = {Scoring rules, generalized entropy, and utility
              maximization},
  volume   = {56},
  issn     = {0030-364X, 1526-5463},
  doi      = {10.1287/opre.1070.0498},
  language = {en},
  number   = {5},
  journal  = {Operations Research},
  author   = {Jose, Victor Richmond R. and Nau, Robert F. and Winkler,
              Robert L.},
  month    = oct,
  year     = {2008},
  pages    = {1146--1157},
  file     = {Jose et al - Scoring rules, generalized entropy, and
              utility maximization
              (2008).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Jose et al - Scoring rules, generalized entropy,
              and utility maximization (2008).pdf:application/pdf}
}

@inproceedings{JouGui79,
  author    = {J. P. Jouannaud and G. Guiho},
  title     = {Inference of functions with an interactive system},
  booktitle = {Machine Intelligence 9},
  year      = {1979},
  pages     = {227--250},
  publisher = {John Wiley and Sons, N. Y.}
}

@inproceedings{JouKod79,
  author       = {J. P. Jouannaud and Y. Kodratoff},
  title        = {Characterization of a class of functions synthesized by a
                  Summers-like method using a {B}.{M}.{W}. matching
                  technique},
  booktitle    = {Proc. 6th IJCAI},
  year         = {1979},
  pages        = {440--447},
  organization = {IJCAI}
}

@article{JouKod80,
  author  = {J. P. Jouannaud and Y. Kodratoff},
  title   = {An automatic construction of {LISP} programs by
             transformations of functions synthesized from their
             input-output behavior},
  journal = {International J. of Policy Analysis and Information
             Systems},
  year    = {1980},
  volume  = {4},
  pages   = {331--358}
}

@article{Jourani_VerifiableConditions_1995,
  title    = {Verifiable {Conditions} for {Openness} and {Regularity} of
              {Multivalued} {Mappings} in {Banach} {Spaces}},
  doi      = {10.1090/s0002-9947-1995-1290719-8},
  abstract = {This paper establishes verifiable conditions in terms of
              approximate subdifferentials implying openness and metric
              regularity of multivalued mappings in Banach spaces. The
              results are then applied to derive Lagrange multipliers for
              general nonsmooth vector optimization problems.},
  language = {en},
  author   = {Jourani, A and Thibault, L},
  year     = {1995},
  pages    = {14},
  file     = {Jourani, Thibault - Verifiable Conditions for Openness and
              Regularity of Multivalued Mappings in
              (1995).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Jourani, Thibault - Verifiable Conditions for
              Openness and Regularity of Multivalued Mappings in
              (1995).pdf:application/pdf}
}

@article{Journeeetal10,
  author  = {Michel Journee and Francis Bach and Pierre-Antoine Absil
             and Rodolphe Sepulchre},
  title   = {Low-Rank Optimization on the Cone of Positive Semidefinite
             Matrices},
  journal = {SIAM Journal on Optimization},
  year    = {2010},
  volume  = {20},
  pages   = {2327--2351},
  number  = {5}
}

@inproceedings{JouTre77,
  author    = {J. P. Jouannaud and T. P. Treuil},
  title     = {{SISP}/1, an interactive system able to synthesize
               functions from examples},
  booktitle = {Proc. 5th IJCAI},
  year      = {1977},
  pages     = {412--418},
  publisher = {IJCAI}
}

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  author  = {B.-H.~Juang and W.~Chou and C.-H.~Lee},
  title   = {{Minimum Classification Error Rate Methods for Speech
             Recognition}},
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  year    = {1997},
  volume  = {5},
  pages   = {257--265},
  number  = {3}
}

@article{Juang85,
  author  = {B. Juang},
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             Speech Recognition - {A} Unified View},
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  year    = {1985},
  volume  = {63},
  pages   = {1213--1242},
  number  = {7},
  month   = sep,
  comment = {Gaussian autoregressive models; Markov models. Baum's
             forward-backward algorithm. Computing all paths versus
             computing best path.}
}

@techreport{Judd87,
  author      = {J. S. Judd},
  title       = {Complexity of Connectionist Learning with Various Node
                 Functions},
  institution = {Department of Computer and Information Science, University
                 of Massachusetts at Amherst},
  year        = {1987},
  number      = {87-60},
  month       = jul,
  note        = {Also presented at the First IEEE International Conference
                 on Neural Networks, June 21--24, 1987, San Diego,
                 California}
}

@phdthesis{Judd88,
  author  = {S.~Judd},
  title   = {Neural Network Design and the Complexity of Learning},
  school  = {University of Massachusetts, Amherst},
  year    = {1988},
  address = {Amherst, MA}
}

@book{Judd90,
  title     = {Neural Network Design and the Complexity of Learning},
  publisher = {{MIT} Press},
  year      = {1990},
  author    = {J. S. Judd},
  address   = {Cambridge, Massachusetts},
  annote    = {This is authors Ph.D thesis from 1988, from Univ. of MA.
               at Amherst}
}

@article{Judd92,
  author    = {J. Stephen Judd},
  title     = {A Reply to Honavar's Book Review of Neural Network Design
               and the Complexity of Learning},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {9},
  pages     = {99--100},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inbook{Judd94,
  chapter   = {3},
  pages     = {49--64},
  title     = {How loading complexity is affected by node function sets},
  publisher = {{MIT} Press},
  year      = {1994},
  author    = {Stephen Judd},
  volume    = {I: Constraints and Prospects},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@article{JudHjaBenDeletal95,
  author  = {A.~Juditsky and H.~Hjalmarasson and A.~Benveniste and B.
             Delyon and L.~Ljung and J.~Sj\"{o}berg and Q.~Zhang},
  title   = { Nonlinear Black-Box Models in System Identification:
             Mathematical Foundations},
  journal = {Automatica},
  year    = {1995},
  volume  = {31},
  pages   = {1725--1750},
  number  = {12}
}

@article{Juhasz_MapsPreserving_2018,
  title    = {On maps preserving connectedness and /or compactness},
  abstract = {We call a function \$f: X{\textbackslash}to Y\$
              \$P\$-preserving if, for every subspace \$A
              {\textbackslash}subset X\$ with property \$P\$, its image
              \$f(A)\$ also has property \$P\$. Of course, all continuous
              maps are both compactness- and connectedness-preserving and
              the natural question about when the converse of this holds,
              i.e. under what conditions is such a map continuous, has a
              long history. Our main result is that any non-trivial
              product function, i.e. one having at least two non-constant
              factors, that has connected domain, \$T\_1\$ range, and is
              connectedness-preserving must actually be continuous. The
              analogous statement badly fails if we replace in it the
              occurrences of "connected" by "compact". We also present,
              however, several interesting results and examples
              concerning maps that are compactness-preserving and/or
              continuum-preserving.},
  journal  = {arXiv:1801.06212 [math]},
  author   = {Juhász, I. and van Mill, J.},
  month    = jan,
  year     = {2018},
  note     = {arXiv: 1801.06212},
  keywords = {⛔ No DOI found},
  file     = {arXiv.org
              Snapshot:/Users/zcranko/Documents/Zotero/storage/TUD5C9SZ/1801.html:text/html;Juhász,
              van Mill - On maps preserving connectedness and -or
              compactness
              (2018).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Juhász, van Mill - On maps preserving
              connectedness and -or compactness
              (2018).pdf:application/pdf}
}

@inproceedings{JuiPol98,
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  title     = {Coevolutionary learning: a case study},
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  pages     = {251--259},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{JuiPol98b,
  author    = {Hugues Juill{\'e} and Jordan B. Pollack},
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  booktitle = {Proc. 4th International Colloquium on Grammatical
               Inference - ICGI '98},
  year      = {1998},
  volume    = {1433},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {126--137},
  publisher = {Springer-Verlag}
}

@inproceedings{JuKaeSin00,
  author    = {Peter Ju and Leslie Pack Kaelbling and Y. Singer},
  title     = {State-based Classification of Finger Gestures from
               Electromyographic Signals},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {439--446},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@book{Jumarie90,
  title     = {Relative information},
  publisher = {Springer-Verlag},
  year      = {1990},
  author    = {G. Jumarie}
}

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  year    = {1993},
  volume  = {84},
  pages   = {417--433}
}

@phdthesis{Jung77,
  author = {H. Jung},
  title  = {Zur {U}ntersuchung von {A}bstrakten {I}nteraktiven
            {E}rkennungssystemen},
  school = {Humbolt University, Berlin},
  year   = {1977}
}

@book{JurMar08,
  author    = {Daniel Jurafsky and James H. Martin},
  title     = {Speech and Language Processing},
  year      = {2008},
  publisher = {Prentice Hall},
  edition   = {2nd}
}

@article{JutHer91,
  author  = {C. Jutten and J. H\'erault},
  title   = {Blind Separation of Sources I. An Adaptive Algorithm based
             on Neuromimetic Architecture},
  journal = {Signal Processing},
  year    = {1991},
  volume  = {24},
  pages   = {1--10},
  number  = {1}
}

@unpublished{JuvMoh06,
  author = {Martin Juvan and Bojan Mohar},
  title  = {An Algorithm for Embedding Graphs in the Torus},
  note   = {Submitted},
  year   = {2006}
}

@article{kabakchieva2013predicting,
  title     = {Predicting student performance by using data mining
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  journal   = {Cybernetics and information technologies},
  volume    = {13},
  number    = {1},
  pages     = {61--72},
  year      = {2013},
  publisher = {Versita}
}

@inproceedings{KabShi93,
  author    = {Y. Kabashima and S. Shinomoto},
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  year      = {1993},
  pages     = {446--452},
  publisher = {ACM Press, New York, NY}
}

@article{Kac43,
  author  = {M.~Kac},
  title   = {On the average number of real roots of a random algebraic
             equation},
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  year    = {1943},
  volume  = {49},
  pages   = {314--320}
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@inproceedings{KadNirFal91,
  author    = {V.~Kadirkamanathan and M.~Niranjan and F.~Fallside},
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}

@inproceedings{Kadous99,
  author    = {Mohammed Waleed Kadous},
  title     = {Learning comprehensible descriptions of multivariate time
               series},
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  year      = {1999},
  pages     = {454--463},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inbook{Kaelbling94,
  chapter   = {9},
  pages     = {133--153},
  title     = {Associative methods in reinforcement learning: an
               empirical study},
  publisher = {{MIT} Press},
  year      = {1994},
  author    = {Leslie Pack Kaelbling},
  volume    = {II: Intersections Between Theory and Experiment},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@article{Kaelbling94b,
  author    = {Leslie Pack Kaelbling},
  title     = {Associative Reinforcement Learning: Functions in {\it
               k}-{DNF}},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {15},
  pages     = {279--298},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Kaelbling94c,
  author    = {Leslie Pack Kaelbling},
  title     = {Associative Reinforcement Learning: {A} Generate and Test
               Algorithm},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {15},
  pages     = {299--319},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Kaelbling96,
  author    = {Leslie Pack Kaelbling},
  title     = {Introduction},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {22},
  pages     = {7--9},
  publisher = {Kluwer Academic Publishers, Boston}
}

@book{Kahane68,
  title     = {Some Random Series of Functions},
  publisher = {Cambridge University Press},
  year      = {1968},
  author    = {J.~P.~Kahane}
}

@article{Kahane75,
  author  = {J.~P.~Kahane},
  title   = {Sur le theoreme de superposition de {Kolmogorov}},
  journal = {Journal of Approximation Theory},
  year    = {1975},
  volume  = {13},
  pages   = {229--234}
}

@article{KahHar08,
  author  = {Fredrik Kahl and Richard Hartley},
  title   = {Multiple View Geometry Under the $L_{\infty}$-Norm},
  journal = pami,
  volume  = 30,
  month   = {in press},
  year    = 2008
}

@article{KahKomSze95,
  author  = {J. Kahn and J. Koml\'{o}s and E. Szemeredi},
  title   = {On the Probability That a Random $\pm 1$-Matrix Is
             Singular},
  journal = {Journal of the Americal Mathematical Society},
  year    = {1995},
  volume  = {8},
  pages   = {223-240},
  number  = {1},
  month   = {January}
}

@book{KaiSayHas00,
  title     = {Linear Estimation},
  publisher = {Prentice Hall},
  year      = {2000},
  author    = {T.~Kailath and A.~Sayed and B.~Hassibi},
  address   = {}
}

@inproceedings{Kakade02,
  author    = {S. Kakade},
  title     = {A Natural Policy Gradient},
  booktitle = nips14,
  year      = {2002},
  editor    = {T.~G. Dietterich and S. Becker and Z. Ghahramani},
  pages     = {1531--1538},
  publisher = {{MIT} Press}
}

@inproceedings{KakIma93,
  author    = {K. Kakihara and H. Imai},
  title     = {Notes on the {PAC} Learning of Geometric Concepts with
               Additional Information},
  booktitle = alt-ws,
  year      = {1993},
  volume    = {743},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {252--259},
  publisher = {Springer-Verlag}
}

@book{Kallenberg05,
  author    = {O. Kallenberg},
  title     = {Probabilistic symmetries and invariance principles},
  publisher = {Springer},
  year      = 2005
}

@article{Kalman60,
  author    = {R. Kalman},
  booktitle = {Transaction of the AMSE: Journal of Basic Engineering},
  title     = {A new approach to linear filtering and prediction
               problems},
  year      = {1960},
  volume    = {82},
  pages     = {35--45}
}

@article{KalMor96,
  author    = {Dimitrios Kalles and Tim Morris},
  title     = {Efficient Incremental Induction of Decision Trees},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {24},
  pages     = {231--242},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Kalnishkan00,
  author    = {Yuri Kalnishkan},
  title     = {Complexity Approximation Principle and {R}issanen's
               Approach to Real-Valued Parameters},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {203--210},
  publisher = {Springer, Berlin}
}

@inproceedings{Kalnishkan99,
  author    = {Yuri Kalnishkan},
  title     = {General Linear Relations among Different Types of
               Predictive Complexity},
  booktitle = alt,
  year      = {1999},
  volume    = {1720},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {323--334},
  publisher = {Springer-Verlag}
}

@inproceedings{Kalnishkan99b,
  author    = {Yuri Kalnishkan},
  title     = {Linear relations between square-loss and {K}olmogorov
               complexity},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {226--232},
  publisher = {ACM Press, New York, NY}
}

@article{Kalnishkan_WeakAggregating_2008,
  title    = {The weak aggregating algorithm and weak mixability},
  volume   = {74},
  issn     = {00220000},
  doi      = {10.1016/j.jcss.2007.08.003},
  language = {en},
  number   = {8},
  journal  = {Journal of Computer and System Sciences},
  author   = {Kalnishkan, Yuri and Vyugin, Michael V.},
  month    = dec,
  year     = {2008},
  pages    = {1228--1244},
  file     = {Full
              Text:/Users/zcranko/Documents/Zotero/storage/EG3ZUM7C/Kalnishkan
              and Vyugin - 2008 - The weak aggregating algorithm and weak
              mixability.pdf:application/pdf;The weak aggregating
              algorithm and weak
              mixability:/Users/zcranko/Documents/Zotero/storage/XEU9N6BX/kalnishkan2008.pdf:application/pdf}
}

@inproceedings{KalVel95,
  author    = {Bala Kalyanasundaram and Mahendran Velauthapillai},
  title     = {Simulating teams with many conjectures},
  booktitle = alt-ws,
  year      = {1995},
  pages     = {201--214},
  publisher = {Springer-Verlag}
}

@inproceedings{Kamalaruban_ExpConcavityProper_2015,
  address   = {Paris, France},
  series    = {Proceedings of {Machine} {Learning} {Research}},
  title     = {Exp-{Concavity} of {Proper} {Composite} {Losses}},
  abstract  = {The goal of online prediction with expert advice is to
               find a decision strategy which will perform almost as well
               as the best expert in a given pool of experts, on any
               sequence of outcomes. This problem has been widely studied
               and O({\textbackslash}sqrtT) and O({\textbackslash}logT)
               regret bounds can be achieved for convex losses and
               strictly convex losses with bounded first and second
               derivatives respectively. In special cases like the
               Aggregating Algorithm with mixable losses and the Weighted
               Average Algorithm with exp-concave losses, it is possible
               to achieve O(1) regret bounds. But mixability and
               exp-concavity are roughly equivalent under certain
               conditions. Thus by understanding the underlying
               relationship between these two notions we can gain the best
               of both algorithms (strong theoretical performance
               guarantees of the Aggregating Algorithm and the
               computational efficiency of the Weighted Average
               Algorithm). In this paper we provide a complete
               characterization of the exp-concavity of any proper
               composite loss. Using this characterization and the
               mixability condition of proper losses, we show that it is
               possible to transform (re-parameterize) any β-mixable
               binary proper loss into a β-exp-concave composite loss
               with the same β. In the multi-class case, we propose an
               approximation approach for this transformation.},
  booktitle = {Proceedings of the 28th conference on learning theory},
  publisher = {PMLR},
  author    = {Kamalaruban, Parameswaran and Williamson, Robert C. and
               Zhang, Xinhua},
  editor    = {Grünwald, Peter and {Satyen Kale}},
  month     = jun,
  year      = {2015},
  pages     = {1035--1065}
}

@inproceedings{Kamimura93,
  author    = {R. Kamimura},
  title     = {Competitive Learning by Entropy Minimization},
  booktitle = alt-ws,
  year      = {1993},
  volume    = {743},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {111--122},
  publisher = {Springer-Verlag}
}

@article{Kamimura98,
  author  = {R. Kamimura},
  title   = {Minimizing Alpha-Information for Generalization and
             Interpretation},
  journal = {Algorithmica},
  year    = {1998},
  volume  = {22},
  pages   = {173--197},
  number  = {1/2}
}

@inproceedings{KamLee94,
  author    = {N.~Kambhatla and T.~K.~Leen},
  title     = {Fast non-linear dimension reduction},
  booktitle = {Advances in Neural Information Processing Systems 6},
  year      = {1994},
  editor    = {J.~D.~Cowan and G.~Tesauro and J.~Alspector},
  pages     = {152--159},
  address   = {San Francisco, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@article{KamLee97,
  author  = {N.~Kambhatla and T.~K.~Leen},
  title   = {Dimension Reduction by Local Principal Component
             Analysis},
  journal = {Neural Computation},
  year    = {1997},
  volume  = {9},
  pages   = {1493--1516},
  number  = {7}
}

@article{KamOhtYoh97,
  author  = {T. Kameda and Y. Ohtsubo and M. Takezawa},
  journal = {Journal of Personality and Social Psychology},
  month   = {August},
  number  = {2},
  pages   = {296--309},
  title   = {Centrality in Sociocognitive Networks and Social
             Influence: An Illustration in a Group Decision-Making
             Context},
  url     = {http://www.sciencedirect.com/science/article/B6X01-46P4MJT-43/2/2e5c7dcccccad2ae4594c1f7bd64eace},
  volume  = {73},
  year    = {1997}
}

@inproceedings{KamWilZha15,
  author    = {Parameswaran Kamalaruban and Robert C Williamson and
               Xinhua Zhang},
  title     = {Exp-Concavity of Proper Composite Losses},
  booktitle = colt,
  year      = 2015
}

@techreport{Kan01,
  author      = {Min-Yen Kan},
  title       = {Combining Visual Layout and Lexical Cohesion Features for
                 Text Segmentation},
  institution = {Columbia University},
  year        = {2001},
  number      = {CUCS-002-01}
}

@inproceedings{KanCriSha03,
  author    = {J. Kandola and N. Cristianini and J. Shawe-Taylor},
  title     = {Learning Semantic Similarity},
  booktitle = nips,
  year      = {2003},
  volume    = {14},
  publisher = {{MIT} Press}
}

@book{Kanerva88,
  title     = {Sparse distributed memory},
  publisher = {{MIT} Press},
  year      = {1988},
  author    = {P.~Kanerva},
  address   = {Cambridge, {MA}}
}

@inproceedings{Kangetal12,
  title     = {GigaTensor: Scaling Tensor Analysis Up By 100 Times -
               Algorithms and Discoveries },
  author    = {U Kang and Evangelos Papalexakis and Abhay Harpale and
               Christos Faloutsos},
  booktitle = {KDD},
  year      = {2012}
}

@book{Kanizsa79,
  title     = {Organization of Vision},
  publisher = {Praeger},
  year      = {1979},
  author    = {G.~Kanizsa},
  address   = {New York}
}

@phdthesis{Kankainen95,
  author = {A. Kankainen},
  title  = {Consistent Testing of Total Independence Based on the
            Empirical Characteristic Function},
  school = {{University of Jyv{\"a}skyl{\"a}}},
  year   = {1995}
}

@inproceedings{KanKanKimYosetal95,
  author    = {T.~Kanade and H.~Kano and S.~Kimura and A.~Yoshida and
               K.~Oda},
  title     = {Development of a Video-Rate Stereo Machine},
  booktitle = {Proc.\ International Robotics and Systems Conf.\ (IROS 95)},
  address   = {Pittsburgh, PA},
  month     = {August 5--9},
  year      = {1995},
  pages     = {95--100}
}

@book{KanKry58,
  title     = {Approximate Methods of Higher Analysis},
  publisher = {Noordhoff},
  year      = {1958},
  author    = {L.~V.~Kantorovich and V.~I.~Krylov},
  address   = {Groningen},
  note      = {({Translated from the Russian original})}
}

@article{KanLisSca05,
  author  = {G. Kaniadakis and M. Lissia and A. M. Scarfone},
  journal = {Phys. Rev. E},
  number  = {046128},
  pages   = {1--12},
  title   = {Two-parameter deformations of logarithm, exponential, and
             entropy: A consistent framework for generalized statistical
             mechanics},
  volume  = {71},
  year    = {2005}
}

@article{KanLisSca05b,
  author  = {G. Kaniadakis and M. Lissia and A. M. Scarfone},
  journal = {Phys. Rev. E},
  number  = {046128},
  pages   = {1--12},
  title   = {Two-parameter deformations of logarithm, exponential, and
             entropy: A consistent framework for generalized statistical
             mechanics},
  volume  = {71},
  year    = {2005}
}

@inproceedings{Kannan93,
  author    = {S. Kannan},
  title     = {On the query complexity of learning},
  booktitle = {Proc. 6th Annu. Conf. on Comput. Learning Theory},
  year      = {1993},
  pages     = {58--66},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{KanOh95,
  author    = {Kukjin Kang and Jong-Hoon Oh},
  title     = {Learning by a population of perceptrons},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {297--300},
  publisher = {ACM Press, New York, NY}
}

@article{Kansa90,
  author  = {E.~J.~Kansa},
  title   = {Multiquadrics\,---\,a Scattered Data Approximation Scheme
             with Applications to Computational Fluid Dynamics\,---\,{I}},
  journal = {Computers Mathematical Applications},
  year    = {1990a},
  volume  = {19},
  pages   = {127--145},
  number  = {8/9}
}

@article{Kansa90b,
  author  = {E. J.~Kansa},
  title   = {Multiquadrics\,---\,a Scattered Data Approximation Scheme
             with Applications to Computational Fluid Dynamics\,---\,{II}},
  journal = {Computers Math.~Applic.},
  year    = {1990b},
  volume  = {19},
  pages   = {147--161},
  number  = {8/9}
}

@inproceedings{KanTchMil93,
  author    = {Raqui Kane and Irina Tchoumatchenko and Maurice Milgram},
  title     = {Extraction of Knowledge from Data Using Constrained Neural
               Networks},
  booktitle = {Machine Learning: ECML-93, European Conference on Machine
               Learning, Proceedings},
  year      = {1993},
  volume    = {667},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {420--425},
  publisher = {Springer-Verlag}
}

@article{Kantorovitch58,
  author  = {Kantorovitch, L.},
  title   = {On the Translocation of Masses},
  journal = {Management Science},
  volume  = {5},
  number  = {1},
  pages   = {1--4},
  year    = {1958}
}

@article{KanVemVet04,
  author  = {R. Kannan and S. Vempala and A. Vetta},
  title   = {On clusterings: good, bad and spectral},
  journal = {Journal of the ACM},
  year    = {2004},
  volume  = {51},
  pages   = {497-515}
}

@inproceedings{KaoReiTat93,
  author    = {M. Kao and J. H. Reif and S. R. Tate},
  title     = {Searching in an Unknown Environment: An Optimal Randomized
               Algorithm for the Cow-Path Problem},
  booktitle = {Proc. SODA 93},
  year      = {1993},
  pages     = {441--447},
  month     = jan
}

@inproceedings{KapBil92,
  author    = {S. Kapur and G. Bilardi},
  title     = {Language learning from stochastic input},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {303--310},
  publisher = {ACM Press, New York, NY}
}

@article{KapBil92b,
  author  = {S. Kapur and G. Bilardi},
  title   = {On uniform learnability of language families},
  journal = {Information Processing Letters},
  year    = {1992},
  volume  = {44},
  pages   = {35--38},
  number  = {1}
}

@article{KapBil95,
  author  = {S. Kapur and G. Bilardi},
  title   = {Language learning without overgeneralization},
  journal = {Theoret. Comput. Sci.},
  year    = {1995},
  volume  = {141},
  pages   = {151--162},
  number  = {1/2},
  note    = {first in STACS '92}
}

@book{KapKes92,
  title     = {Entropy Optimization Principles with Applications},
  publisher = {Academic Press, Inc.},
  year      = {1992},
  author    = {J. N. Kapur and H. K Kesavan}
}

@incollection{KapLusHarMot93,
  author    = {S. Kapur and B. Lust and W. Harbert and G. Motohardjono},
  title     = {Universal Grammar and Learnability Theory: The Case of
               Binding Domains and the `Subset Principle'},
  booktitle = {Knowledge and Language},
  publisher = {Kluwer Academic Publishers},
  year      = {1993},
  editor    = {Eric Reuland and Werner Abraham},
  volume    = {1}
}

@phdthesis{Kapur91,
  author = {Shyam Kapur},
  title  = {Computational Learning of Languages},
  school = {Department of Computer Science, Cornell University,
            Ithaca, New York},
  year   = {1991}
}

@phdthesis{Kapur92,
  author  = {S.~Kapur},
  title   = {Computational Learning of Languages},
  school  = {Cornell University},
  year    = {1992},
  address = {Ithaca, NY}
}

@inproceedings{Kapur93,
  author    = {S. Kapur},
  title     = {Monotonic Language Learning},
  booktitle = alt-ws,
  year      = {1993},
  volume    = {743},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {147--158},
  publisher = {Springer-Verlag}
}

@inproceedings{Kapur93b,
  author    = {S. Kapur},
  title     = {Uniform Characterizations of Various Kinds of Language
               Learning},
  booktitle = alt-ws,
  year      = {1993},
  volume    = {744},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {197--208},
  publisher = {Springer-Verlag}
}

@inproceedings{Kapur94,
  author    = {S. Kapur},
  title     = {Language Learning Under Various Types of Constraint
               Combinations},
  booktitle = alt-ws,
  year      = {1994},
  volume    = {872},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {365--378},
  publisher = {Springer-Verlag}
}

@article{KarBra97,
  author    = {Aram Karaliccaron and Ivan Bratko},
  title     = {First Order Regression},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {26},
  pages     = {147--176},
  publisher = {Kluwer Academic Publishers, Boston}
}

@mastersthesis{Karchin00,
  author = {Rachel Karchin},
  title  = {Classifying G-Protein Coupled Receptors with Support
            Vector Machines},
  school = {University of California},
  year   = {2000}
}

@article{KarEriKoi02,
  author  = {J. Karvanen and J. Eriksson and V. Koivunen},
  title   = {Adaptive Score Functions for Maximum Likelihood {ICA}},
  journal = {Journal of VLSI Signal Processing Systems},
  year    = {2002},
  volume  = {32},
  pages   = {83--92}
}

@article{Karhunen46,
  author  = {K.~Karhunen},
  title   = {Zur {S}pektraltheorie stochastischer {P}rozesse},
  journal = {Ann. Acad. Sci. Fenn.},
  year    = {1946},
  volume  = {37}
}

@inproceedings{Karhunen94,
  author    = {Juha Karhunen},
  title     = {Optimization Criteria and Nonlinear {PCA} Neural
               Networks},
  booktitle = {IEEE World Congress on Computational Intelligence},
  pages     = {1241--1246},
  year      = {1994},
  volume    = {2}
}

@article{KarJou94,
  author  = {Juha Karhunen and Jyrki Joutsensalo},
  title   = {Representation and separation of signals using nonlinear
             {PCA} type learning.},
  journal = {Neural Networks},
  volume  = {7},
  number  = {1},
  year    = {1994},
  pages   = {113-127}
}

@article{KarJou95,
  author  = {J.~Karhunen and J.~Joutsensalo},
  title   = {Generalizations of Principal Component Analysis,
             Optimization Problems, and Neural Networks},
  journal = {Neural Networks},
  year    = {1995},
  volume  = {8},
  pages   = {549--562},
  number  = {4}
}

@inproceedings{KarKhoMehRab06,
  author    = {H. Karloff and S. Khot and A. Mehta and Y. Rabani},
  title     = {On earthmover distance, metric labeling, and 0-extension},
  booktitle = {the 38th annual ACM symposium on Theory of computing},
  year      = {2006}
}

@article{KarKum98,
  author    = {G. Karypis and V. Kumar},
  title     = {Multilevel $k$-way partitioning scheme for irregular
               graphs},
  journal   = {J. Parallel Distrib.~Comput.},
  year      = {1998},
  number    = {1},
  volume    = {48},
  publisher = {Academic Press},
  cdate     = {1970-01-01},
  mdate     = {2005-08-18}
}

@book{Karlin59,
  title     = {Mathematical Methods and Theory in Games, Programming, and
               Economics},
  publisher = {Addison-Wesley},
  year      = {1959},
  author    = {S.~Karlin},
  volume    = {{I} and {II}},
  address   = {Reading, {MA}}
}

@book{Karlin68,
  title     = {Total Positivity},
  publisher = {Stanford University Press},
  year      = {1968},
  author    = {S. Karlin},
  volume    = {1},
  address   = {Stanford, CA, USA}
}

@incollection{Karlin76,
  author    = {S.~Karlin},
  title     = {On a class of best nonlinear approximation problems and
               extended monosplines},
  booktitle = {Studies in spline functions and approximation theory},
  publisher = {Academic Press},
  year      = {1976},
  editor    = {S.~Karlin and C.~A.~Micchelli and A.~Pinkus and I.
               J.~Schoenberg},
  pages     = {19--66},
  address   = {New York}
}

@techreport{KarMac95,
  author      = {M.~Karpinski and A.~Macintyre},
  title       = {Polynomial Bounds for {VC} Dimension of Sigmoidal Neural
                 Networks},
  institution = {International Computer Science Institute},
  year        = {1995},
  number      = {TR-95-001},
  address     = {Berkeley, CA},
  month       = jan
}

@inproceedings{KarMac95b,
  author    = {Marek Karpinski and Angus Macintyre},
  title     = {Polynomial bounds for {VC} dimension of sigmoidal neural
               networks},
  booktitle = {Proc. 27th Annual ACM Symposium on Theory of Computing},
  year      = {1995},
  pages     = {200--208},
  publisher = {ACM Press, New York, NY}
}

@article{KarMac97,
  author  = {Marek Karpinski and Angus Macintyre},
  title   = {Polynomial Bounds for {VC} Dimension of Sigmoidal and
             General {Pfaffian} Neural Networks},
  journal = {Journal of Computer and System Sciences},
  year    = {1997},
  volume  = {54},
  pages   = {169--176},
  number  = {1},
  month   = feb
}

@article{Karmarkar84,
  author  = {N.~K.~Karmarkar},
  title   = {A new polynomial-time algorithm for linear programming},
  journal = {the 16th Annual ACM Symposium on Theory of Computing},
  year    = {1984},
  pages   = {302--311}
}

@article{KarNew10,
  title   = {{Stochastic blockmodels and community structure in
             networks}},
  author  = {Karrer, B. and Newman, MEJ},
  journal = {Arxiv preprint arXiv:1008.3926},
  year    = {2010}
}

@article{KarNew11,
  title     = {{Stochastic blockmodels and community structure in
               networks}},
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@inproceedings{KauSte97,
  author    = {Susanne Kaufmann and Frank Stephan},
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@inproceedings{KauSte97b,
  author    = {Susanne Kaufmann and Frank Stephan},
  title     = {Robust learning with infinite additional information},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  pages     = {316--330},
  publisher = {Springer-Verlag}
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@inproceedings{Kawaguchi16,
  author    = {Kenji Kawaguchi},
  title     = {Deep Learning without Poor Local Minima},
  booktitle = nips,
  year      = {2016}
}

@article{KawNis00,
  author  = {T. Kawabata and K. Nishikawa},
  title   = {Protein tertiary structure comparison using the {M}arkov
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  volume  = {41},
  pages   = {108-122}
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@inproceedings{KayAlp00,
  author    = {Cenk Kaynak and Ethem Alpaydin},
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               Man's Noise is Another Man's Data},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {455--462},
  publisher = {Morgan Kaufmann, San Francisco, CA}
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@article{KazMan01,
  author    = {Dimitar Kazakov and Suresh Manandhar},
  title     = {Unsupervised Learning of Word Segmentation Rules with
               Genetic Algorithms and Inductive Logic Programming},
  journal   = {Machine Learning},
  year      = {2001},
  volume    = {43},
  pages     = {121--162},
  number    = {1/2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Kazman94,
  author    = {Rick Kazman},
  title     = {Simulating the Child's Acquisition of the Lexicon and
               Syntax - Experiences with {\it Babel}},
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  year      = {1994},
  volume    = {16},
  pages     = {87--120},
  publisher = {Kluwer Academic Publishers, Boston}
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@article{KeaLi93,
  author  = {M. Kearns and M. Li},
  title   = {Learning in the presence of malicious errors},
  journal = {SIAM J. Comput.},
  year    = {1993},
  volume  = {22},
  pages   = {807--837},
  annote  = {Also Harvard TR 03-87},
  comment = {Studies pac learning in the presence of worst sort of
             errors. prev iously appeared in stoc'88.}
}

@inproceedings{KeaLiPitVal87,
  author    = {M. Kearns and M. Li and L. Pitt and L. Valiant},
  title     = {On the learnability of {B}oolean formulae},
  booktitle = {Proc. 19th Annu. ACM Sympos. Theory Comput.},
  year      = {1987},
  pages     = {285--294},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{KeaLiPitVal87b,
  author    = {M. Kearns and M. Li and L. Pitt and L. Valiant},
  title     = {Recent Results on Boolean Concept Learning},
  booktitle = {Proc. of the Fourth International Workshop on Machine
               Learning},
  year      = {1987},
  pages     = {337--352},
  address   = {Irvine, California},
  month     = jun,
  publisher = {(published by Morgan Kaufmann, Los Altos California)}
}

@inproceedings{KeaLitSin01,
  author    = {Michael Kearns and Michael Littleman and Satinder Singh},
  title     = {Graphical Models for Game Theory},
  booktitle = uai,
  year      = 2001
}

@inproceedings{KeaMan98,
  author    = {M. Kearns and Yishay Mansour},
  title     = {A fast, bottom-up decision tree pruning algorithm with
               near-optimal generalization},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {269--277},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{KeaMan99,
  author  = {M. Kearns and Y. Mansour},
  title   = {On the boosting ability of top-down decision tree learning
             algorithms},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1999},
  volume  = {58},
  pages   = {109--128},
  number  = {1}
}

@article{KeaManNgRon97,
  author    = {M.~Kearns and Y.~Mansour and A.~Y.~Ng and D.~Ron},
  title     = {An Experimental and Theoretical Comparison of Model
               Selection Methods},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {27},
  pages     = {7--50},
  annote    = {Earlier version in COLT95},
  publisher = {Kluwer Academic}
}

@inproceedings{KeaManRonRubetal94,
  author    = {M. Kearns and Y. Mansour and D. Ron and R. Rubinfeld and
               R. Schapire and L. Sellie},
  title     = {On the learnability of discrete distributions},
  booktitle = {Proc. of Twenty-sixth ACM Symposium on Theory of
               Computing},
  year      = {1994},
  pages     = {273--282}
}

@inproceedings{KeaPit89,
  author    = {M. Kearns and L. Pitt},
  title     = {A polynomial-time algorithm for learning $k$-variable
               pattern languages from examples},
  booktitle = {Proc. 2nd Annu. Workshop on Comput. Learning Theory},
  year      = {1989},
  pages     = {57--71},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{kearns1994toward,
  title     = {Toward efficient agnostic learning},
  author    = {Kearns, Michael J and Schapire, Robert E and Sellie, Linda
               M},
  journal   = {Machine Learning},
  volume    = {17},
  number    = {2-3},
  pages     = {115--141},
  year      = {1994},
  publisher = {Springer}
}

@unpublished{Kearns88,
  author = {M. Kearns},
  title  = {Thoughts on Hypothesis Boosting},
  note   = {(Unpublished)},
  month  = dec,
  year   = {1988}
}

@phdthesis{Kearns89,
  author = {M. Kearns},
  title  = {The Computational Complexity of Machine Learning},
  school = {Harvard University Center for Research in Computing
            Technology},
  year   = {1989},
  month  = may,
  note   = {Technical Report TR-13-89. Also published by MIT Press as
            an ACM Distinguished Dissertation.}
}

@inproceedings{Kearns93,
  author    = {M. J.~Kearns},
  title     = {Efficient Noise-Tolerant Learning From Statistical
               Queries},
  booktitle = {the 25th ACM Symposium on the Theory of Computing},
  year      = {1993},
  pages     = {392--401},
  publisher = {ACM Press}
}

@article{Kearns97,
  author  = {M.~Kearns},
  title   = {A Bound on the Error of Cross Validation Using the
             Approximation and Estimation Rates, with Consequences for
             the Training-Test Split},
  journal = {Neural Computation},
  year    = {1997},
  volume  = {9},
  pages   = {1143--1161},
  number  = {5}
}

@article{Kearns98,
  author  = {Michael Kearns},
  title   = {Efficient Noise-Tolerant Learning from Statistical
             Queries},
  journal = {Journal of the ACM},
  year    = {1998},
  volume  = {45},
  pages   = {983--1006},
  number  = {6}
}

@article{KeaRon00,
  author  = {M. Kearns and Dana Ron},
  title   = {Testing Problems with Sublearning Sample Complexity},
  journal = {J. of Comput. Syst. Sci.},
  year    = {2000},
  volume  = {61},
  pages   = {428--456},
  number  = {3},
  month   = dec
}

@inproceedings{KeaRon97,
  author    = {M. Kearns and Dana Ron},
  title     = {Algorithmic Stability and Sanity-Check Bounds for
               Leave-One-Out Cross-Validation},
  booktitle = {Proc. 10th Annu. Conf. on Comput. Learning Theory},
  year      = {1997},
  pages     = {152--162},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{KeaRon98,
  author    = {M. Kearns and Dana Ron},
  title     = {Testing Problems with Sub-Learning Sample Complexity},
  booktitle = {Proc. 11th Annu. Conf. on Comput. Learning Theory},
  year      = {1998},
  pages     = {268--279},
  publisher = {ACM Press, New York, NY}
}

@article{KeaRon99,
  author  = {M.~Kearns and D.~Ron},
  title   = {Algorithmic Stability and Sanity-Check Bounds for
             Leave-One-Out Cross-Validation},
  journal = {Neural Computation},
  year    = {1999},
  volume  = {11},
  pages   = {1427--1453},
  number  = {6}
}

@inproceedings{KeaSch90,
  author    = {M.~J.~Kearns and R.~E.~Schapire},
  title     = {Efficient distribution-free learning of probabilistic
               concepts},
  booktitle = {Proc.~of the 31st Symposium on the Foundations of Comp.
               Sci.},
  year      = {1990},
  pages     = {382--391},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@article{KeaSch94,
  author  = {M.~J.~Kearns and R.~E.~Schapire},
  title   = {Efficient Distribution-free Learning of Probabilistic
             Concepts},
  journal = {Journal of Computer and System Sciences},
  year    = {1994},
  volume  = {48},
  pages   = {464--497},
  number  = {3}
}

@inproceedings{KeaSchSel92,
  author    = {M. J.~Kearns and Robert E.~Schapire and Linda M. Sellie},
  title     = {Toward Efficient Agnostic Learning (Extended Abstract)},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1992},
  pages     = {341--352},
  address   = {Pittsburgh, Pennsylvania},
  publisher = {ACM Press}
}

@article{KeaSchSel94,
  author  = {M.~J.~Kearns and R.~E.~Schapire and L.~M.~Sellie},
  title   = {Toward efficient agnostic learning},
  journal = {Machine Learning},
  year    = {1994},
  volume  = {17},
  pages   = {115--141},
  number  = {2}
}

@article{KeaSeu95,
  author    = {M. Kearns and H. Sebastian Seung},
  title     = {Learning from a population of hypotheses},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {18},
  pages     = {255--276},
  number    = {2/3},
  annote    = {Earlier version appeared in sixth COLT, 1993},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{KeaSin00,
  author    = {M. Kearns and S. Singh},
  title     = {Bias-Variance Error Bounds for Temporal Difference
               Updates},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {142--147},
  publisher = {Morgan Kaufmann, San Francisco}
}

@inproceedings{KeaSin98,
  author    = {M. Kearns and S. Singh},
  title     = {Near-optimal reinforcement learning in polynomial time},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {260--268},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@techreport{KeaVal88,
  author      = {M. Kearns and L. G. Valiant},
  title       = {Learning Boolean Formulae or Finite Automata is as Hard as
                 Factoring},
  institution = {Harvard University Aiken Computation Laboratory},
  year        = {1988},
  number      = {TR 14-88}
}

@inproceedings{KeaVal89,
  author    = {M. Kearns and L. G. Valiant},
  title     = {Cryptographic limitations on learning {B}oolean formulae
               and finite automata},
  booktitle = {Proc. of the 21st Symposium on Theory of Computing},
  year      = {1989},
  pages     = {433--444},
  publisher = {ACM Press, New York, NY},
  comment   = {To appear in J. ACM}
}

@book{KeaVaz94,
  title     = {An Introduction to Computational Learning Theory},
  publisher = {{MIT} Press},
  year      = {1994},
  author    = {M.~J.~Kearns and U.~V.~Vazirani},
  address   = {Cambridge, Massachusetts}
}

@article{KecLiTro97,
  author  = {John Kececioglu and Ming Li and John Tromp},
  title   = {Inferring a {DNA} sequence from erroneous copies},
  journal = {Theoret. Comput. Sci.},
  year    = {1997},
  volume  = {185},
  pages   = {3--13},
  number  = {1},
  note    = {Special issue for ALT '95}
}

@article{KeeChaDec06,
  author  = {S. Sathiya Keerthi and Olivier Chapelle and Dennis
             DeCoste},
  journal = jmlr,
  pages   = {1493--1515},
  title   = {Building Support Vector Machines with Reduced Classifier
             Complexity},
  volume  = {7},
  year    = {2006}
}

@article{KeeDeC05,
  author  = {S. S. Keerthi and D. DeCoste },
  title   = {A Modified Finite {N}ewton Method for Fast Solution of
             Large Scale Linear {SVMs}},
  journal = {J. Mach. Learn. Res.},
  year    = 2005,
  volume  = 6,
  pages   = {341-361}
}

@techreport{KeeGil00,
  author      = {S.S. Keerthi and E.~G.~Gilbert},
  title       = {Convergence of a Generalized {SMO} Algorithm for {SVM}
                 Classifier Design},
  institution = {Dept.\ of Mechanical and Production Engineering},
  year        = {2000},
  address     = {National University of Singapore}
}

@article{KeeGil02,
  author  = {Sathiya S. Keerthi and Elmer Grant Gilbert},
  title   = {Convergence of a Generalized {SMO} Algorithm for {SVM}
             Classifier Design},
  journal = {Machine Learning},
  year    = {2002},
  volume  = {46},
  issue   = {3},
  pages   = {351--360}
}

@article{Keeler88,
  author  = {J.~D.~Keeler},
  title   = {Comparison between {Kanerv\`{a}s SDM} and {Hopfield}-type
             neural networks},
  journal = {Cognitive Science},
  year    = {1988},
  volume  = {12},
  pages   = {299--329}
}

@article{KeeSheBhaMur00,
  author  = {S.S. Keerthi and S.~K. Shevade and C. Bhattacharyya and
             K.~R.~K. Murthy},
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             Vector Machine Classifier Design},
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}

@article{KeeSheBhaMur01,
  author  = {S.S. Keerthi and S.K. Shevade and C. Bhattacharyya and
             K.R.K. Murty},
  title   = {Improvements to Platt's {SMO} algorithm for {SVM}
             classifier design},
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  year    = {2001},
  volume  = {13},
  pages   = {637-649}
}

@techreport{KeeSheBhaMur99,
  author      = {S.S. Keerthi and S.~K.~Shevade and C.~Bhattacharyya and
                 K.~R.~K.~Murthy},
  title       = {A Fast Iterative Nearest Point Algorithm for Support
                 Vector Machine Classifier Design},
  institution = {Indian Institute of Science, Bangalore},
  year        = {1999},
  number      = {Technical Report TR-ISL-99-03},
  url         = {http://guppy.mpe.nus.edu.sg/~mpessk/npa_tr.ps.gz}
}

@techreport{KeeSheBhaMur99b,
  author      = {S.S. Keerthi and S.~K.~Shevade and C.~Bhattacharyya and
                 K.~R.~K.~Murthy},
  title       = {Improvements to {P}latt's {SMO} Algorithm for {SVM}
                 Classifier Design},
  institution = {Dept.~of Mechanical and Production Engineering,
                 Natl.~Univ.~Singapore},
  year        = {1999},
  number      = {CD-99-14},
  address     = {Singapore}
}

@inproceedings{KeeSunChaHsietal08,
  author    = {S. Sathiya Keerthi and S. Sundararajan and Kai-Wei Chang
               and Cho-Jui Hsieh and Chih-Jen Lin},
  editor    = {Ying Li and Bing Liu and Sunita Sarawagi},
  title     = {A sequential dual method for large scale multi-class
               linear {SVM}s},
  booktitle = kdd,
  year      = 2008,
  pages     = {408--416},
  publisher = {ACM}
}

@article{KeeWal85,
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             Intelligence},
  year    = {2000},
  pages   = {281--297}
}

@misc{KegKrzLinZeg97,
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            Kenneth Zeger},
  title  = {Learning and Design of Principal Curves},
  year   = {1997},
  note   = {submitted}
}

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  author    = {B.~K\'egl and A.~Krzy\.zak and T.~Linder and K.~Zeger},
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  editor    = {M.~S.~Kearns and S.~A.~Solla and D.~A.~Cohn},
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}

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  author      = {K. Kelly and C. Glymour},
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  author  = {J.~E.~Kelly},
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             Mathematics},
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             and D. E. Root and B. R. Stockwell and T. Ideker},
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  year    = 2006
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  author    = {Jeffrey O. Kephart and Gerald J. Tesauro},
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  year      = {2000},
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@article{Kerdreux_ApproximateShapleyFolkman_2019,
  title    = {An approximate {Shapley}-{Folkman} theorem},
  abstract = {The Shapley-Folkman theorem shows that Minkowski averages
              of uniformly bounded sets tend to be convex when the number
              of terms in the sum becomes much larger than the ambient
              dimension. In optimization, Aubin and Ekeland [1976] show
              that this produces an a priori bound on the duality gap of
              separable nonconvex optimization problems involving finite
              sums. This bound is highly conservative and depends on
              unstable quantities, and we relax it in several directions
              to show that non convexity can have a much milder impact on
              finite sum minimization problems such as empirical risk
              minimization and multi-task classification. As a byproduct,
              we show a new version of Maurey's classical approximate
              Carath{\textbackslash}'eodory lemma where we sample a
              significant fraction of the coefficients, without
              replacement, as well as a result on sampling constraints
              using an approximate Helly theorem, both of independent
              interest.},
  journal  = {arXiv:1712.08559 [math]},
  author   = {Kerdreux, Thomas and Colin, Igor and d'Aspremont,
              Alexandre},
  month    = jul,
  year     = {2019},
  note     = {arXiv: 1712.08559},
  keywords = {⛔ No DOI found, Mathematics - Optimization and Control}
}

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  year   = {2016}
}

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}

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  volume  = {35},
  pages   = {547-550},
  number  = {3}
}

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  author  = { Padmakar V. Khadikar and Narayan V. Deshpande and
             Prabhakar P. Kale and Andrey Dobrynin and Ivan Gutman and
             Gyula Domotor},
  title   = {The Szeged Index and an Analogy with the Wiener Index},
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  year    = {1995},
  volume  = {35},
  number  = {3},
  pages   = {547-550}
}

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  year      = {2000},
  pages     = {471--478},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

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  number    = {2},
  comment   = {first in 8th COLT '95},
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}

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  author    = {R.~Khardon and D.~Roth and R.~Servedio},
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  year      = {2002},
  pages     = {423-430}
}

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  year      = {1994},
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  year      = {1994},
  volume    = {14},
  pages     = {193--217},
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  year      = {1993},
  address   = {Vienna, Austria}
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@inproceedings{Kietz93b,
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  volume    = {667},
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  year      = {2004}
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@article{KikKaw92,
  author  = {T. Kikukawa and S. Kawafuchi},
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  author    = {J. Kilian and K. J. Lang and B. A. Pearlmutter},
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  pages     = {159--164},
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@inproceedings{KilManUkk95,
  author    = {Pekka Kilpel{\"a}inen and Heikki Mannila and Esko Ukkonen},
  title     = {{MDL} learning of unions of simple pattern languages from
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  year      = {1995},
  volume    = {904},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {252--260},
  publisher = {Springer-Verlag}
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@inproceedings{KilSie93,
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  year      = {1993},
  pages     = {137--143},
  publisher = {ACM Press, New York, NY}
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@phdthesis{Kim83,
  author  = {J. H. Kim},
  title   = {{CONVINCE}: {A} Conversational Inference Consolidation
             Engine},
  school  = {University of California, Los Angeles},
  year    = {1983},
  comment = {Interactive decision support system using Pearl's Bayesian
             networks}
}

@inproceedings{Kim91,
  author    = {W. M. Kim},
  title     = {Learning by smoothing: a morphological approach},
  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {43--57},
  address   = {San Mateo, CA},
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@inproceedings{KimCha03,
  author    = {H. R. Kim and P. K. Chan},
  title     = {Learning implicit user interest hierarchy for context in
               personalization},
  pages     = {101--108},
  editor    = {W. L. Johnson and E. Andr{\'e} and J. Domingue},
  booktitle = {the 2003 International Conference on Intelligent User
               Interfaces ({IUI}-03)},
  publisher = {{ACM} Press},
  address   = {New York},
  year      = {2003}
}

@article{KimFraSch05,
  author  = {K. Kim and M. O. Franz and B. Sch\"olkopf},
  title   = {Iterative Kernel Principal Component Analysis for Image
             Modeling},
  journal = {IEEE Transactions on Pattern Analysis and Machine
             Intelligence},
  year    = {2005},
  volume  = {27},
  pages   = {1351-1366},
  number  = {9}
}

@article{KimGha06,
  author  = {H.-C. Kim and Zoubin Ghahramani},
  title   = {Bayesian Gaussian Process Classification with the {EM-EP}
             Algorithm},
  year    = 2006,
  journal = {IEEE Transactions on Pattern Analysis and Machine
             Intelligence},
  volume  = 28,
  number  = 12,
  pages   = {1948--1959}
}

@inproceedings{KimIwa92,
  author    = {H. Kimoto and T. Iwadera},
  title     = {Learning Effect of a Dynamic Thesaurus in Associated
               Information Retrieval},
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  year      = {1992},
  pages     = {47--60},
  publisher = {IOS Press}
}

@article{KimJunParKim00,
  author  = {K.~I.~Kim and K.~Jung and S.~H.~Park and H.~J.~Kim},
  title   = {Texture Classification with Kernel Principal Component
             Analysis},
  journal = {Electronics Letters},
  year    = {2000},
  volume  = {36},
  pages   = {1021--1022},
  number  = {12}
}

@inproceedings{KimKob98,
  author    = {Hajime Kimura and Shigenobu Kobayashi},
  title     = {An Analysis of Actor/Critic Algorithms Using Eligibility
               Traces: Reinforcement Learning with Imperfect Value
               Functions},
  booktitle = icml,
  year      = {1998},
  pages     = {278--286},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{KimKob98b,
  author    = {Hajime Kimura and Shigenobu Kobayashi},
  title     = {Reinforcement Learning for Continuous Action using
               Stochastic Gradient Ascent},
  booktitle = {Intelligent Autonomous Systems (IAS-5)},
  pages     = {288--295},
  year      = {1998}
}

@inproceedings{KimKob99,
  author    = {Hajime Kimura and Shigenobu Kobayashi},
  title     = {Efficient non-linear control by combining {Q}-learning
               with local linear controllers},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {210--219},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{KimKohLusBoyetal07,
  author  = {Kim, Seung-Jean and Koh, K. and Lustig, M. and Boyd, S.
             and Gorinevsky, D.},
  journal = {IEEE J SEL TOP SIGNAL PROC},
  title   = {An Interior-Point Method for Large-Scale
             $\ell$1-Regularized Least Squares},
  volume  = {1},
  year    = {2007}
}

@article{KimKoyTopGraetal06,
  author  = {Y. Kim and M. Koyuturk and U. Topkara and A. Grama and S.
             Subramaniam},
  title   = {Inferring functional information from domain
             co-evolution},
  journal = {Bioinformatics},
  volume  = {22},
  number  = {1},
  pages   = {40-49},
  month   = {Jan},
  year    = {2006}
}

@inproceedings{KimLee00,
  author    = {Dae{E}un Kim and Jaeho Lee},
  title     = {Handling Continuous-Valued Attributes in Decision Tree
               with Neural Network Modelling},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {211--219},
  publisher = {Springer, Berlin}
}

@article{KimLes10,
  author    = {Myunghwan Kim and Jure Leskovec},
  title     = {Multiplicative Attribute Graph Model of Real-World
               Networks},
  journal   = {CoRR},
  volume    = {abs/1009.3499},
  year      = {2010},
  ee        = {http://arxiv.org/abs/1009.3499},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{KimLon92,
  author    = {D. Kimber and P. M. Long},
  title     = {The learning complexity of smooth functions of a single
               variable},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {153--159},
  publisher = {ACM Press, New York, NY}
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@inproceedings{KimMiyKob97,
  author    = {Hajime Kimura and Kazuteru Miyazaki and Shigenobu
               Kobayashi},
  title     = {Reinforcement learning in {POMDP}s with function
               approximation},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {152--160},
  publisher = {Morgan Kaufmann}
}

@inproceedings{KimNev00,
  author    = {Zu Whan Kim and Ramakant Nevatia},
  title     = {Learning Bayesian Networks for Diverse and Varying Numbers
               of Evidence Sets},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {479--486},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{KimOhtTsuTatetal04,
  author    = {Jin-Dong Kim and Tomoko Ohta and Yoshimasa Tsuruoka and
               Yuka Tateisi and Nigel Collier},
  title     = {Introduction to the Bio-Entity Recognition Task at
               {JNLPBA}},
  booktitle = {Proc.\ Intl.\ Joint Workshop on Natural Language
               Processing in Biomedicine and its Applications (NLPBA)},
  year      = {2004},
  pages     = {70--75},
  address   = {Geneva, Switzerland}
}

@inproceedings{KimPar99,
  author    = {D.~K. Kim and K. Park},
  title     = {Linear-time construction of two-dimensional suffix trees},
  booktitle = {the $26\mathrm{th}$ International Colloquium on Automata,
               Languages and Programming (ICALP)},
  year      = {1999},
  volume    = {1644},
  series    = {LNCS},
  pages     = {463--472},
  address   = {Prague, Czech},
  month     = {July},
  publisher = {Springer}
}

@inproceedings{KimRoc95,
  author    = {Jeong Han Kim and James R. Roche},
  title     = {On the optimal capacity of binary neural networks:
               rigorous combinatorial approaches},
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  year      = {1995},
  pages     = {240--249},
  publisher = {ACM Press, New York, NY}
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  author    = { D.~K.~Kim and J.~S.~Sim and H.~Park and K.~Park},
  title     = {Linear-time construction of suffix arrays},
  booktitle = {14th Annual Symposium, CPM 2003},
  year      = {2003},
  editor    = {R.~Baeza-Yates, E.~Ch\'{a}vez, M.~Crochemore},
  volume    = {2676},
  series    = {Lecture Notes in Computer Science},
  pages     = {186--199},
  publisher = {Springer-Verlag}
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@inproceedings{KimSimParPar03b,
  author    = { D.~k. Kim and J.~S. Sim and H. Park and K. Park},
  title     = {Linear-time construction of suffix arrays},
  booktitle = {14th Annual Symposium, CPM 2003},
  series    = {Lecture Notes in Computer Science},
  year      = {2003},
  pages     = {186--199},
  volume    = {2676},
  editor    = {R. Baeza-Yates, E. Ch\'{a}vez, M. Crochemore},
  publisher = {Springer-Verlag}
}

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  month   = dec
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  author    = {S. Kimura and A. Togashi and N. Shiratori},
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  booktitle = alt-ws,
  year      = {1994},
  pages     = {379--394},
  publisher = {Springer-Verlag}
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@article{KimWah70,
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  year    = {1970},
  volume  = {41},
  pages   = {495--502}
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@inproceedings{KimXin10,
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  year      = {2010}
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@article{KimXing09,
  author  = {Seyoung Kim and Eric P. Xing},
  title   = {Statistical Estimation of Correlated Genome Associations
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  number  = {8},
  pages   = {1--18},
  year    = {2009}
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@inproceedings{KimYamKob95,
  author    = {Hajime Kimura and Masayuki Yamamura and Shigenobu
               Kobayashi},
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  booktitle = icml,
  year      = {1995},
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@incollection{Kivinen_AveragingExpert_1999,
  address   = {Berlin, Germany},
  title     = {Averaging expert predictions},
  volume    = {1572},
  isbn      = {978-3-540-65701-9 978-3-540-49097-5},
  booktitle = {Computational {Learning} {Theory}},
  publisher = {Springer},
  author    = {Kivinen, Jyrki and Warmuth, Manfred K.},
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  year      = {1999},
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  file      = {Kivinen, Warmuth - Averaging expert predictions
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  address   = {Oxford},
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  isbn      = {0 19 853492 2}
}

@unpublished{KivSmoWanWil01,
  author = {J.~Kivinen and A. J. Smola and P.~Wankadia and
            R.~C.~Williamson},
  title  = {On-line Algorithms for Kernel Methods},
  note   = {in preparation},
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@inproceedings{KivSmoWil02,
  author    = {J.~Kivinen and A. J. Smola and R.~C.~Williamson},
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  booktitle = {Neural Information Processing Systems},
  year      = {2002}
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@inproceedings{KivSmoWil02b,
  author    = {J. Kivinen and A. J. Smola and R.~C.~Williamson},
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  address   = {L\"ubeck, Germany},
  year      = {2002},
  volume    = {2533},
  series    = {Lecture Notes in Artificial Intelligence},
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@article{KivSmoWil04,
  title    = {Online Learning with Kernels},
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  year     = {2004},
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  month    = {Aug},
  crossref = {KivSmoWil04b}
}

@article{KivSmoWil04b,
  author  = {J.~Kivinen and A. J. Smola and R.~C.~Williamson},
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  month   = {Aug}
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@article{KivWar01,
  author  = {J. Kivinen and M. K. Warmuth},
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@techreport{KivWar94,
  author      = {J. Kivinen and M. K. Warmuth},
  title       = {Exponentiated Gradient Versus Gradient Descent for Linear
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  institution = {University of California, Santa Cruz, Computer Research
                 Laboratory},
  year        = {1994},
  number      = {UCSC-CRL-94-16},
  month       = jun,
  note        = {Revised December 7, 1995. An extended abstract to appeared
                 in the STOC 95, pp. 209--218.}
}

@inproceedings{KivWar94b,
  author    = {J. Kivinen and M. K. Warmuth},
  title     = {Using Experts for Predicting Continuous Outcomes},
  booktitle = {Computational Learning Theory: EuroColt '93},
  year      = {1994},
  volume    = {New Series Number 53},
  series    = {The Institute of Mathematics and its Applications
               Conference Series},
  pages     = {109--120},
  address   = {Oxford},
  publisher = {Oxford University Press},
  isbn      = {0 19 853492 2}
}

@inproceedings{KivWar95,
  author    = {J. Kivinen and M. K. Warmuth},
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  year      = {1995},
  pages     = {209--218},
  publisher = {ACM Press, New York, NY}
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@inproceedings{KivWar95b,
  author    = {J. Kivinen and M. K. Warmuth},
  title     = {The perceptron algorithm vs. {W}innow: linear vs.
               logarithmic mistake bounds when few input variables are
               relevant},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {289--296},
  publisher = {ACM Press, New York, NY}
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@article{KivWar97,
  author  = {J. Kivinen and M. K. Warmuth},
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             Predictors},
  journal = {Information and Computation},
  year    = {1997},
  volume  = {132},
  pages   = {1--64},
  number  = {1},
  month   = {January}
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@inproceedings{KivWar99,
  author    = {J. Kivinen and M. K. Warmuth},
  title     = {Averaging Expert Predictions},
  booktitle = {Proc. 4th European Conference on Computational Learning
               Theory},
  year      = {1999},
  volume    = {1572},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {153--167},
  publisher = {Springer-Verlag}
}

@inproceedings{KivWar99b,
  author    = {J. Kivinen and M. K. Warmuth},
  title     = {Boosting as entropy projection},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {134--144},
  publisher = {ACM Press, New York, NY}
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@article{KivWarAue97,
  author  = {J. Kivinen and M. K. Warmuth and P. Auer},
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             vs.~Logarithmic Mistake Bounds When Few Input Variables are
             Relevant},
  journal = {Artificial Intelligence},
  year    = {1997},
  volume  = {97},
  pages   = {325--343},
  number  = {1--2}
}

@inproceedings{KivWarHas03,
  author       = {J. Kivinen and M. K. Warmuth and B. Hassibi},
  title        = {The $p$-norm generaliziation of the {LMS} algorithm for
                  adaptive filtering},
  booktitle    = {the 13th IFAC symposium on system identification},
  year         = {2003},
  editor       = {P.M.J. Van den Hof and B. Wahlberg and S. Weiland},
  pages        = {1755--1760},
  address      = {Rotterdam, Netherlands},
  month        = {August},
  organization = {IFAC},
  publisher    = {Elsevier Science}
}

@article{KivWarHas06,
  author  = {J. Kivinen and M. K. Warmuth and B. Hassibi},
  title   = {The $p$-norm generaliziation of the {LMS} algorithm for
             adaptive filtering},
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@article{Kolmogorov06,
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@article{Kolmogorov08,
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  year    = 2009
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  title     = {Learning Time-Varying Concepts},
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  year      = {1991},
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  booktitle = {Advances in Neural Information Processing Systems 4},
  year      = {1991},
  editor    = {J.~E. Moody and S.~J. Hanson and R. Lippmann},
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  pages     = {920--927},
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}

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  title     = {Nonlinear Programming},
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               Statistics and Probabilistics},
  year      = {1951},
  pages     = {481--492},
  address   = {Berkeley},
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@book{KuiSal86,
  title     = {Semirings, Automata, Languages},
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}

@inproceedings{KulBar10,
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  title     = {Implicit Online Learning},
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@techreport{Kulkarni89,
  author      = {S. Kulkarni},
  title       = {On Metric Entripy, {V}apnik-{C}hervonenkis Dimension, and
                 Learnability for a Class of Distributions},
  institution = {Center for Intelligent Control Systems, MIT},
  year        = {1989},
  number      = {LIDS-P-1910}
}

@phdthesis{Kulkarni91,
  author = {S. R. Kulkarni},
  title  = {Problems of computational and information complexity in
            machine vision and learning},
  school = {MIT EECS},
  year   = {1991},
  note   = {(Also CICS report CICS-TH-298.)}
}

@article{KulLugVen98,
  author  = {S. Kulkarni and G. Lugosi and S. Venkatesh},
  title   = {Learning Pattern Classification\,---\,A Survey},
  journal = {IEEE Transactions on Information Theory},
  year    = {1998},
  volume  = {44},
  pages   = {2178--2206}
}

@article{KulMitTsi93,
  author    = {S. R. Kulkarni and S. K. Mitter and J. N. Tsitsiklis},
  title     = {Active Learning Using Arbitrary Binary Valued Queries},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {11},
  pages     = {23--35},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{KulMitTsiZei92,
  author    = {S. R. Kulkarni and S. K. Mitter and J. N. Tsitsiklis and
               O. Zeitouni},
  title     = {{PAC} learning with generalized samples and an application
               to stochastic geometry},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {172--179},
  publisher = {ACM Press, New York, NY}
}

@incollection{KulPer08,
  title     = {Structured Learning with Approximate Inference},
  author    = {A. Kulesza and F. Pereira},
  booktitle = {Advances in Neural Information Processing Systems 20},
  editor    = {J.C. Platt and D. Koller and Y. Singer and S. Roweis},
  publisher = {MIT Press},
  address   = {Cambridge, MA},
  year      = {2008}
}

@inproceedings{KulSaeDar11,
  title        = {What you saw is not what you get: Domain adaptation using
                  asymmetric kernel transforms},
  author       = {Kulis, Brian and Saenko, Kate and Darrell, Trevor},
  booktitle    = cvpr,
  pages        = {1785--1792},
  year         = {2011},
  organization = {IEEE}
}

@inproceedings{KulZei93,
  author    = {S.~R.~Kulkarni and O.~Zeitouni},
  title     = {On Probably Correct Classification of Concepts},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1993},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{KumAll04,
  author    = {Kumaran, Giridhar and Allan, James},
  title     = {Text classification and named entities for new event
               detection},
  booktitle = {SIGIR},
  year      = {2004},
  isbn      = {1-58113-881-4},
  pages     = {297--304},
  location  = {Sheffield, United Kingdom},
  doi       = {http://doi.acm.org/10.1145/1008992.1009044},
  publisher = {ACM},
  address   = {New York, NY, USA}
}

@inproceedings{kumardaume11,
  author    = {A. Kumar and H. {Daume III}},
  title     = {A Co-training Approach for Multi-view Spectral Clustering
               },
  booktitle = icml,
  year      = {2011}
}

@inproceedings{kumaretal11,
  author    = {A. Kumar and P. Rai and H. {Daume III}},
  title     = {Co-regularized Multi-view Spectral Clustering},
  booktitle = nips,
  year      = {2011}
}

@inproceedings{Kumaretal18,
  author    = {Abhishek Kumar and Prasanna Sattigeri and Kahini Wadhawan
               and Leonid Karlinsky and Rogerio Feris and William T.
               Freeman and Gregory Wornell},
  title     = {Co-regularized Alignment for Unsupervised Domain
               Adaptation},
  booktitle = nips,
  year      = {2018}
}

@article{KumCasMur82,
  author  = {B.~Kumar and D.~Casasent and H.~Murakami},
  title   = {Principal {C}omponent {I}magery for {S}tatistical
             {P}attern {R}ecognition {C}orrelators},
  journal = {Optical Engineering},
  year    = {1982},
  volume  = {21},
  number  = {1}
}

@inproceedings{KumHeb03,
  author    = {Sanjiv Kumar and Martial Hebert},
  title     = {Discriminative Random Fields: A Discriminative Framework
               for Contextual Interaction in Classification},
  booktitle = {Proc.\ Intl.\ Conf.\ Computer Vision},
  year      = {2003},
  url       = {http://www-2.cs.cmu.edu/~skumar/san/DRF/DRF.pdf}
}

@inproceedings{KumHeb03b,
  author    = {Sanjiv Kumar and Martial Hebert},
  title     = {Man-Made Structure Detection in Natural Images Using a
               Causal Multiscale Random Field},
  booktitle = {Proc.\ IEEE Conf. Computer Vision and Pattern Recognition},
  year      = {2003}
}

@inproceedings{KumHeb04,
  author    = {Sanjiv Kumar and Martial Hebert},
  title     = {Discriminative Fields for Modeling Spatial Dependencies in
               Natural Images},
  booktitle = {Advances in Neural Information Processing Systems 16},
  year      = {2004},
  editor    = {S. Thrun and L. Saul and B. Sch\"{o}lkopf}
}

@unpublished{KumHeb04b,
  author = {Sanjiv Kumar and Martial Hebert},
  title  = {Approximate Parameter Learning in Discriminative Fields},
  note   = {Learning Workshop, Snowbird, Utah},
  year   = {2004}
}

@article{KumHeb06,
  author  = {Sanjiv Kumar and Martial Hebert},
  title   = {Discriminative Random Fields},
  pages   = {179--201},
  journal = {International Journal of Computer Vision},
  volume  = 68,
  number  = 2,
  year    = 2006
}

@inproceedings{Kummer94,
  author    = {M. Kummer},
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               learners},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {268--277},
  publisher = {ACM Press, New York, NY}
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@article{Kummer95,
  author  = {Martin Kummer},
  title   = {A learning theoretic characterization of classes of
             recursive functions},
  journal = {Information Processing Letters},
  year    = {1995},
  volume  = {54},
  pages   = {205--211}
}

@inproceedings{KumNovTom06,
  author   = {Ravi Kumar and Jasmine Novak and Andrew Tomkins},
  title    = {Structure and evolution of online social networks.},
  year     = {2006},
  pages    = {611-617},
  ee       = {http://doi.acm.org/10.1145/1150402.1150476},
  crossref = {EliUngCraGun06}
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  author    = {Martin Kummer and Matthias Ott},
  title     = {Learning branches and learning to win closed games},
  booktitle = {Proc. 9th Annu. Conf. on Comput. Learning Theory},
  year      = {1996},
  pages     = {280--291},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{KumSte94,
  author    = {M. Kummer and F. Stephan},
  title     = {Inclusion problems in parallel learning and games},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {287--298},
  publisher = {ACM Press, New York, NY}
}

@article{KumSte96,
  author  = {Martin Kummer and Frank Stephan},
  title   = {On the structure of the Degrees of Inferability},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1996},
  volume  = {52},
  pages   = {214--238},
  number  = {2},
  note    = {Earlier version in Sixth Annual Conference on
             Computational Learning Theory, 1993}
}

@article{KumYil10,
  author  = {Kumar, Piyush and Yildirim, E. Alper},
  title   = {A Linearly Convergent Linear-Time First-Order Algorithm
             for Support Vector Classification with a Core Set Result},
  journal = {INFORMS J. on Computing},
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  number  = {3},
  year    = {2011},
  pages   = {377--391}
}

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@article{KunLei80,
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  year    = {1980},
  bibdate = {Tue Sep 8 10:43:48 1987},
  owner   = {reaves}
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@inproceedings{KunLomBau09,
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  title     = {The Slashdot zoo: mining a social network with negative
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  booktitle = {World Wide Web {WWW}},
  year      = {2009},
  pages     = {741--750},
  location  = {Madrid, Spain},
  doi       = {http://doi.acm.org/10.1145/1526709.1526809},
  publisher = {ACM},
  address   = {New York, NY, USA}
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@article{KunYajKon93,
  author  = {Takahito Kuno and Yasutoshi Yajima and Hiroshi Konno},
  title   = {An outer approximation method for minimizing the product
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  year    = 1993,
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  month   = {September}
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@techreport{Kurakin_AdversarialExamples_2017,
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@article{Kuratowski_GeneralTheorem_1965,
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@inproceedings{KurGooBen17,
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  booktitle = iclr,
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  volume    = {743},
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@inproceedings{KurKar04b,
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@article{KurSmiWie01,
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  year    = {2001},
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@inproceedings{KurWelTeh07,
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@book{KusCla78,
  title     = {Stochastic Approximation Methods for Constrained and
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  volume  = {22},
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@article{KusRasHer05,
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@article{KusRot96,
  author    = {Eyal Kushilevitz and D. Roth},
  title     = {On Learning Visual Concepts and {DNF} Formulae},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {24},
  pages     = {65--85},
  annote    = {Earlier versionin COLT95},
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  author = {M.\ Kuss},
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  volume   = {27},
  issn     = {0036-0279, 1468-4829},
  doi      = {10.1070/rm1972v027n03abeh001380},
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  year      = {1986},
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@inproceedings{Lambiotteetal18,
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  editor    = {M.~S.~Watanabe},
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@inproceedings{lampertkroemer10,
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  year      = {2010}
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@book{Lamport86,
  title     = {LaTe{X} user's guide and reference manual},
  publisher = {Addison-Wesley},
  year      = {1986 or later},
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  address   = {Reading}
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@article{LamPre07,
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  author  = {D. Lambert and D. Pregibon},
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  year    = {2007},
  number  = {2},
  volume  = {9},
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@article{LamRigPer00,
  author    = {E. Lamma and F. Riguzzi and L. M. Pereira},
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  volume    = {38},
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  number    = {1/2},
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@article{Lan10,
  author  = {Guanghui Lan},
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  year    = 2010
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@unpublished{Lan13,
  author = {Guanghui Lan},
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            Linear Optimizaton Oracle},
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}

@techreport{Lan14,
  author      = {Guanghui Lan},
  title       = {The Complexity of Large-scale Convex Programming under a
                 Linear Optimization Oracle},
  institution = {University of Florida},
  year        = {2014},
  type        = {Technical Report}
}

@inproceedings{LanBlu99,
  author    = {J. Langford and A. Blum},
  title     = {Microchoice bounds and self bounding learning algorithms},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {209--214},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{LanBro00,
  author    = {Terran Lane and C.E. Brodley},
  title     = {Data Reduction Techniques for Instance-Based Learning from
               Human/Computer Interface Data},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {519--526},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{lanckriet2002robust,
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  journal = {Journal of Machine Learning Research},
  volume  = {3},
  number  = {Dec},
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  year    = {2002}
}

@inproceedings{LanCriBarGhaetal02,
  author    = {G. Lanckriet and N. Cristianini and P. Bartlett and L.
               El~Ghaoui and M.~I.~Jordan},
  title     = {Learning the kernel matrix with semidefinite programming},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2002},
  pages     = {323--330},
  publisher = {Morgan Kaufmann}
}

@article{LanCriBarGhaetal04,
  author  = {G. Lanckriet and N. Cristianini and P. Bartlett and L. El
             Ghaoui and M.~I.~Jordan},
  title   = {Learning the Kernel Matrix with Semi-Definite
             Programming},
  journal = {Journal of Machine Learning Research},
  year    = {2004},
  volume  = {5},
  pages   = {27--72}
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@article{Land59,
  author  = {E.~H.~Land},
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  year    = {1959},
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  pages   = {115--129}
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@article{Land83,
  author  = {E.~H.~Land},
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  year    = {1983},
  volume  = {80},
  pages   = {5163--5169}
}

@article{Land86,
  author  = {E.~H.~Land},
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  year    = {1986},
  volume  = {83},
  pages   = {3078--3080}
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  year      = {1992},
  pages     = {45--52},
  publisher = {ACM Press, New York, NY}
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@inproceedings{Lang95,
  author    = {Kevin J. Lang},
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  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {340--343},
  publisher = {Morgan Kaufmann}
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@inproceedings{Lang95b,
  author    = {Ken Lang},
  title     = {News{W}eeder: learning to filter netnews},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {331--339},
  publisher = {Morgan Kaufmann}
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@inproceedings{Lange90,
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  year      = {1990},
  volume    = {543},
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  pages     = {178--183},
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@inproceedings{Lange94,
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@inproceedings{LanGhaBhaJor02,
  author    = {G.~R.G. Lanckriet and L. El Ghaoui and C. Bhattacharyya
               and M.~I.~Jordan},
  title     = {Minimax Probability Machine},
  booktitle = nips,
  year      = {2002},
  publisher = {{MIT} Press}
}

@article{LanGhaBhaJor02b,
  author  = {G.R.G. Lanckriet and L.E. Ghaoui and C. Bhattacharrya and
             M.I. Jordan},
  title   = {A robust minimax approach to classification},
  journal = {Journal of Machine Learning Research},
  year    = {2002},
  volume  = {3},
  pages   = {555--582}
}

@inproceedings{Langley00,
  author    = {Pat Langley},
  title     = {Crafting Papers on Machine Learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {1207--1212},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{Langley86,
  author    = {Pat Langley},
  title     = {Editorial: Human and Machine Learning},
  journal   = {Machine Learning},
  year      = {1986},
  volume    = {1},
  pages     = {243--248},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Langley86b,
  author    = {Pat Langley},
  title     = {Editorial: The Terminology of Machine Learning},
  journal   = {Machine Learning},
  year      = {1986},
  volume    = {1},
  pages     = {141--144},
  publisher = {Kluwer Academic Publishers, Boston}
}

@techreport{Langley86c,
  author      = {P. Langley},
  title       = {A General Theory of Discrimination Learning},
  institution = {University of California, Irvine},
  year        = {1986},
  number      = {86-18}
}

@article{Langley86d,
  author    = {Pat Langley},
  title     = {On Machine Learning},
  journal   = {Machine Learning},
  year      = {1986},
  volume    = {1},
  pages     = {5--10},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Langley87,
  author    = {P. Langley},
  title     = {Machine Learning and Concept Formation},
  journal   = {Machine Learning},
  year      = {1987},
  volume    = {2},
  pages     = {99--102},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Langley87b,
  author    = {P. Langley},
  title     = {Machine Learning and Grammar Induction},
  journal   = {Machine Learning},
  year      = {1987},
  volume    = {2},
  pages     = {5--8},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Langley87c,
  author    = {P. Langley},
  title     = {Research Papers in Machine Learning},
  journal   = {Machine Learning},
  year      = {1987},
  volume    = {2},
  pages     = {195--198},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Langley88,
  author    = {P. Langley},
  title     = {Machine Learning as an Experimental Science},
  journal   = {Machine Learning},
  year      = {1988},
  volume    = {3},
  pages     = {5--8},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Langley89,
  author    = {P. Langley},
  title     = {Toward a Unified Science of Machine Learning},
  journal   = {Machine Learning},
  year      = {1989},
  volume    = {3},
  pages     = {253--259},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Langley90,
  author    = {Pat Langley},
  title     = {Advice to Machine Learning Authors},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  pages     = {233--237},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Langley93,
  author    = {Pat Langley},
  title     = {Induction of Recursive {B}ayesian Classifiers},
  booktitle = {Machine Learning: ECML-93, European Conference on Machine
               Learning, Proceedings},
  year      = {1993},
  volume    = {667},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {153--164},
  publisher = {Springer-Verlag}
}

@inproceedings{LanGri98,
  author    = {Steffen Lange and Gunter Grieser},
  title     = {On Variants of Iterative Learning},
  booktitle = {Proc. 1st International Conference on Discovery Science},
  year      = {1998},
  volume    = {1532},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {72--83},
  publisher = {Springer-Verlag}
}

@inproceedings{LanGri99,
  author    = {Steffen Lange and Gunter Grieser},
  title     = {On the Strength of Incremental Learning},
  booktitle = alt,
  year      = {1999},
  volume    = {1720},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {118--131},
  publisher = {Springer-Verlag}
}

@misc{LanLiStr07,
  author = {J. Langford and L. Li and A. Strehl},
  title  = {Vowpal Wabbit Online Learning Project},
  note   = {\texttt{http://hunch.net/?p=309}},
  year   = 2007
}

@article{LanLitTay89,
  author  = {Kenneth L. Lange and Roderick J. A. Little and Jeremy M.
             G. Taylor},
  title   = {Robust Statistical Modeling Using the t Distribution},
  journal = {Journal of the American Statistical Association},
  volume  = {84},
  number  = {408},
  pages   = {881-896},
  year    = {1989}
}

@article{LanLuMon09,
  author  = {Guanghui Lan and Zhaosong Lu and Renato D. C. Monteiro},
  title   = {Primal-dual first-order methods with
             $\mathcal{O}(1/\epsilon)$ iteration complexity for cone
             programming},
  journal = {Mathematical Programming},
  year    = {2009}
}

@inproceedings{LanMcA00,
  author    = {J.~Langford and D.~Mc{A}llester},
  title     = {Computable Shell Decomposition Bounds},
  booktitle = {Proc.~13th Annu.~Conference on Comput.~Learning Theory},
  year      = {2000},
  pages     = {25--34},
  publisher = {Morgan Kaufmann Publishers}
}

@article{LanMcC71,
  author  = {E.~Land and J.~J.~McCann},
  title   = {Lightness and Retinex Theory},
  journal = {Journal of the Optical Society of America},
  year    = {1971},
  volume  = {61},
  pages   = {1--11}
}

@article{LanMic86,
  author    = {Pat Langley and Ryszard S. Michalski},
  title     = {Machine Learning and Discovery},
  journal   = {Machine Learning},
  year      = {1986},
  volume    = {1},
  pages     = {363--366},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{LanNesWie94,
  author    = {S. Lange and J. Nessel and R. Wiehagen},
  title     = {Language Learning from Good Examples},
  booktitle = alt-ws,
  year      = {1994},
  pages     = {423--437},
  publisher = {Springer-Verlag}
}

@article{LanNesWie98,
  author  = {Steffen Lange and Jochen Nessel and Rolf Wiehagen},
  title   = {Learning Recursive Languages from Good Examples},
  journal = {Annals of Mathematics and Artificial Intelligence},
  year    = {1998},
  volume  = {23},
  pages   = {27--52},
  number  = {1/2},
  note    = {Special issue for AII '94 and ALT '94}
}

@techreport{LanNor86,
  author      = {P. Langley and B. Nordhausen},
  title       = {A Framework for Empirical Discovery},
  institution = {University of California, Irvine},
  year        = {1986},
  number      = {86-19}
}

@inproceedings{LanPeaPri98,
  author    = {K. J. Lang and B. A. Pearlmutter and R. A. Price},
  title     = {Results of the {A}bbadingo one {DFA} learning competition
               and a new evidence-driven state merging algorithm},
  booktitle = {Proc. 4th International Colloquium on Grammatical
               Inference - ICGI '98},
  year      = {1998},
  volume    = {1433},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {1--12},
  publisher = {Springer-Verlag}
}

@inproceedings{LanPfl95,
  author    = {Pat Langley and Karl Pfleger},
  title     = {Case-based acquisition of place knowledge},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {344--352},
  publisher = {Morgan Kaufmann}
}

@article{LanProSmy97,
  author    = {Pat Langley and Gregory M. Provan and Padhraic Smyth},
  title     = {Learning with probabilistic representations},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {29},
  pages     = {91--101},
  publisher = {Kluwer Academic Publishers, Boston}
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@inproceedings{Lanquillon00,
  author    = {Carsten Lanquillon},
  title     = {Partially Supervised Text Classification: Combining
               Labeled and Unlabeled Documents Using an {EM}-like Scheme},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {229--237},
  publisher = {Springer, Berlin}
}

@inbook{LanSag97,
  chapter   = {4},
  pages     = {51--63},
  title     = {Scaling to domains with irrelevant features},
  publisher = {{MIT} Press},
  year      = {1997},
  author    = {Patrick Langley and Stephanie Sage},
  volume    = {IV: Making Learning Systems Practical},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@inproceedings{LanSag99,
  author    = {Pat Langley and Stephanie Sage},
  title     = {Tractable average-case analysis of naive {B}ayesian
               classifiers},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {220--228},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@book{LanSal86,
  title     = {Curve and Surface Fitting},
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  author      = {S. Lange and T. Zeugmann},
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  year        = {1992},
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}

@article{lecun2015deep,
  title     = {Deep learning},
  author    = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
  journal   = {nature},
  volume    = {521},
  number    = {7553},
  pages     = {436},
  year      = {2015},
  publisher = {Nature Publishing Group}
}

@inproceedings{LeCun88,
  author    = {Y.~LeCun},
  title     = {A Theoretical Framework for Backpropagation},
  booktitle = {the 1988 Connectionist Models Summer School},
  year      = {1988},
  editor    = {D.~Touretzky and G.~Hinton and T.~Sejnowski},
  pages     = {21--28},
  address   = {Palo Alto},
  month     = jun,
  publisher = {Morgan Kaufmann Publishers}
}

@misc{LeCun98,
  author = {Y.~LeCun},
  title  = {{MNIST} Handwritten Digit Database},
  year   = {1998},
  url    = {http://yann.lecun.com/exdb/mnist/}
}

@inproceedings{Lecuyer19,
  title     = {Certified Robustness to Adversarial Examples with
               Differential Privacy},
  author    = {Mathias Lécuyer and Vaggelis Atlidakis and Roxana
               Geambasu and Daniel Hsu and Suman Jana},
  booktitle = {IEEE Symposium on Security and Privacy (S\&P)},
  year      = {2019}
}

@book{Ledoux01,
  author    = {M. Ledoux},
  title     = {The Concentration of Measure Phenomenon},
  publisher = {AMS},
  year      = 2001,
  address   = {Providence, RI}
}

@book{LedTal91,
  author    = {M. Ledoux and M. Talagrand},
  title     = {Probability in Banach Spaces},
  publisher = {Springer},
  year      = {1991}
}

@book{Lee00,
  title     = {Introduction to Topological Manifolds},
  publisher = {Springer},
  year      = {2000},
  author    = {J. M. Lee},
  address   = {New York}
}

@book{Lee04,
  author    = {Jon Lee},
  title     = {A First Course in Combinatorial Optimization},
  publisher = {Cambridge University Press},
  year      = {2004}
}

@article{lee2004multicategory,
  title     = {Multicategory support vector machines: Theory and
               application to the classification of microarray data and
               satellite radiance data},
  author    = {Lee, Yoonkyung and Lin, Yi and Wahba, Grace},
  journal   = {Journal of the American Statistical Association},
  volume    = {99},
  number    = {465},
  pages     = {67--81},
  year      = {2004},
  publisher = {Taylor \& Francis}
}

@inproceedings{Lee92,
  author    = {H.~C. Lee},
  title     = {A physics-based color encoding model for images of natural
               scenes},
  booktitle = {the Conference on Modern Engineering and Technology,
               Electro-Optics session},
  year      = {1992},
  pages     = {25--52},
  address   = {Taipei, Taiwan},
  month     = dec # { 6--15}
}

@phdthesis{Lee96,
  author = {W.~S. Lee},
  title  = {Agnostic Learning and Single Hidden Layer Neural
            Networks},
  school = {Australian National University},
  year   = {1996},
  note   = {{http://routh.ee.adfa.oz.au/\~{}weesun/thesis.ps.Z}}
}

@book{Lee97,
  title     = {Riemannian Manifolds},
  publisher = {Springer},
  year      = {1997},
  author    = {J. M. Lee},
  number    = {176},
  series    = {Graduate Texts in Mathematics},
  address   = {New York}
}

@book{Lee_IntroductionRiemannian_2018,
  address   = {New York, NY, USA},
  edition   = {2},
  title     = {Introduction to {Riemannian} manifolds},
  isbn      = {978-3-319-91754-2},
  publisher = {Springer International Publishing},
  author    = {Lee, John M.},
  year      = {2018},
  file      = {Lee - Introduction to Riemannian manifolds
               (2018).pdf:/Users/zcranko/Documents/Library/Book/Lee -
               Introduction to Riemannian manifolds
               (2018).pdf:application/pdf}
}

@book{Lee_IntroductionSmooth_2013,
  address   = {New York, NY, USA},
  edition   = {2},
  series    = {Graduate texts in mathematics},
  title     = {Introduction to smooth manifolds},
  isbn      = {978-1-4419-9981-8 978-1-4419-9982-5},
  number    = {218},
  publisher = {Springer},
  author    = {Lee, John M.},
  year      = {2013},
  note      = {OCLC: ocn800646950},
  keywords  = {Manifolds (Mathematics)},
  file      = {Lee - Introduction to smooth manifolds
               (2013).pdf:/Users/zcranko/Documents/Library/Book/Lee -
               Introduction to smooth manifolds
               (2013).pdf:application/pdf}
}

@book{Lee_IntroductionTopological_2011,
  address   = {New York, NY, USA},
  edition   = {2},
  series    = {Graduate texts in mathematics},
  title     = {Introduction to topological manifolds},
  isbn      = {978-1-4419-7939-1},
  number    = {202},
  publisher = {Springer Science+Business Media},
  author    = {Lee, John M.},
  year      = {2011},
  keywords  = {Topological manifolds},
  file      = {Lee - Introduction to topological manifolds
               (2011).pdf:/Users/zcranko/Documents/Library/Book/Lee -
               Introduction to topological manifolds
               (2011).pdf:application/pdf}
}

@incollection{Lee_MinimaxStatistical_2018,
  title     = {Minimax statistical learning with wasserstein distances},
  booktitle = {Advances in neural information processing systems 31},
  publisher = {Curran Associates, Inc.},
  author    = {Lee, Jaeho and Raginsky, Maxim},
  editor    = {Bengio, S. and Wallach, H. and Larochelle, H. and Grauman,
               K. and Cesa-Bianchi, N. and Garnett, R.},
  year      = {2018},
  pages     = {2687--2696},
  file      = {Full
               Text:/Users/zcranko/Documents/Zotero/storage/4EI2AG8R/Lee
               and Raginsky - 2018 - Minimax statistical learning with
               wasserstein dist.pdf:application/pdf}
}

@inproceedings{LeeBarWil94,
  author    = {W. S. Lee and P. L. Bartlett and R.~C.~Williamson},
  title     = {Lower bounds on the {VC}-dimension of smoothly
               parametrized function classes},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {362--367},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{LeeBarWil95,
  author    = {W.~S.~Lee and P.~L.~Bartlett and R.~C.~Williamson},
  title     = {On efficient agnostic learning of linear combinations of
               basis functions},
  booktitle = {Proc.~8th Annu.~Workshop on Comput.~Learning Theory},
  year      = {1995},
  pages     = {369--376},
  address   = {New York},
  publisher = {ACM Press}
}

@article{LeeBarWil96,
  author  = {W.~S.~Lee and P.~L.~Bartlett and R.~C.~Williamson},
  title   = {Efficient agnostic learning of neural networks with
             bounded fan-in},
  journal = {IEEE Transactions on Information Theory},
  year    = {1996},
  volume  = {42},
  pages   = {2118--2132},
  number  = {6},
  month   = nov
}

@inproceedings{LeeBarWil96b,
  author    = {W.~S.~Lee and P.~L.~Bartlett and R.~C.~Williamson},
  title     = {The Importance of Convexity in Learning with Squared
               Loss},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1996},
  pages     = {1400--146},
  publisher = {ACM Press}
}

@article{LeeBarWil98,
  author  = {W.~S.~Lee and P.~L.~Bartlett and R.~C.~Williamson},
  title   = {The Importance of Convexity in Learning with Squared
             Loss},
  journal = {IEEE Transactions on Information Theory},
  year    = {1998},
  volume  = {44},
  pages   = {1974--1980},
  number  = {5}
}

@article{LeeBucAro98,
  author    = {Yongwon Lee and Bruce G. Buchanan and John M. Aronis},
  title     = {Knowledge-based learning in exploratory science: learning
               rules to predict rodent carcinogenicity},
  journal   = {Machine Learning},
  year      = {1998},
  volume    = {30},
  pages     = {217--240},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{LeeChaVicKol07,
  title        = {Learning a meta-level prior for feature relevance from
                  multiple related tasks},
  author       = {Lee, Su-In and Chatalbashev, Vassil and Vickrey, David and
                  Koller, Daphne},
  booktitle    = icml,
  pages        = {489--496},
  year         = {2007},
  organization = {ACM}
}

@article{LeeChuSea97,
  author  = {D.L. Lee and H. Chuang and K. Seamons},
  title   = {Document Ranking and the Vector Space Model},
  journal = {{IEEE} Transactions on Software},
  year    = {1997},
  volume  = {14},
  number  = {2},
  pages   = {67-75}
}

@inproceedings{Leeetal09,
  title     = {Exponential Family Sparse Coding with Application to
               Self-taught Learning},
  author    = {Honglak Lee and Rajat Raina and Alex Teichman and Andrew
               Y. Ng},
  booktitle = ijcai,
  year      = {2009},
  editor    = {C. Boutilier},
  pages     = {1113--1119},
  url       = {http://ijcai.org/papers09/Papers/IJCAI09-188.pdf}
}

@inproceedings{LeeGanKol06,
  author    = {S. Lee and V. Ganapathi and D. Koller},
  title     = {Efficient Structure Learning of Markov Networks using
               L1-Regularization},
  booktitle = {NIPS},
  year      = {2006}
}

@inproceedings{LeeGanKol07,
  title     = {Efficient Structure Learning of Markov Networks using
               L1-Regularization},
  author    = {S.I. Lee and V. Ganapathi and D. Koller},
  booktitle = {Advances in Neural Information Processing Systems 19},
  editor    = {B. {Sch\"olkopf} and J. Platt and T. Hofmann},
  publisher = {{MIT} Press},
  address   = {Cambridge, MA},
  pages     = {817--824},
  year      = {2007}
}

@article{LeeGirBelSej00,
  author  = {T.-W. Lee and M. Girolami and A. Bell and T. Sejnowski},
  title   = {A Unifying Framework for Independent Component Analysis},
  journal = {Comput. Math. Appl.},
  year    = {2000},
  volume  = {39},
  pages   = {1--21}
}

@article{LeeGirSej99,
  author  = {T.-W. Lee and M. Girolami and T. Sejnowski},
  title   = {Independent Component Analysis using an Extended {I}nfomax
             Algorithm for Mixed Sub-Gaussian and Super-Gaussian
             Sources},
  journal = {Neural Computation},
  year    = {1999},
  volume  = {11},
  pages   = {409--433},
  number  = {2}
}

@article{LeeJenPaiTsaetal06,
  author  = {W. P. Lee and B. C. Jeng and T. W. Pai and C. P. Tsai and
             C. Y. Yu and W. S. Tzou},
  title   = {Differential evolutionary conservation of motif modes in
             the yeast protein interaction network},
  journal = {BMC Genomics},
  volume  = {7},
  number  = {},
  pages   = {89},
  month   = {},
  year    = {2006}
}

@article{LeeLee03,
  author  = {Y. Lee and C. K. Lee},
  title   = {Classification of multiple cancer types by multicategory
             support vector machines using gene expression data},
  journal = {Bioinformatics},
  year    = {2003},
  volume  = {19},
  pages   = {1132-1139},
  number  = {9},
  month   = {Jun},
  address = {Department of Statistics, The Ohio State University,
             Columbus, OH 43210, USA.}
}

@techreport{LeeLinWah01,
  author      = {Lee, Y and Lin, Y. and Wahba, G. },
  title       = {Multicategory Support Vector Machines},
  institution = {University of Madison Winsconsin},
  year        = {2001},
  number      = {TR 1040},
  month       = {July}
}

@article{LeeLiWah04,
  author  = {Y. Lee and Y. Li and G. Wahba},
  title   = {Multicategory Support Vector Machines: Theory and
             Application to the Classification of Microarray Data and
             Satellite Radiance Data},
  journal = {Journal of the American Statistical Association},
  year    = {2004},
  volume  = {99},
  number  = {465},
  pages   = {67--81}
}

@article{LeeMah88,
  author  = {K. Lee and S. Mahajan},
  title   = {A Pattern Classification Approach to Evaluation Function
             Learning},
  journal = {Artificial Intelligence},
  year    = {1988},
  volume  = {36},
  pages   = {1--26},
  number  = {1},
  month   = aug,
  comment = {Learns Othello evaluation function based on four features
             and multivariate normal distribution assumption.}
}

@inproceedings{LeeMajRavetal19,
  author    = {Kwonjoon Lee and Subhransu Maji and Avinash Ravichandran
               and Stefano Soatto},
  title     = {Meta-Learning with Differentiable Convex Optimization},
  booktitle = cvpr,
  year      = {2019}
}

@article{LeeMan01,
  author  = {Y.J. Lee and O.L. Mangasarian},
  title   = {{SSVM}: A smooth support vector machine for
             classification},
  journal = {Computational optimization and Applications},
  year    = {2001},
  volume  = {20},
  pages   = {5--22},
  number  = {1}
}

@article{Leen95,
  author  = {T.~K.~Leen},
  title   = {From Data Distribution to Regularization in Invariant
             Learning},
  journal = {Neural Computation},
  year    = {1995},
  volume  = {7},
  pages   = {974--981},
  number  = {5},
  type    = {Letter}
}

@article{LeeQiaJonBaretal90,
  author  = {Y. C. Lee and S. Qian and R. D. Jones and C. W. Barnes and
             G. W. Flake and M. K. O'Rourke and K. Lee and H. H. Chen
             and G. Z. Sun and Y. Q. Zhang and D. Chen and C. L. Giles},
  title   = {Adaptive Stochastic Cellular Automata: Theory},
  journal = {Phyisca D},
  year    = {1990},
  volume  = {666},
  comment = {Nice survey of convergence results for stochastic learning
             automata.}
}

@inproceedings{LeeSeu01,
  author    = {D. Lee and H. Seung},
  title     = {Algorithms for non-negative matrix factorization},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  publisher = {{MIT} Press}
}

@article{LeeSeung99,
  author  = {Lee, Daniel D and Seung, H Sebastian},
  title   = {Learning the Parts of Objects by Non-Negative Matrix
             Factorization},
  journal = {Nature},
  year    = {1999},
  volume  = {401},
  pages   = {788--791}
}

@article{LeeSto00,
  author  = {W. Lee and S.~J. Stolfo},
  title   = {A Framework for Constructing Features and Models for
             Intrusion Detection Systems},
  journal = {ACM Transactions on Information and System Security},
  year    = {2000},
  volume  = {3},
  pages   = {227--261},
  number  = {4}
}

@article{LeeStoMok00,
  author  = {W. Lee and S. Stolfo and K. Mok},
  title   = {Adaptive Intrusion Detection: {A} Data Mining Approach},
  journal = {Artificial Intelligence Review},
  year    = {2000},
  volume  = {14},
  number  = {6},
  month   = dec
}

@inproceedings{LeeStoMok99,
  author    = {Wenke Lee and Salvatore J. Stolfo and Kui W. Mok},
  title     = {A Data Mining Framework for Building Intrusion Detection
               Models},
  booktitle = {{IEEE} Symposium on Security and Privacy},
  year      = {1999},
  pages     = {120-132}
}

@inproceedings{LeeTerWat95,
  author    = {K.~Lee and Y.~Terzopoulos and D.~Waters},
  title     = {Realistic Facial Modeling for Animation},
  booktitle = {Computer Graphics {\rm Proceedings, Annual Conference
               Series}},
  year      = {1995},
  series    = {Proc.~SIGGRAPH '95 (Los Angeles, CA)},
  pages     = {55--62},
  month     = aug,
  publisher = {ACM SIGGRAPH}
}

@book{leeverleysen10,
  title     = {Nonlinear Dimensionality Reduction},
  publisher = {Springer},
  year      = {2010},
  author    = {J. Lee and M. Verleysen}
}

@inproceedings{LeeWanJiaetal06,
  author    = {Chi-Hoon Lee and Shaojun Wang and Feng Jiao and Dale
               Schuurmans and Russell Greiner},
  title     = {Learning to Model Spatial Dependency: Semi-Supervised
               Discriminative Random Fields},
  booktitle = nips,
  year      = {2006}
}

@inproceedings{LeeWil96,
  author    = {P.~L. Bartlett W.~S. Lee and R.~C. Williamson},
  title     = {The Importance of Convexity in Learning with Squared
               Loss},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1996},
  pages     = {1400--146},
  publisher = {ACM Press}
}

@inproceedings{LeeXieGaletal15,
  title     = {{Deeply-Supervised Nets}},
  author    = {Chen-Yu Lee and Saining Xie and Patrick Gallagher and
               Zhengyou Zhang and Zhuowen Tu},
  booktitle = aistats,
  year      = {2015}
}

@inproceedings{LegHut07,
  author       = {Shane Legg and Marcus Hutter},
  title        = {Tests of Machine Intelligence},
  booktitle    = {50 Years of Artificial Intelligence},
  booksubtitle = {Essays Dedicated to the 50th Anniversary of Artificial
                  Intelligence},
  address      = {Monte Verita, Switzerland},
  series       = {LNAI},
  volume       = {4850},
  editor       = {M. Lungarella, F. Iida, J. Bongard, R. Pfeifer},
  pages        = {232--242},
  month        = dec,
  year         = {2007},
  bibtex       = {http://www.hutter1.net/official/bib.htm#intest},
  url          = {http://arxiv.org/abs/0712.3825},
  pdf          = {http://www.hutter1.net/ai/intest.pdf},
  ps           = {http://www.hutter1.net/ai/intest.ps},
  latex        = {http://www.hutter1.net/ai/intest.tex},
  poster       = {http://www.hutter1.net/ai/siors.pdf},
  project      = {http://www.hutter1.net/official/projects.htm#uai},
  press        = {http://www.hutter1.net/official/press.htm#mim},
  doi          = {10.1007/978-3-540-77296-5_22},
  issn         = {0302-9743},
  isbn         = {978-3-540-77295-8},
  keywords     = {Turing test and derivatives; Compression tests; Linguistic
                  complexity; Multiple cognitive abilities; Competitive
                  games; Psychometric tests; Smith's test; C-test; Universal
                  intelligence},
  abstract     = {Although the definition and measurement of intelligence is
                  clearly of fundamental importance to the field of
                  artificial intelligence, no general survey of definitions
                  and tests of machine intelligence exists. Indeed few
                  researchers are even aware of alternatives to the Turing
                  test and its many derivatives. In this paper we fill this
                  gap by providing a short survey of the many tests of
                  machine intelligence that have been proposed.},
  support      = {SNF grant 200020-107616}
}

@article{LegHut07b,
  author   = {Shane Legg and Marcus Hutter},
  title    = {Universal Intelligence: A Definition of Machine
              Intelligence},
  volume   = {17},
  number   = {4},
  journal  = {Minds \& Machines},
  pages    = {391--444},
  month    = dec,
  year     = {2007},
  bibtex   = {http://www.hutter1.net/official/bib.htm#iorx},
  url      = {http://arxiv.org/0712.3329},
  pdf      = {http://www.hutter1.net/ai/iorx.pdf},
  ps       = {http://www.hutter1.net/ai/iorx.ps},
  latex    = {http://www.hutter1.net/ai/iorx.zip},
  poster   = {http://www.hutter1.net/ai/sior.pdf},
  project  = {http://www.hutter1.net/official/projects.htm#uai},
  press    = {http://www.hutter1.net/official/press.htm#mim},
  doi      = {10.1007/s11023-007-9079-x},
  issn     = {0924-6495},
  keywords = {AIXI, complexity theory, intelligence, theoretical
              foundations, Turing test, intelligence
              tests/measures/definitions},
  abstract = {A fundamental problem in artificial intelligence is that
              nobody really knows what intelligence is. The problem is
              especially acute when we need to consider artificial
              systems which are significantly different to humans. In
              this paper we approach this problem in the following way:
              We take a number of well known informal definitions of
              human intelligence that have been given by experts, and
              extract their essential features. These are then
              mathematically formalised to produce a general measure of
              intelligence for arbitrary machines. We believe that this
              equation formally captures the concept of machine
              intelligence in the broadest reasonable sense. We then show
              how this formal definition is related to the theory of
              universal optimal learning agents. Finally, we survey the
              many other tests and definitions of intelligence that have
              been proposed for machines.},
  support  = {SNF grant 200020-107616}
}

@inproceedings{LegHut07c,
  author    = {Shane Legg and Marcus Hutter},
  title     = {A Collection of Definitions of Intelligence},
  booktitle = {Advances in Artificial General Intelligence: Concepts,
               Architectures and Algorithms},
  series    = {Frontiers in Artificial Intelligence and Applications},
  volume    = {157},
  pages     = {17--24},
  editor    = {B. Goertzel and P. Wang},
  publisher = {IOS Press},
  address   = {Amsterdam, NL},
  month     = jun,
  year      = {2007},
  bibtex    = {http://www.hutter1.net/official/bib.htm#idefs},
  url       = {http://arxiv.org/abs/0706.3639},
  http      = {http://www.idsia.ch/~shane/intelligence.html},
  pdf       = {http://www.hutter1.net/ai/idefs.pdf},
  ps        = {http://www.hutter1.net/ai/idefs.ps},
  latex     = {http://www.hutter1.net/ai/idefs.tex},
  project   = {http://www.hutter1.net/official/projects.htm#uai},
  isbn      = {978-1-58603-758-1},
  issn      = {0922-6389},
  keywords  = {intelligence definitions, collective, psychologist,
               artificial, universal},
  abstract  = {This chapter is a survey of a large number of informal
               definitions of ``intelligence'' that the authors have
               collected over the years. Naturally, compiling a complete
               list would be impossible as many definitions of
               intelligence are buried deep inside articles and books.
               Nevertheless, the 70-odd definitions presented here are, to
               the authors' knowledge, the largest and most well
               referenced collection there is.},
  support   = {SNF grant 200020-107616}
}

@book{LehCas98,
  author    = {E.L. Lehmann and G. Casella},
  title     = {{Theory of Point Estimation}},
  publisher = {Springer Verlag},
  isbn      = {0387985026},
  year      = 1998
}

@book{Lehmann59,
  title     = {Testing Statistical Hypothesis},
  publisher = {Wiley, New York},
  year      = {1959},
  author    = {Lehmann, E. L.}
}

@article{Lehmann77,
  author  = {Daniel J. Lehmann},
  title   = {Algebraic Structures for Transitive Closure},
  journal = {Theoretical Computer Science},
  year    = 1977,
  volume  = 4,
  number  = 1,
  pages   = {59--76},
  month   = {February}
}

@book{Lehmann83,
  title     = {Theory of Point Estimation},
  publisher = wiley,
  year      = {1983},
  author    = {Erich Lehmann},
  address   = {New York}
}

@article{LeiEtSch08,
  author    = {Bastian L. and A. Ettlin and B. Schiele},
  title     = {Learning semantic object parts for object categorization},
  journal   = {Image Vision Comput.},
  volume    = {26},
  number    = {1},
  year      = {2008},
  issn      = {0262-8856},
  pages     = {15--26},
  doi       = {http://dx.doi.org/10.1016/j.imavis.2007.08.012},
  publisher = {Butterworth-Heinemann},
  address   = {Newton, MA, USA}
}

@inproceedings{LeiLeoSch04,
  author    = {B. Leibe and A. Leonardis and B. Schiele},
  title     = {Combined object categorization and segmentation with an
               implicit shape model},
  booktitle = {the Workshop on Statistical Learning in Computer Vision},
  month     = {May},
  year      = {2004},
  address   = {Prague, Czech Republic}
}

@inproceedings{LeiLiaPog14,
  title        = {Subtasks of unconstrained face recognition},
  author       = {Leibo, Joel Z and Liao, Qianli and Poggio, Tomaso},
  booktitle    = {Computer Vision Theory and Applications (VISAPP), 2014
                  International Conference on},
  volume       = {2},
  pages        = {113--121},
  year         = {2014},
  organization = {IEEE}
}

@article{LeiNew08,
  title     = {{Community structure in directed networks}},
  author    = {Leicht, E.A. and Newman, M.E.J.},
  journal   = {Physical Review Letters},
  volume    = {100},
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@book{Liu01,
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@article{Liu04,
  author  = {Chuanhai Liu},
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@inproceedings{Liu07,
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  year      = {2015}
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@unpublished{LiuCheChe90,
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  year    = {2015}
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  keywords  = {data\_mining, statistical\_learning},
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  number    = {474},
  pages     = {500--509},
  publisher = {American Statistical Association},
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  title     = {Multicategory psi-Learning and Support Vector Machine:
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  year      = {2004}
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  keywords  = {benchmark, dataset, features, letor, ranking, trec},
  title     = {LETOR: Benchmark Dataset for Research on Learning to Rank
               for Information Retrieval},
  url       = {http://research.microsoft.com/en-us/um/beijing/projects/letor/Paper/LR4IR-LETOR.pdf},
  year      = {2007}
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@inproceedings{LiuYe09,
  author    = {Jun Liu and Jieping Ye},
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@inproceedings{LiuYuaYe10,
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  series    = {Proceedings of machine learning research},
  title     = {Boosting variational inference: an optimization
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  abstract  = {Variational inference is a popular technique to
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               greedily adding components to the mixture. However, as is
               the case with many other variational inference algorithms,
               its theoretical properties have not been studied. In the
               present work, we study the convergence properties of this
               approach from a modern optimization viewpoint by
               establishing connections to the classic Frank-Wolfe
               algorithm. Our analyses yields novel theoretical insights
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               lot of focus in previous works for variational inference
               has been on tractability, our work is especially important
               as a much needed attempt to bridge the gap between
               probabilistic models and their corresponding theoretical
               properties.},
  booktitle = {Proceedings of the twenty-first international conference
               on artificial intelligence and statistics},
  publisher = {PMLR},
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               and Ratsch, Gunnar},
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  year      = {2018},
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  editor    = {T.~K.~Leen and T.~G.~Dietterich and V.~Tresp},
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  author = {P. M. Long},
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  school = {UC Santa Cruz},
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@inproceedings{Long96,
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  booktitle = alt-ws,
  year      = {1996},
  volume    = {1160},
  series    = {Lecture Notes in Artificial Intelligence},
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  publisher = {Springer-Verlag}
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@article{Long97,
  author    = {P. M. Long},
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  journal   = {Machine Learning},
  year      = {1997},
  volume    = {27},
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  publisher = {Kluwer Academic Publishers, Boston}
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@inproceedings{Long97b,
  author    = {P. M. Long},
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  year      = {1997},
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  publisher = {ACM Press, New York, NY}
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  year      = {1998},
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  publisher = {ACM Press}
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  author    = {P. M. Long},
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  journal   = {Machine Learning},
  year      = {1999},
  volume    = {37},
  pages     = {337--354},
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  author    = {B. Long and P. Yu and Z. Zhang},
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  author  = {Phil Long and Rocco Servedio},
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  journal = {Machine Learning Journal},
  year    = 2010,
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  number  = 3,
  pages   = {287--304}
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}

@inproceedings{LonSue03,
  author    = {X. Long and T. Suel},
  title     = {Optimized Query Execution in Large Search Engines with
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  editor    = {J.C. Freytag and P.C. Lockemann and S. Abiteboul and M.J.
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  publisher = {Morgan Kaufmann Publishers},
  pages     = {129--140},
  year      = {2003}
}

@article{LonTan98,
  author    = {P. M. Long and Lei Tan},
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  journal   = {Machine Learning},
  year      = {1998},
  volume    = {30},
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  note      = {Earlier version in COLT96},
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  author  = {P. M. Long and M. K. Warmuth},
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  note    = {To Appear},
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  doi     = {10.1090/s0002-9947-01-02664-2},
  number  = {10},
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@article{Luc_LevelsSets_1997,
  title    = {Levels {Sets} {Infimal} {Convolution} and {Level}
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  volume   = {94},
  issn     = {1573-2878},
  doi      = {10.1023/a:1022657102069},
  abstract = {A sufficient criterion is established for the infimal
              convolution of two functions having connected level sets to
              share the same property without being exact. As a
              consequence, the infimal convolution of quasiconvex
              functions on a real line is quasiconvex. However, this is
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              illustrated by an example in R2. Furthermore, connectedness
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              with local-global minimum properties.},
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  file     = {Luc, Volle - Levels Sets Infimal Convolution and Level
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  issn     = {1572-932X},
  doi      = {10.1023/a:1014458603461},
  abstract = {We develop the concept of recessive compactness recently
              introduced by Luc and Penot. Then we employ this idea to
              extend some important results of functional analysis such
              as closed image criteria, a theorem on a family of
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              existence condition for a variational inequality problem on
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              a Hilbert space without a-priori estimates for the
              solutions of the equation to stay in a bounded region.},
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  year     = {2002},
  pages    = {15--35},
  file     = {Luc - Recessively compact sets
              (2002).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Luc - Recessively compact sets
              (2002).pdf:application/pdf}
}

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  language  = {en},
  number    = {319},
  publisher = {Springer-Verlag},
  author    = {Luc, Dinh The},
  year      = {1989},
  note      = {OCLC: 263110952},
  keywords  = {asymptotic cones},
  file      = {Luc - Theory of vector optimization
               (1989).pdf:/Users/zcranko/Documents/Library/Book/Luc -
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}

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@article{Ma2018CharacterizingAS,
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@inproceedings{MaaDalPha11,
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@article{MaaHin08,
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  year    = {2008},
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  pages   = {2579--2605}
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@inproceedings{MaaSch97,
  author    = {Wolfgang Maass and M. Schmitt},
  title     = {On the Complexity of Learning for a Spiking Neuron},
  booktitle = {Proc. 10th Annu. Conf. on Comput. Learning Theory},
  year      = {1997},
  pages     = {54--61},
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@article{MaaSch99,
  author  = {W. Maass and M. Schmitt},
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  volume  = {153},
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@inproceedings{MaaSchSon91,
  author    = {W. Maass and G. Schnitger and E. D. Sontag},
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@article{Maass01,
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@inproceedings{Maass91,
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@article{Maass94,
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@inproceedings{Maass94b,
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  year      = {1994},
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@article{Maass95,
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  number  = {5}
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@article{Maass97,
  author  = {Wolfgang Maass},
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  journal = {{SIAM} J. Comput.},
  year    = {1997},
  volume  = {26},
  pages   = {708--732},
  number  = {3},
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@inproceedings{Maass97b,
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  booktitle = alt-ws,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {364--384},
  publisher = {Springer-Verlag}
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@inproceedings{MaaTur89,
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  year      = {1989},
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@inproceedings{MaaTur90,
  author    = {W. Maass and G. Tur{\'a}n},
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  year      = {1990},
  pages     = {203--210},
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@article{MaaTur92,
  author    = {Wolfgang Maass and Gy{\"{o}}rgy Tur{\'{a}}n},
  title     = {Lower Bound Methods and Separation Results for On-Line
               Learning Models},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {9},
  pages     = {107--145},
  publisher = {Kluwer Academic Publishers, Boston}
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@article{MaaTur94,
  author    = {Wolfgang Maass and Gy{\"{o}}rgy Tur{\'{a}}n},
  title     = {Algorithms and Lower Bounds for On-Line Learning of
               Geometrical Concepts},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {14},
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  chapter   = {13},
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  publisher = {{MIT} Press},
  year      = {1994},
  author    = {Wolgang Maass and Gy{\"o}rgy Tur{\'a}n},
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               I: Constraints and Prospects}
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@inproceedings{MaaWar95,
  author    = {Wolfgang Maass and M. K. Warmuth},
  title     = {Efficient learning with virtual threshold gates},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {378--386},
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@inproceedings{MaaWelSau11,
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  booktitle = {Proc. International Conference on Artificial Intelligence
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  year      = {2011},
  url       = {http://cseweb.ucsd.edu/~lvdmaaten/hucrf/Hidden-Unit_Conditional_Random_Fields.html}
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@inproceedings{MaCBel05,
  author    = {Andrew Mccallum and Kedar Bellare},
  title     = {A conditional random field for discriminatively-trained
               finite-state string edit distance},
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@article{MacFarlane30,
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@inproceedings{MaCFre00,
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  title     = {Maximum Entropy Markov Models for Information Extraction
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@book{MacKay03,
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@phdthesis{MacKay91,
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@article{MacKay92,
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@article{MacKay92b,
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@article{MacKay94,
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@unpublished{MacKay97,
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@incollection{MacKay98,
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@incollection{MacKay98b,
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@misc{MacKay99,
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@article{MacKay99b,
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@inproceedings{MacOpi97,
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@inproceedings{MaCPalDruWan06,
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@incollection{MacQueen67,
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@article{MacSha93,
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@inbook{MacSha94,
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@article{MacSha96,
  author    = {R. Maclin and Jude W. Shavlik},
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@article{MacSiaKau96,
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@inproceedings{MacSon93,
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@inproceedings{MacZecBet08,
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  year      = {2008},
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@inproceedings{MadBerRah01,
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@inproceedings{MadCrePitZem18,
  title     = {Learning adversarially fair and transferable
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@book{Maddox70,
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@book{Maddox88,
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@inproceedings{MadMakSchetal18,
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@article{MadMakSchTsietal18,
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@unpublished{MadNel83,
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@article{MadNel90b,
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@inproceedings{Madry_DeepLearning_2018,
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  author    = {Madry, Aleksander and Makelov, Aleksandar and Schmidt,
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  keywords  = {⛔ No DOI found},
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@inproceedings{MadTetCho12,
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  booktitle = {the 2012 Conference of the North American Chapter of the
               Association for Computational Linguistics: Human Language
               Technologies},
  year      = {2012},
  pages     = {182--190},
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@article{MadYor95,
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@inproceedings{MaeBro90,
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  volume    = {2},
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@inproceedings{MaeDenGal07,
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  title     = {XML Structure Mapping Application to the {PASCAL/INEX}
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  booktitle = {Advances in XML Information Retrieval and Evaluation:
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               {XML} Retrieval (INEX'06)},
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@article{MaeMur87,
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  volume  = {MI-6},
  pages   = {320--324},
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@article{Maetal11,
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  volume  = {128},
  pages   = {321--353}
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@inproceedings{MaGanZha19,
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  title     = {Learning Invariant Representations with Kernel Warping},
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@inproceedings{Magerman96,
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@book{MagNeu99,
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@techreport{Mangasarian97b,
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}

@techreport{Mangasarian98,
  author      = {O.~L.~Mangasarian},
  title       = {Generalized Support Vector Machines},
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  year        = {1999}
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  volume    = {11},
  pages     = {955--984},
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@article{ManMey79,
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@inproceedings{ManMus00,
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@article{ManMus01,
  author  = {O.~L.~Mangasarian and D.~R.~Musicant},
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  year    = {2001},
  volume  = {1},
  pages   = {161--177}
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@techreport{ManMus98,
  author      = {O.~L.~Mangasarian and D.~R.~Musicant},
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  year        = {1998},
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@article{ManMye93,
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@incollection{Mannetal09,
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@inproceedings{Mannila96,
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@proceedings{ManPla00,
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@article{ManPug05,
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}

@article{ManSah00,
  author    = {Yishay Mansour and Sigal Sahar},
  title     = {Implementation Issues in the Fourier Transform Algorithm},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {40},
  pages     = {5--33},
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@inproceedings{ManSch97,
  author    = {Yishay Mansour and Mariano Schain},
  title     = {Learning with Maximum-Entropy Distributions},
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  year      = {1997},
  pages     = {201--210},
  publisher = {ACM Press, New York, NY}
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@book{ManSch99,
  title     = {Foundations of statistical natural language processing},
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@article{ManShaWil04,
  author  = {O.~L.~Mangasarian and J.~W.~Shavlik and E.~W.~Wild},
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  volume  = {5}
}

@unpublished{Mansour90,
  author = {Y. Mansour},
  title  = {Learning via Fourier Transform},
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  month  = apr,
  year   = {1990}
}

@inproceedings{Mansour97,
  author    = {Yishay Mansour},
  title     = {Pessimistic decision tree pruning based on tree size},
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  year      = {1997},
  pages     = {195--201},
  publisher = {Morgan Kaufmann}
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@inproceedings{Mansour99,
  author    = {Yishay Mansour},
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  year      = {1999},
  pages     = {183--192},
  publisher = {ACM Press, New York, NY}
}

@article{ManSto71,
  author  = {B. Manvel and P.K. Stockmeyer},
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  year    = {1971},
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  number  = {4},
  pages   = {218-221},
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}

@article{Mantaras91,
  author    = {R. L{\'{o}}pez De M{\'{a}}ntaras},
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               Tree Induction},
  journal   = {Machine Learning},
  year      = {1991},
  volume    = {6},
  pages     = {81--92},
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}

@techreport{ManWil05,
  author      = {O.~L. Mangasarian and E.~W. Wild},
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  year        = {2005},
  pages       = {1127--1141}
}

@article{ManYou01,
  author  = {L.~M. Manevitz and M. Yousef},
  title   = {One-Class {SVMs} for Document Classification},
  journal = {Journal of Machine Learning Research},
  year    = {2001},
  volume  = {2},
  pages   = {139--154},
  month   = {December}
}

@inproceedings{Manzinni04,
  author    = {Giovanni~Manzinni},
  title     = {Two Space Saving Tricks for Linear Time LCP Array
               Computation},
  booktitle = {Proc. 9th Scandinavian Workshop on Algorithm Theory
               (SWAT'04)},
  year      = {2004},
  volume    = {3111},
  series    = {LCNS},
  pages     = {372--383},
  address   = {Humlebaek, Denmark},
  month     = {july},
  publisher = {Springer-Verlag},
  editor    = {Torben~Hagerup and Jyrki~Karajainen}
}

@inproceedings{MarAst16,
  author    = {Andre Martins and Ramon Astudillo },
  title     = {From Softmax to Sparsemax: A Sparse Model of Attention and
               Multi-Label Classification},
  booktitle = icml,
  year      = 2016
}

@article{MarBil94,
  author    = {Joel D. Martin and Dorrit O. Billman},
  title     = {Acquiring and Combining Overlapping Concepts},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {16},
  pages     = {121--155},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{MarBoo77,
  author  = {F. J. Maryanski and T. L. Booth},
  title   = {Inference of finite-state probabilistic grammars},
  journal = {IEEE Trans. on Computers},
  year    = {1977},
  volume  = {C-26},
  pages   = {521--536}
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@article{MarBra03,
  author  = {Martinez and Bray},
  title   = {Nonlinear Blind Source Separation Using Kernels},
  journal = {IEEE Transactions on Neural Networks},
  year    = {2003},
  volume  = {14},
  number  = {1}
}

@book{Marchette01,
  title     = {Computer Intrusion Detection and Network Monitoring: A
               Statistical Viewpoint},
  publisher = {Springer},
  year      = {2001},
  author    = {D.~J. Marchette},
  series    = {Statistics for Engineering and Information Science}
}

@inproceedings{MarDie00,
  author    = {Dragos D. Margineantu and T. G. Dietterich},
  title     = {Bootstrap Methods for the Cost-Sensitive Evaluation of
               Classifiers},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {583--590},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{MarDie97,
  author    = {Dragos D. Margineantu and T. G. Dietterich},
  title     = {Pruning adaptive boosting},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {211--218},
  publisher = {Morgan Kaufmann}
}

@book{MarFis03,
  author    = {Jane Margolis and Allan Fisher},
  title     = {Unlocking the Clubhouse: Women in Computing},
  publisher = {MIT press,},
  year      = {2003},
  address   = {Cambridge, MA}
}

@techreport{MarGir93,
  author      = {J.~Marroquin and F.~Girosi},
  title       = {Some Extensions of the {K}-Means Algorithm for Image
                 Segmentation and Pattern Classification},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1993},
  type        = {A.I.~Memo No.},
  number      = {1390}
}

@techreport{MarGirPog91,
  author      = {M.~Maruyama and F.~Girosi and T.~Poggio},
  title       = {Techniques for Learning from Examples: Numerical
                 Comparisons and Approximation Power},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1991},
  type        = {A.I.~Memo},
  number      = {1290},
  note        = {(in press)}
}

@techreport{MarGirPog91b,
  author      = {M.~Maruyama and F.~Girosi and T.~Poggio},
  title       = {A Connection between {HBF} and {MLP}},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1991b},
  type        = {A.I. Memo No.},
  number      = {1291},
  note        = {(in press)}
}

@article{MarGolRuj89,
  author  = {M.~Marchand and M.~Golea and P.~Ruj\'an},
  title   = {Convergence Theorem for Sequential Learning in Two Layer
             Perceptrons},
  journal = {Europhysics Letters},
  year    = {1989},
  volume  = {11},
  pages   = {487--492}
}

@techreport{MarHir96,
  author      = {J.~K.~Martin and D.~S.~Hirschberg},
  title       = {Small Sample Statisics for Classification Error Rates I:
                 Error Rate Measurements},
  institution = {Department of Information and Computer Science, UC
                 Irvine},
  year        = {1996},
  number      = {96-21}
}

@techreport{Marill89,
  author      = {T.~Marill},
  title       = {Computer perpception of three-dimensional objects},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1989},
  type        = {A.I.Memo},
  number      = {1136}
}

@article{Marill91,
  author  = {T.~Marill},
  title   = {Emulating the Human Interpretation of Line-Drawings as
             Three-Dimensional Objects},
  journal = {International Journal of Comuter Vision},
  year    = {1991},
  volume  = {6},
  pages   = {147--161}
}

@book{MarKenBib79,
  title     = {Multivariate Analysis},
  publisher = {Academic Press},
  year      = {1979},
  author    = {K.~V.~Mardia and J.~T.~Kent and J.~M.~Bibby}
}

@article{MarKo87,
  author  = {A. Marron and K. Ko},
  title   = {Identification of Pattern Languages from Examples and
             Queries},
  journal = {Inform. Comput.},
  year    = {1987},
  volume  = {74},
  pages   = {91--112},
  number  = {2},
  month   = aug
}

@book{Markov12,
  title     = {Wahrscheinlichkeitsrechnung},
  publisher = {B.G.~Teubner Verlag},
  year      = {1912},
  author    = {A.~A.~Markov},
  address   = {Leipzig}
}

@article{Markovich89,
  author  = {N.~M.~Markovich},
  title   = {Experimental analysis of nonparametric probability density
             estimates ans of methods for smoothing them},
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  year    = {1989},
  volume  = {7},
  pages   = {110--119},
  month   = jul
}

@mastersthesis{Marlin04,
  author = {B. Marlin},
  title  = {Collaborative Filtering: A machine learning perspective},
  school = {University of Toronto},
  year   = {2004},
  type   = {Masters Thesis}
}

@inproceedings{MarMar00,
  author    = {Zdravko Markov and Ivo Marinchev},
  title     = {Metric-Based Inductive Learning Using Semantic Height
               Functions},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {254--262},
  publisher = {Springer, Berlin}
}

@article{MarMar84,
  author  = {K.~V.~Mardia and R.~J.~Marshall},
  title   = {Maximum Likelihood Estimation for Models of Residual
             Covariance in Spatial Regression},
  journal = {Biometrika},
  year    = {1984},
  volume  = {71},
  pages   = {135--146},
  number  = {1}
}

@article{MarMarSan93,
  author  = {Marcus, Mitchell P. and Marcinkiewicz, Mary Ann and
             Santorini, Beatrice},
  title   = {Building a Large Annotated Corpus of English: The Penn
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  journal = {Comput. Linguist.},
  volume  = {19},
  number  = {2},
  year    = {1993},
  pages   = {313--330}
}

@article{MarMitPog87,
  author  = {J.~L.~Marroquin and S.~Mitter and T.~Poggio},
  title   = {Probabilistic Solution of Ill-Posed Problems in
             Computational Vision},
  journal = {Journal of the American Statistical Association},
  year    = {1987},
  volume  = {82},
  pages   = {76--89}
}

@techreport{MarMiy91,
  author      = {O. Maruyama and S. Miyano},
  title       = {Inferring a Tree from Walks},
  institution = {Kyushu University},
  year        = {1991},
  number      = {RIFIS-TR-CS-51},
  month       = dec
}

@article{MarNis78,
  author  = {D.~Marr and H.~K.~Nishihara},
  title   = {Representation and Recognition of the Spatial Organization
             of Three Dimensional Structure},
  journal = {the Royal Society of London B},
  year    = {1978},
  volume  = {200},
  pages   = {269--294}
}

@article{MarOlk60,
  author  = {A. W. Marshall and I. Olkin},
  title   = {Multivariate Chebyshev inequalities},
  journal = {Annals of Mathematical Statistics},
  year    = {1960},
  volume  = {31(4)},
  pages   = {1001--1014}
}

@article{Maron61,
  author  = {M.E. Maron},
  title   = {Automatic indexing: An experimental inquiry},
  journal = {Journal of the Association for Computing Machinery},
  year    = 1961,
  volume  = 8,
  pages   = {404-417}
}

@inproceedings{MarOsh95,
  author    = {Eric Martin and Daniel Osherson},
  title     = {A note on the use of probabilities by mechanical
               learners},
  booktitle = {Computational Learning Theory: EuroColt '95},
  year      = {1995},
  pages     = {261--271},
  publisher = {Springer-Verlag}
}

@article{MarOsh97,
  author  = {Eric Martin and Daniel N. Osherson},
  title   = {Scientific discovery based on belief revision},
  journal = {The Journal of Symbolic Logic},
  year    = {1997},
  volume  = {62},
  pages   = {1352--1370},
  number  = {4},
  month   = dec
}

@article{MarOsh98,
  author  = {Eric Martin and Daniel N. Osherson},
  title   = {Belief revision in the service of scientific discovery},
  journal = {Mathematical Social Sciences},
  year    = {1998},
  volume  = {36},
  pages   = {57--68},
  number  = {1}
}

@book{MarOsh98b,
  title     = {Elements of Scientific Inquiry},
  publisher = {{MIT} Press},
  year      = {1998},
  author    = {Eric Martin and Daniel N. Osherson},
  address   = {Cambridge, Massachusetts}
}

@article{MarPadRod00,
  author    = {L. M{\`a}rquez and L. Padr{\'o} and H. Rodriguez},
  title     = {A Machine Learning Approach to {POS} Tagging},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {39},
  pages     = {59--91},
  number    = {1},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{MarPog76,
  author  = {D.~Marr and T.~Poggio},
  title   = {Cooperative Computation of Stereo Disparity},
  journal = {Science},
  year    = {1976},
  volume  = {194},
  pages   = {283--287}
}

@article{Marr69,
  author  = {D.~Marr},
  title   = {A theory of cerebellar cortex},
  journal = {J.~Physiology},
  year    = {1969},
  volume  = {202},
  pages   = {437--470}
}

@book{Marr82,
  title     = {Vision: {A} Computational Investigation into the Human
               Representation and Processing of Visual Information},
  publisher = {W.~H.~Freeman and Company},
  year      = {1982},
  author    = {David Marr},
  address   = {San Francisco}
}

@article{MarRam91,
  author  = {J.~L.~Marroquin and A.~Ramirez},
  title   = {Stochastic Cellular Automata with Gibbsian Invariant
             Measures},
  journal = {IEEE Transactions on Information Theory},
  year    = {1991},
  note    = {(in press)}
}

@inproceedings{MarRat98,
  author    = {Oded Maron and Aparna Lakshmi Ratan},
  title     = {Multiple-instance learning for natural scene
               classification},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {341--349},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{Marron88,
  author    = {A. Marron},
  title     = {Learning pattern languages from a single initial example
               and from queries},
  booktitle = {Proc. 1st Annu. Workshop on Comput. Learning Theory},
  year      = {1988},
  pages     = {345--358},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{Marroquin92,
  author  = {J.~L.~Marroquin},
  title   = {Random Measure Fields and the Integration of Visual
             Information},
  journal = {IEEE Trans.~SMC},
  year    = {1992},
  volume  = {22},
  pages   = {705--716}
}

@article{MarSanMar93,
  author  = {Mitchell P. Marcus and Beatrice Santorini and Mary Ann
             Marcinkiewicz},
  title   = {Building a Large Annotated Corpus of English: The Penn
             Treebank},
  journal = {COMPUTATIONAL LINGUISTICS},
  year    = {1993},
  volume  = {19},
  number  = {2},
  pages   = {313--330}
}

@incollection{MarSch91,
  author    = {T.~Martinez and K.~Schulten},
  title     = {A {``Neural Gas'' Network Learns Topology}},
  booktitle = {Artificial Neural Networks},
  publisher = {Elsevier Science Publishers},
  year      = {1991},
  editor    = {T.~Kohonen and K.~Makisara and O.~Simula and J.~Kangas},
  pages     = {397--402}
}

@article{MarSch94,
  author  = {T.~Martinetz and K.~Schulten},
  title   = {Topology representing networks},
  journal = {Neural Networks},
  year    = {1994},
  volume  = {7},
  pages   = {507--522}
}

@article{MarSco93,
  author    = {Shaul Markovitch and Paul D. Scott},
  title     = {Information Filtering: Selection Mechanisms in Learning
               Systems},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {10},
  pages     = {113--151},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{MarSha01,
  author    = {M.~Marchand and J.~Shawe-Taylor},
  title     = {Learning with the Set Covering Machine},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2001},
  pages     = {345--352},
  address   = {San Francisco, California},
  publisher = {Morgan Kaufmann Publishers}
}

@article{Marshall04,
  author  = {E. Marshall},
  title   = {Getting the noise out of gene arrays},
  journal = {Science},
  year    = {2004},
  volume  = {306},
  pages   = {630-631},
  number  = {5696},
  month   = {Oct}
}

@article{Marshall91,
  author  = {R.M. Marshall},
  title   = {Mapping disease and mortality rates using Empirical Bayes
             Estimators},
  journal = {Applied Statistics},
  year    = {1991},
  volume  = {40},
  pages   = {283-294}
}

@inproceedings{MarTak98,
  author    = {A. Maruoka and E. Takimoto},
  title     = {Structured Weight-Based Prediction Algorithms},
  booktitle = alt,
  year      = {1998},
  volume    = {1501},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {127--142},
  publisher = {Springer-Verlag}
}

@inproceedings{MarThr98,
  author    = {Dimitris Margaritis and Sebastian Thrun},
  title     = {Learning to Locate an Object in {3D} Space from a Sequence
               of Camera Images},
  booktitle = icml,
  year      = {1998},
  pages     = {332--340},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{Martin00,
  author  = {R.J. Martin},
  title   = {A Metric for {ARMA} Processes},
  journal = {{IEEE} Trans. Signal Process.},
  year    = {2000},
  volume  = {48},
  pages   = {1164--1170},
  number  = {4}
}

@article{Martin97,
  author    = {J. Kent Martin},
  title     = {An exact probability metric for decision tree splitting
               and stopping},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {28},
  pages     = {257--291},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Martinek00,
  author    = {Pavel Martinek},
  title     = {An Inverse Limit of Context-Free Grammars - {A} New
               Approach to Identifiability in the Limit},
  booktitle = {Grammatical Inference: {A}lgorithms and {A}pplications,
               5th International Colloquium, ICGI 2000, Lisbon, Portugal,
               September 11--13, 2000 ; Proceedings},
  year      = {2000},
  volume    = {1891},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {171--185},
  publisher = {Springer, Berlin}
}

@inproceedings{Martinetz93,
  author    = {T.~M.~Martinetz},
  title     = {Competitive hebbian learning rule forms perfectly topology
               preserving maps},
  booktitle = {the International Conference on Artificial Neural
               Networks},
  year      = {1993},
  pages     = {427--434}
}

@article{Martinez-Legaz_EsubdifferentialsTerms_1996,
  title   = {ε-subdifferentials in terms of subdifferentials},
  volume  = {4},
  issn    = {0927-6947},
  doi     = {10.1007/bf00436109},
  number  = {4},
  journal = {Set-Valued Anal.},
  author  = {Martinez-Legaz, Juan-Enrique and Théra, Michel},
  year    = {1996},
  note    = {tex.fjournal: Set-Valued Analysis. An International
             Journal Devoted to the Theory of Multifunctions and its
             Applications tex.mrclass: 49J52 (47H05 47N10 54C60)
             tex.mrnumber: 1422399 tex.mrreviewer: Adam B. Levy},
  pages   = {327--332},
  file    = {Martinez-Legaz, Théra - ε-subdifferentials in terms of
             subdifferentials
             (1996).pdf:/Users/zcranko/Documents/Library/Journal
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}

@inproceedings{Martinez04,
  author    = {C. Mart{\'i}nez},
  title     = {Partial Quicksort},
  booktitle = {Proc. of the 6th ACM-SIAM Workshop on Algorithm
               Engineering and Experiments (ALENEX) and the 1st ACM-SIAM
               Workshop on Analytic Algorithmics and Combinatorics
               (ANALCO)},
  year      = 2004,
  editor    = {L. Arge and G.F. Italiano and R. Sedgewick},
  pages     = {224--228},
  isbn      = {0-89871-564-4},
  publisher = {SIAM}
}

@inproceedings{MarTsi99,
  author    = {Perter Marbach and John N. Tsitsiklis},
  title     = {Gradient-based Optimisation of {M}arkov Reward Processes:
               Practical Variants},
  booktitle = {38th IEEE Conference on Decisions and Control},
  year      = {1999},
  month     = {December}
}

@inproceedings{MarVra96,
  author    = {Lionel Martin and Christel Vrain},
  title     = {Induction of Constraint Logic Programs},
  booktitle = alt-ws,
  year      = {1996},
  volume    = {1160},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {169--176},
  publisher = {Springer-Verlag}
}

@inproceedings{MasAve07,
  author    = {P. Massa and P. Avesani},
  title     = {Trust-aware recommender systems},
  booktitle = {{ACM} conference on Recommender Systems {RecSys}},
  year      = {2007},
  isbn      = {978-1-59593-730--8},
  pages     = {17--24},
  location  = {Minneapolis, MN, USA},
  doi       = {http://doi.acm.org/10.1145/1297231.1297235},
  publisher = {ACM},
  address   = {New York, NY, USA}
}

@article{MasBarBax00,
  author    = {Llew Mason and Peter L. Bartlett and Jonathan Baxter},
  title     = {Improved Generalization Through Explicit Optimization of
               Margins},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {38},
  pages     = {243--255},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{MasBarBax99,
  author    = {L.~Mason and P.~L.~Bartlett and J.~Baxter},
  title     = {Direct optimization of margins improves generalization in
               combined classifiers},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {1999},
  editor    = {S.~A.~Solla and T.~K.~Leen and K.-R.~M{\"u}ller},
  pages     = {288--294},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{MasBarBax99b,
  author  = {L.~Mason and P.~L.~Bartlett and J.~Baxter},
  title   = {Improved generalization through explicit optimization of
             margins},
  journal = {Machine Learning},
  year    = {1999},
  note    = {(to appear\,---\,extended abstract in NIPS 98)}
}

@inproceedings{MasBaxBarFre00,
  author    = {Llew Mason and Jonathan Baxter and Peter Bartlett and
               Marcus Frean},
  title     = {Functional Gradient Techniques for Combining Hypotheses},
  booktitle = {Advances in Large Margin Classifiers},
  year      = {2000},
  editor    = {A. J. Smola and P.~L.~Bartlett and B.~Sch{\"o}lkopf and
               D.~Schuurmans},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press},
  pages     = {221--246}
}

@techreport{MasBaxBarFre99,
  author      = {Llew Mason and Jonathan Baxter and Peter Bartlett and
                 Marcus Frean},
  title       = {Boosting Algorithms as Gradient Descent in Function
                 Space},
  institution = {Research School of Information, ANU Canberra},
  year        = {1999}
}

@article{Masi05,
  author  = {M. Masi},
  journal = {Physics Letters A},
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  title   = {A Step Beyond {T}sallis and {R}enyi entropies},
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  year    = {2005}
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@article{Masi05b,
  author  = {M. Masi},
  journal = {Physics Letters A},
  pages   = {217--224},
  title   = {A Step Beyond {T}sallis and {R}enyi entropies},
  volume  = {338},
  year    = {2005}
}

@incollection{Masnadi-shirazi_DesignLoss_2009,
  title     = {On the {Design} of {Loss} {Functions} for
               {Classification}: theory, robustness to outliers, and
               {SavageBoost}},
  booktitle = {Advances in neural information processing systems 21},
  publisher = {Curran Associates, Inc.},
  author    = {Masnadi-shirazi, Hamed and Vasconcelos, Nuno},
  editor    = {Koller, D. and Schuurmans, D. and Bengio, Y. and Bottou,
               L.},
  year      = {2009},
  pages     = {1049--1056},
  file      = {Masnadi-shirazi, Vasconcelos - On the Design of Loss
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               (2009).pdf:/Users/zcranko/Documents/Library/Book
               Section/Masnadi-shirazi, Vasconcelos - On the Design of
               Loss Functions for Classification
               (2009).pdf:application/pdf}
}

@inproceedings{Masonetal00,
  author    = {Llew Mason and Jonathan Baxter and Peter L. Bartlett and
               Marcus Frean},
  title     = {Functional Gradient Techniques for Combining Hypotheses},
  booktitle = {Advances in Large Margin Classifiers},
  year      = {2000},
  pages     = {221--246}
}

@inproceedings{MasVas08,
  author    = {H. Masnadi-Shirazi and N. Vasconcelos},
  title     = {On the design of loss functions for classification:
               theory, robustness to outliers, and {SavageBoost}},
  year      = {2008},
  pages     = {1049--1056},
  booktitle = nips21,
  editor    = nips21e,
  publisher = mitpr
}

@inproceedings{MasVasMah10,
  author    = {H. Masnadi-Shirazi and N. Vasconcelos and V. Mahadevan},
  title     = {On the design of robust classifiers for computer vision},
  booktitle = cvpr,
  pages     = {779--786},
  year      = {2010}
}

@mastersthesis{Matar93,
  author = {S. Matar},
  title  = {Learning with Minimal Number of Queries},
  school = {University of Alberta, Canada},
  year   = {1993}
}

@incollection{Mataric91,
  author    = {M.~J.~Mataric},
  title     = {Navigating with a rat brain: a neurobiologically-inspired
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@inproceedings{McCWel04,
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  title     = {On the method of bounded differences},
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  year      = {1989},
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@article{McDPer05,
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  title   = {Identifying gene and protein mentions in text using
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  volume  = {6},
  number  = {Suppl 1},
  month   = {May}
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@article{McEMacChe98,
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@article{McERodRumWel77,
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@techreport{McKay84,
  author      = {B. D. McKay},
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@inproceedings{McKBol00,
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@book{McLKri97,
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@inproceedings{McmGorBlu03,
  author    = {H. Brendan Mcmahan and Georey J Gordon and Avrim Blum},
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@incollection{McNNad90,
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  title     = {Exploration of Multi-State Environments: {L}ocal Measures
               and Back-Propagation of Uncertainty},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {35},
  pages     = {117--154},
  number    = {2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{MeuHauKimPesetal98,
  author    = {Nicolas Meuleau and Milos Hauskrecht and Kee-Eung Kim and
               Leonid Peshkin and Leslie Kaelbling and Thomas Dean and
               Craig Boutilier},
  title     = {Solving Very Large Weakly Coupled Markov Decision
               Processes},
  booktitle = {{AAAI}/{IAAI}},
  pages     = {165--172},
  year      = {1998},
  url       = {citeseer.ist.psu.edu/meuleau98solving.html}
}

@article{Meyer72,
  author  = {A. Meyer},
  title   = {Program size in restricted programming languages},
  journal = {Inform. Control},
  year    = {1972},
  volume  = {21},
  pages   = {382--394}
}

@article{Meyer74,
  author  = {Gerard Meyer},
  title   = {Accelerated {F}rank--{W}olfe Algorithms},
  journal = {SIAM Journal on Control},
  year    = {1974},
  volume  = {12},
  pages   = {655--655}
}

@book{Meyer92,
  title     = {Wavelets and Operators},
  publisher = {Cambridge University Press},
  year      = {1992},
  author    = {Y.~Meyer},
  note      = {First published in French by Hermann, /'eds., Paris,
               1990}
}

@inproceedings{Meyer97,
  author    = {L{\'e}a Meyer},
  title     = {Monotonic and dual-monotonic probabilistic language
               learning of indexed families with high probability},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  volume    = {1208},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {66--78},
  publisher = {Springer-Verlag}
}

@article{Meyer97b,
  author  = {L{\'e}a Meyer},
  title   = {Probabilistic language learning under monotonicity
             constraint},
  journal = {Theoret. Comput. Sci.},
  year    = {1997},
  volume  = {185},
  pages   = {81--128},
  number  = {1},
  note    = {Special issue for ALT '95}
}

@inproceedings{Meyer98,
  author    = {L{\'e}a Meyer},
  title     = {Aspects of complexity of conservative probabilistic
               learning},
  booktitle = {Proc. 11th Annu. Conf. on Comput. Learning Theory},
  year      = {1998},
  pages     = {72--78},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{Meyer98b,
  author    = {L. Meyer},
  title     = {Comparing the Power of Probabilistic Learning and Oracle
               Identification under Monotonicity Constraints},
  booktitle = alt,
  year      = {1998},
  volume    = {1501},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {306--320},
  publisher = {Springer-Verlag}
}

@article{MeyLeiHor03,
  author  = {D. Meyer and F. Leisch and K. Hornik},
  title   = {The support vector machine under test},
  journal = {Neurocomputing},
  year    = {2003},
  volume  = {55},
  pages   = {169--186},
  number  = {1--2}
}

@article{MezBieRab82,
  author  = {A.~Mezzanotte and I.~Biederman and V.~Rabinowitz},
  title   = {Scene Perception: Detecting and Judging Objects Undergoing
             Relational},
  journal = {Cognitive Psychology},
  year    = {1982},
  volume  = {14},
  pages   = {143--177}
}

@book{MezParVir87,
  title     = {Spin Glass Theory and Beyond},
  publisher = {World Scientific},
  year      = {1987},
  author    = {M.~M{\'e}zard and G.~Parisi and M.~G.~Virasoro},
  address   = {Singapore}
}

@article{MhaMic92,
  author  = {H.~N.~Mhaskar and C.~A.~Micchelli},
  title   = {Approximation by superposition of a sigmoidal function},
  journal = {Advances in Applied Mathematics},
  year    = {1992},
  volume  = {13},
  pages   = {350--373}
}

@inproceedings{MhaMic93,
  author    = {H.~N.~Mhaskar and C.~A.~Micchelli},
  title     = {How to choose an activation function},
  booktitle = {Advances in Neural Information Processing Systems 5},
  year      = {1993},
  editor    = {S.~J.~Hanson and J.~D.~Cowan and C.~L.~Giles},
  pages     = {319--326},
  publisher = {Morgan Kaufmann Publishers}
}

@article{MhaMic94,
  author  = {H.~N.~Mhaskar and C.~A.~Micchelli},
  title   = {Dimension independent bounds on the degree of
             approximation by neural networks},
  journal = {{IBM Journal of Research and Development}},
  year    = {1994},
  volume  = {38},
  pages   = {277--284}
}

@article{MhaMic95,
  author  = {H.~Mhaskar and C.~Micchelli},
  title   = {Degree of approximation by neural and translation networks
             with a single hidden layer},
  journal = {Advances in applied mathematics},
  year    = {1995},
  volume  = {16},
  pages   = {151--183}
}

@inproceedings{Mhammedi_ConstantRegret_2018,
  title     = {Constant regret, generalized mixability, and mirror
               descent},
  booktitle = {Advances in neural information processing systems 31},
  publisher = {Curran Associates, Inc.},
  author    = {Mhammedi, Zakaria and Williamson, Robert C.},
  editor    = {Bengio, S. and Wallach, H. and Larochelle, H. and Grauman,
               K. and Cesa-Bianchi, N. and Garnett, R.},
  year      = {2018},
  keywords  = {⛔ No DOI found},
  pages     = {7419--7428},
  file      = {Mhammedi, Williamson - Constant regret, generalized
               mixability, and mirror descent
               (2018).pdf:/Users/zcranko/Documents/Library/Conference
               Paper/Mhammedi, Williamson - Constant regret, generalized
               mixability, and mirror descent (2018).pdf:application/pdf}
}

@article{Mhaskar93,
  author  = {H.~N.~Mhaskar},
  title   = {Approximation properties of a multilayered feedforward
             artificial neural network},
  journal = {Advances in Computational Mathematics},
  year    = {1993},
  volume  = {1},
  pages   = {61--80}
}

@inproceedings{Mhaskar93b,
  author       = {H.~N.~Mhaskar},
  title        = {Neural networks for localized approximation of real
                  functions},
  booktitle    = {Neural networks for signal processing III, Proceedings of
                  the 1993 {IEEE-SP} Workshop},
  year         = {1993a},
  editor       = {C.~A.~Kamm et al.},
  pages        = {190--196},
  address      = {New York},
  organization = {IEEE Signal Processing Society}
}

@book{MicCarMit83,
  title     = {Machine Learning: an Artificial Intelligence Approach},
  publisher = {Morgan Kaufmann Publishers},
  year      = {1983},
  editor    = {R.~S.~Michalski and J.~G.~Carbonell and T.~M.~Mitchell},
  address   = {Los Altos, CA}
}

@book{MicCarMit86,
  title     = {Machine Learning: {A}n Artificial Intelligence Approach},
  publisher = {Morgan Kaufmann},
  year      = {1986},
  editor    = {Ryszard S. Michalski and Jaime G. Carbonell and Tom M.
               Mitchell},
  volume    = {II},
  address   = {Los Altos, California},
  isbn      = {0-934613-00-1}
}

@article{Micchelli86,
  author  = {C.~A.~Micchelli},
  title   = {Algebraic aspects of interpolation},
  journal = {Symposia in Applied Mathematics},
  year    = {1986},
  volume  = {36},
  pages   = {81--102}
}

@article{Micchelli86b,
  author  = {C.~A.~Micchelli},
  title   = {Interpolation of scattered data: distance matrices and
             conditionally positive definite functions},
  journal = {Constructive Approximation},
  year    = {1986},
  volume  = {2},
  pages   = {11--22}
}

@article{Micchellietal13,
  title   = {Regularizers for Structured Sparsity},
  author  = {Charles A. Micchelli and Jean M. Morales and Massimiliano
             Pontil},
  journal = {Advances in Computational Mathematics},
  volume  = {38},
  number  = {3},
  pages   = {455--489},
  year    = {2013}
}

@article{MicDemMal74,
  author  = {B.~F.~Michell and V.~F.~Demyanov and V.~N.~Malozemov},
  title   = {Finding the point of polyhedron closest to the origin},
  journal = {SIAM J.~Control},
  year    = {1974},
  volume  = {12},
  pages   = {19--26}
}

@article{Michalski00,
  author    = {Ryszard S. Michalski},
  title     = {{LEARNABLE EVOLUTION MODEL}: Evolutionary Processes Guided
               by Machine Learning},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {38},
  pages     = {9--40},
  number    = {1/2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Michalski80,
  author  = {R. Michalski},
  title   = {Pattern recognition as rule guided inductive inference},
  journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
  year    = {1980},
  volume  = {PAMI-2},
  pages   = {349--361}
}

@incollection{Michalski86,
  author    = {R. S. Michalski},
  title     = {A Theory and Methodology of Inductive Inference},
  booktitle = {Machine Learning Vol2},
  publisher = {Morgan Kaufman},
  year      = {1986},
  pages     = {215--244}
}

@inbook{Michalski86b,
  pages     = {3--26},
  title     = {Understanding the Nature of Learning: Issues and Research
               Directions},
  publisher = {Morgan Kaufmann},
  year      = {1986},
  author    = {R. M. Michalski},
  address   = {Los Altos, CA},
  booktitle = {Machine Learning: An Artificial Intelligence Approach,
               Vol. II}
}

@article{Michalski93,
  author    = {Ryszard S. Michalski},
  title     = {Inferential Theory of Learning as a Conceptual Basis for
               Multistrategy Learning},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {11},
  pages     = {111--151},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Michie83,
  author    = {D. Michie},
  title     = {Inductive Rule Generation in the Context of the Fifth
               Generation},
  booktitle = {Proc. of the First International Workshop on Machine
               Learning},
  year      = {1983},
  pages     = {66--70},
  address   = {Monticello, Illinois},
  month     = jun
}

@article{Miclet80,
  author  = {L. Miclet},
  title   = {Regular inference with a tail-clustering method},
  journal = {IEEE Trans. on Systems, Man, and Cybernetics},
  year    = {1980},
  volume  = {SMC-10},
  pages   = {737--743}
}

@article{MicMorPon11,
  author  = {Charles A. Micchelli and Jean M. Morales and Massimiliano
             Pontil},
  title   = {Regularizers for Structured Sparsity},
  journal = {Advances in Computational Mathematics},
  year    = {2011}
}

@article{MicMorPon13,
  author  = {Charles A. Micchelli and Jean M. Morales and Massimiliano
             Pontil},
  title   = {Regularizers for structured sparsity},
  journal = {Advances in Computational Mathematics},
  year    = {2013},
  volume  = {38},
  number  = {3},
  pages   = {455--489}
}

@techreport{MicPon03,
  author      = {C.A. Micchelli and M. Pontil},
  title       = {On Learning Vector-Valued Functions},
  institution = {Department of Computer Science, University College
                 London},
  year        = {2003},
  type        = {Research Note},
  key         = {RN/03/08}
}

@incollection{MicRiv76,
  author    = {C.~A.~Micchelli and T.~J.~Rivlin},
  title     = {A survey of optimal recovery},
  booktitle = {Optimal Estimation in Approximation Theory},
  publisher = {Plenum Press},
  year      = {1976},
  editor    = {C.~A.~Micchelli and T.~J.~Rivlin},
  pages     = {1--54},
  address   = {New York}
}

@book{MicTec93,
  title     = {Machine Learning: {A} Multistrategy Approach},
  publisher = {Morgan Kaufmann},
  year      = {1993},
  editor    = {Ryszard Michalski and George Tecuci},
  volume    = {IV},
  address   = {Los Altos, California},
  isbn      = {1-55860-251-8}
}

@incollection{MicWah81,
  author    = {C.~A.~Micchelli and G.~Wahba},
  title     = {Design problems for optimal surface interpolation},
  booktitle = {Approximation theory and applications},
  publisher = {Academic Press},
  year      = {1981},
  editor    = {Z.~Ziegler},
  pages     = {329--348},
  address   = {New York}
}

@article{MicXuZha06,
  title   = {Universal Kernels},
  author  = {Charles A. Micchelli and Yuesheng Xu and Haizhang Zhang},
  journal = jmlr,
  year    = {2006},
  volume  = {7},
  pages   = {2651--2667}
}

@inbook{Miesbach95,
  chapter   = {2},
  pages     = {21--42},
  title     = {High accuracy path tracking by neural linearization
               techniques},
  publisher = {{MIT} Press},
  year      = {1995},
  author    = {Stefan Miesbach},
  volume    = {III: Selecting Good Models},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@inproceedings{MihPed03,
  author    = {Mihalcea, Rada and Pedersen, Ted},
  title     = {An Evaluation Exercise for Word Alignment},
  booktitle = {the HLT-NAACL 2003 Workshop on Building and Using Parallel
               Texts: Data Driven Machine Translation and Beyond - Volume
               3},
  year      = {2003},
  pages     = {1--10},
  numpages  = {10}
}

@mastersthesis{Mika98,
  author = {S.~Mika},
  title  = {Nichtlineare {S}ignalverarbeitung in {F}eature-{R\"aumen}},
  school = {Technische {Universit\"at} Berlin},
  year   = {1998},
  type   = {Diplomarbeit}
}

@book{Mikhlin65,
  title     = {The problem of the minimum of a quadratic functional},
  publisher = {Holden-Day},
  year      = {1965},
  author    = {S.~G.~Mikhlin},
  address   = {San Francisco, CA}
}

@inproceedings{MikRatMul01,
  author    = {S.~Mika and G.~R\"atsch and K.-R.~M\"uller},
  title     = {A Mathematical Programming Approach to the Kernel Fisher
               Algorithm},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K.~Leen and T.~G.~Dietterich and V.~Tresp},
  pages     = {591--597},
  publisher = {{MIT} Press}
}

@inproceedings{MikRatWesSchetal00,
  author    = {S.~Mika and G.~R{\"a}tsch and J.~Weston and B.
               Sch{\"o}lkopf and A. J. Smola and K.-R.~M{\"u}ller},
  title     = {Invariant Feature Extraction and Classification in Kernel
               Spaces},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A.~Solla and T.~K.~Leen and K.-R.~M{\"u}ller},
  pages     = {526--532},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{MikRatWesSchetal03,
  author  = {S.~Mika and G.~{R\"atsch} and J.~Weston and B.
             {Sch\"olkopf} and A. J. Smola and K.-R.~{M\"uller}},
  title   = {Learning discriminative and invariant nonlinear features},
  journal = {IEEE Transactions on Pattern Analysis and Machine
             Intelligence},
  year    = {2003},
  volume  = {25},
  pages   = {623--628},
  number  = {5}
}

@inproceedings{MikRatWesSchetal99,
  author    = {S.~Mika and G.~R{\"a}tsch and J.~Weston and
               B.~Sch{\"o}lkopf and K.-R.~M{\"u}ller},
  title     = {Fisher Discriminant Analysis with Kernels},
  booktitle = {Neural Networks for Signal Processing IX},
  year      = {1999},
  editor    = {Y.-H.~Hu and J.~Larsen and E.~Wilson and S.~Douglas},
  pages     = {41--48},
  publisher = {IEEE}
}

@inproceedings{MikSchSmoMuletal99,
  author    = {S.~Mika and B.~{Sch\"olkopf} and A. J. Smola and
               K.-R.~{M\"uller} and Matthias Scholz and G.~{R\"atsch}},
  title     = {Kernel {PCA} and De-noising in feature spaces},
  booktitle = {Advances in Neural Information Processing Systems 11},
  year      = {1999},
  editor    = {M.~S. Kearns and S.~A. Solla and D.~A. Cohn},
  pages     = {536--542},
  publisher = {{MIT} Press}
}

@inproceedings{MikSmoSch01,
  author    = {S.~Mika and A. J. Smola and B.~{Sch\"olkopf}},
  title     = {An improved training algorithm for kernel {Fisher}
               discriminants},
  booktitle = {Artificial Intelligence and Statistics},
  year      = {2001},
  editor    = {T.~Jaakkola and T.~Richardson},
  pages     = {98--104},
  address   = {San Francisco, CA},
  publisher = {Morgan Kaufmann Publishers},
  note      = {Also: Microsoft Research TR-2000-77}
}

@phdthesis{Milano02,
  author  = {Michele Milano},
  title   = {Machine Learning Techniques for Flow Modeling and
             Control},
  school  = {Eid\-ge\-n\"os\-si\-sche Tech\-ni\-sche Hoch\-schu\-le {(ETH)}},
  year    = {2002},
  address = {Z\"urich, Switzerland}
}

@article{MilGlaKra87,
  author  = {W. T. Miller and F. H. Glanz and L. G. Kraft},
  title   = {Application of a General Learning Algorithm to the Control
             of Robotic Manipulators},
  journal = {International Journal of Robotics Research},
  year    = {1987},
  volume  = {6},
  pages   = {84--98},
  number  = {2},
  comment = {experimental study of CMAC variation}
}

@article{MilGra08,
  author  = {J. J. Milner and Angus J. Grandison},
  title   = {A Fast, Streaming {SIMD} Extensions 2, Logistic Squashing
             Function},
  pages   = {2967--2972},
  journal = nc,
  volume  = 20,
  number  = 12,
  year    = 2008
}

@inproceedings{MilGriJor09,
  author    = {K.T. Miller and T.L. Griffiths and M.I. Jordan},
  title     = {Latent Feature Models for Link Prediction},
  pages     = {2 pages},
  year      = 2009,
  booktitle = {Snowbird}
}

@inproceedings{MilHauJur89,
  author    = {A. Milosavljevi{'c} and D. Haussler and J. Jurka},
  title     = {Informed parsimonious inference of prototypical genetic
               sequences},
  booktitle = {Proc. 2nd Annu. Workshop on Comput. Learning Theory},
  year      = {1989},
  pages     = {102--117},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{MilJur93,
  author    = {Aleksandar Milosavljevi{\'{c}} and Jerzy Jurka},
  title     = {Discovery by Minimal Length Encoding: {A} Case Study in
               Molecular Evolution},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {12},
  pages     = {69--87},
  publisher = {Kluwer Academic Publishers, Boston}
}

@techreport{Miller04,
  author      = {Eric G. Learned-Miller},
  title       = {Hyperspacing and the Estimation of Information Theoretic
                 Quantities},
  institution = {Department of Computer Science, University of
                 Massachusetts},
  year        = {2004},
  number      = {04-104},
  note        = {http://www.cs.umass.edu/~elm/papers/04-104.pdf}
}

@inproceedings{Miller_VariationalBoosting_2017,
  address   = {Sydney, Australia},
  series    = {Proceedings of machine learning research},
  title     = {Variational boosting: iteratively refining posterior
               approximations},
  volume    = {70},
  abstract  = {We propose a black-box variational inference method to
               approximate intractable distributions with an increasingly
               rich approximating class. Our method, variational boosting,
               iteratively refines an existing variational approximation
               by solving a sequence of optimization problems, allowing a
               trade-off between computation time and accuracy. We expand
               the variational approximating class by incorporating
               additional covariance structure and by introducing new
               components to form a mixture. We apply variational boosting
               to synthetic and real statistical models, and show that the
               resulting posterior inferences compare favorably to
               existing variational algorithms.},
  booktitle = {Proceedings of the 34th international conference on
               machine learning},
  publisher = {PMLR},
  author    = {Miller, Andrew C. and Foti, Nicholas J. and Adams, Ryan
               P.},
  editor    = {Precup, Doina and Teh, Yee Whye},
  month     = aug,
  year      = {2017},
  pages     = {2420--2429}
}

@inproceedings{Milleretal12,
  author    = {K. Miller and M. Kumar and B. Packer and D. Goodman and D.
               Koller},
  title     = {Maxmargin min-entropy models},
  booktitle = aistats,
  year      = 2012
}

@incollection{Milman_CharacterizingRadial_2017,
  address   = {Cham, Switzerland},
  title     = {Characterizing the {Radial} {Sum} for {Star} {Bodies}},
  isbn      = {978-3-319-45282-1},
  abstract  = {In this paper we prove two theorems characterizing the
               radial sum of star bodies. By doing so we demonstrate an
               interesting phenomenon: essentially the same conditions, on
               two different spaces, can uniquely characterize very
               different operations. In our first theorem we characterize
               the radial sum by its induced homothety, and our list of
               assumptions is identical to the assumptions of the
               corresponding theorem which characterizes the Minkowski sum
               for convex bodies. In our second theorem give a different
               characterization from a short list of natural properties,
               without assuming the homothety has any specific form. For
               this theorem one has to add an assumption to the
               corresponding theorem for convex bodies, as we demonstrate
               by a simple example.},
  booktitle = {Geometric {Aspects} of {Functional} {Analysis}: {Israel}
               {Seminar} ({GAFA}) 2014–2016},
  publisher = {Springer International Publishing},
  author    = {Milman, Vitali and Rotem, Liran},
  editor    = {Klartag, Bo'az and Milman, Emanuel},
  year      = {2017},
  doi       = {10.1007/978-3-319-45282-1_20},
  pages     = {319--329},
  file      = {Milman, Rotem - Characterizing the Radial Sum for Star
               Bodies (2017).pdf:/Users/zcranko/Documents/Library/Book
               Section/Milman, Rotem - Characterizing the Radial Sum for
               Star Bodies (2017).pdf:application/pdf}
}

@article{Milosavljevic95,
  author    = {Aleksandar Milosavljevi{\`{c}}},
  title     = {Discovering Dependencies via Algorithmic Mutual
               Information: {A} Case Study in {DNA} Sequence Comparisons},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {21},
  pages     = {35--50},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{MilRei85,
  author    = {G. Miller and J. Reif},
  title     = {Parallel tree contraction and its applications},
  booktitle = {In the 26th IEEE Annual Symposium on Foundations of
               Computer Science},
  year      = {1985},
  pages     = {478--489}
}

@inproceedings{MilSch98,
  author    = {Matthias Miltrup and Georg Schnitger},
  title     = {Neural networks and efficient associative memory},
  booktitle = {Proc. 11th Annu. Conf. on Comput. Learning Theory},
  year      = {1998},
  pages     = {235--246},
  publisher = {ACM Press, New York, NY}
}

@article{MilSheItzKasetal02,
  author  = {R. Milo and S. Shen-Orr and S. Itzkovitz and N. Kashtan
             and D. Chklovskii and U. Alon},
  title   = {Network motifs: simple building blocks of complex
             networks},
  journal = {Science},
  volume  = {298},
  number  = {5594},
  pages   = {824-827},
  month   = {Oct},
  year    = {2002}
}

@article{MilTor92,
  author    = {Jos{\'{e}} Del R. Mill{\'{a}}n and Carme Torras},
  title     = {A Reinforcement Connectionist Approach to Robot Path
               Finding in Non-Maze-Like Environments},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {8},
  pages     = {363--395},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{MimLiMcC07,
  title     = {Mixtures of hierarchical topics with Pachinko allocation},
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@inproceedings{monfort2015softstar,
  title     = {Softstar: Heuristic-Guided Probabilistic Inference},
  author    = {Monfort, Mathew and Lake, Brenden M and Ziebart, Brian D
               and Lucey, Patrick and Tenenbaum, Joshua B},
  booktitle = {Advances in Neural Information Processing Systems (NIPS)},
  year      = {2015}
}

@article{monfort2017deep,
  title  = {A Deep Learning Approach to Identifying Shock Locations in
            Turbulent Combustion Tensor Fields},
  author = {Monfort, Mathew and Luciani, Timothy and Komperda,
            Jonathan and Ziebart, Brian and Mashayek, Farzad and Marai,
            G Elisabeta},
  year   = {2017}
}

@inproceedings{MonFraIen93,
  author    = {F.~Mondada and E.~Franzi and P.~Ienne},
  title     = {Mobile robot miniaturisation: {A} tool for investigation
               in control algorithms},
  booktitle = {the Third International Symposium on Experimental
               Robotics, Kyoto, Japan},
  year      = {1993}
}

@article{MonSavKutFeuetal05,
  author  = {S. Monti and K. J. Savage and J. L. Kutok and F. Feuerhake
             and P. Kurtin and M. Mihm and B. Wu and L. Pasqualucci and
             D. Neuberg and R. C. Aguiar and P. Dal Cin and C. Ladd and
             G. S. Pinkus and G. Salles and N. L. Harris and R.
             Dalla-Favera and T. M. Habermann and J. C. Aster and T. R.
             Golub and M. A. Shipp},
  title   = {Molecular profiling of diffuse large B-cell lymphoma
             identifies robust subtypes including one characterized by
             host inflammatory response},
  journal = {Blood},
  volume  = {105},
  number  = {5},
  pages   = {1851-1861},
  month   = {Mar},
  year    = {2005}
}

@article{MonSenPan07,
  author  = {M. A. Montemurro and R. Senatore and S. Panzeri},
  title   = {Tight data-robust bounds to mutual information combining
             shuffling and model selection techniques},
  journal = {Neural Computation},
  year    = {2007},
  volume  = {(in press)}
}

@article{Montana90,
  author    = {David J. Montana},
  title     = {Empirical Learning Using Rule Threshold Optimization for
               Detection of Events in Synthetic Images},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  pages     = {427--450},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{MontavonEtal2013,
  title   = {Machine Learning of Molecular Electronic Properties in
             Chemical Compound Space},
  author  = {Montavon, Gr{\'e}goire and Rupp, Matthias and Gobre,
             Vivekanand and Vazquez-Mayagoitia, Alvaro and Hansen, Katja
             and Tkatchenko, Alexandre and M{\"u}ller, Klaus-Robert and
             von Lilienfeld, O Anatole},
  journal = {arXiv preprint arXiv:1305.7074},
  year    = {2013}
}

@article{MooAtk93,
  author    = {Andrew W. Moore and Christopher G. Atkeson},
  title     = {Prioritized Sweeping: Reinforcement Learning With Less
               Data and Less Time},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {13},
  pages     = {103--130},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{MooAtk95,
  author    = {Andrew W. Moore and Christopher G. Atkeson},
  title     = {The Parti-game Algorithm for Variable Resolution
               Reinforcement Learning in Multidimensional State-spaces},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {21},
  pages     = {199--233},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{MooDar88,
  author    = {J.~Moody and C.~Darken},
  title     = {Learning with Localized Receptive Fields},
  booktitle = {the 1988 Connectionist Models Summer School},
  year      = {1988},
  editor    = {G.~Hinton and T.~Sejnowski and D.~Touretzsky},
  pages     = {133--143},
  address   = {Palo Alto}
}

@article{MooDar89,
  author  = {J.~Moody and C.~Darken},
  title   = {Fast learning in networks of locally-tuned processing
             units},
  journal = {Neural Computation},
  year    = {1989},
  volume  = {1},
  pages   = {281--294},
  number  = {2},
  key     = {Moody}
}

@techreport{MooDar89b,
  author      = {J.~Moody and C.~Darken},
  title       = {Fast learning in networks of locally-tuned processing
                 units},
  institution = {{Yale University, Department of Computer Science}},
  year        = {1989},
  type        = {Research Report},
  number      = {{YALEU/DCS/RR-654}}
}

@inproceedings{Moody89,
  author    = {J.~Moody},
  title     = {Fast learning in multi resolution hierarchies},
  booktitle = {Advances in Neural Information Processing Systems 1},
  year      = {1989},
  editor    = {D.~S.~Touretzky},
  pages     = {29--39},
  address   = {Carnegie Mellon University},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{Moody91,
  author    = {J.~Moody},
  title     = {Note on Generalization, Regularization, and Architecture
               Selection in Nonlinear Learning Systems},
  booktitle = {{the First IEEE-SP Workshop on Neural Networks for Signal
               Processing}},
  year      = {1991a},
  pages     = {1--10},
  address   = {Los Alamitos, CA},
  publisher = {IEEE Computer Society Press}
}

@inproceedings{Moody92,
  author    = {J.~Moody},
  title     = {The {\em effective} number of parameters: an analysis of
               generalization and regularization in nonlinear learning
               systems},
  booktitle = {Advances in Neural Information Processing Systems 4},
  year      = {1992},
  editor    = {J.~E.~Moody and S.~J.~Hanson and R.~Lippmann},
  pages     = {847--854},
  address   = {Palo Alto, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@inbook{MooHilJoh95,
  chapter   = {19},
  pages     = {361--379},
  title     = {An empirical investigation of brute force to choose
               features, smoothers and function approximators},
  publisher = {{MIT} Press},
  year      = {1995},
  author    = {Andrew W. Moore and Daniel J. Hill and M. P. Johnson},
  volume    = {III: Selecting Good Models},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@article{MooKap07,
  title   = {Sufficient Conditions for Convergence of the
             {S}um-{P}roduct Algorithm},
  author  = {J.M. Mooij and H.J. Kappen},
  journal = {ITIT},
  year    = {2007},
  pages   = {4422-4437}
}

@inproceedings{MooLee94,
  author    = {Andrew W. Moore and Mary S. Lee},
  title     = {Efficient algorithms for minimizing cross validation
               error},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1994},
  pages     = {190--198},
  publisher = {Morgan Kaufmann}
}

@article{Mooney93,
  author    = {Raymond J. Mooney},
  title     = {Induction Over the Unexplained: Using Overly-General
               Domain Theories to Aid Concept Learning},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {10},
  pages     = {79--110},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Mooney95,
  author    = {Raymond J. Mooney},
  title     = {Encouraging Experimental Results on Learning {CNF}},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {19},
  pages     = {79--92},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inbook{Mooney95b,
  chapter   = {3},
  pages     = {43--53},
  title     = {A preliminary {PAC} analysis of theory revision},
  publisher = {{MIT} Press},
  year      = {1995},
  author    = {Raymond J. Mooney},
  volume    = {III: Selecting Good Models},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@inproceedings{MooPog88,
  author    = {B.~Moore and T.~Poggio},
  title     = {Representations properties of multilayer feedforward
               networks},
  booktitle = {Abstracts of the first annual {INNS} meeting},
  year      = {1988},
  pages     = {502},
  address   = {New York},
  publisher = {Pergamon Press}
}

@article{Moore16,
  author  = {E.~H.~Moore},
  title   = {On properly positive {Hermitian} matrices},
  journal = {Bull.~Amer.~Math.~Soc.},
  year    = {1916},
  volume  = {23},
  pages   = {66--67},
  number  = {59}
}

@inproceedings{Moore96,
  author    = {Andrew W. Moore},
  title     = {Reinforcement learning in factories: the auton project
               (abstract)},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {556},
  publisher = {Morgan Kaufmann}
}

@inproceedings{MoosaviDezfooli_UniversalAdversarial_2017,
  address   = {Honolulu, HI, USA,},
  title     = {Universal adversarial perturbations},
  isbn      = {978-1-5386-0457-1},
  doi       = {10.1109/Cvpr.2017.17},
  abstract  = {Given a state-of-the-art deep neural network classifier,
               we show the existence of a universal (image-agnostic) and
               very small perturbation vector that causes natural images
               to be misclassified with high probability. We propose a
               systematic algorithm for computing universal perturbations,
               and show that state-of-the-art deep neural networks are
               highly vulnerable to such perturbations, albeit being
               quasi-imperceptible to the human eye. We further
               empirically analyze these universal perturbations and show,
               in particular, that they generalize very well across neural
               networks. The surprising existence of universal
               perturbations reveals important geometric correlations
               among the high-dimensional decision boundary of
               classifiers. It further outlines potential security
               breaches with the existence of single directions in the
               input space that adversaries can possibly exploit to break
               a classifier on most natural images.},
  booktitle = {Proceedings of 2017 {IEEE} {Conference} on {Computer}
               {Vision} and {Pattern} {Recognition} ({CVPR})},
  publisher = {IEEE},
  author    = {Moosavi Dezfooli, Seyed Mohsen and Fawzi, Alhussein and
               Fawzi, Omar and Frossard, Pascal},
  year      = {2017},
  note      = {tex.address: New York tex.publisher: Ieee},
  pages     = {9}
}

@inproceedings{MooSchBoyLee98,
  author    = {Andrew W. Moore and Jeff G. Schneider and Justin A. Boyan
               and Mary S. Lee},
  title     = {{Q2}: memory-based active learning for optimizing noisy
               continuous functions},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {386--394},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{MooSchDen97,
  author    = {Andrew W. Moore and Jeff Schneider and Kan Deng},
  title     = {Efficient locally weighted polynomial regression
               predictions},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {236--244},
  publisher = {Morgan Kaufmann}
}

@inproceedings{MooYar91,
  author    = {J.~Moody and N.~Yarvin},
  title     = {Networks with Learned Unit Response Functions},
  booktitle = {Advances in Neural Information Processing Systems 4},
  year      = {1991},
  editor    = {J.~E.~Moody and S.~J.~Hanson and R.~Lippmann},
  pages     = {1048--1055},
  address   = {Palo Alto, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@article{Morales97,
  author    = {Eduardo F. Morales},
  title     = {{PAL}: {A} Pattern and dash;Based First and dash;Order
               Inductive System},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {26},
  pages     = {227--252},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Moravciketal17,
  author  = {Matej Moravcik and Martin Schmid and Neil Burch and Viliam
             Lisy and Dustin Morrill and Nolan Bard and Trevor Davis and
             Kevin Waugh and Michael Johanson and Michael Bowling},
  title   = {DeepStack: Expert-Level Artificial Intelligence in
             No-Limit Poker},
  journal = {Technical report: https://arxiv.org/abs/1701.01724},
  year    = 2017
}

@article{MorBerBun94,
  author    = {Katharina Morik and Francesco Bergadano and Wray Buntine},
  title     = {Guest Editorial},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {14},
  pages     = {137--138},
  note      = {This issue was a special issue from the Workshop on
               Evaluating and Changing representation from 1991},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{MorBroJoa99,
  author    = {Katharina Morik and Peter Brockhausen and Thorsten
               Joachims},
  title     = {Combining statistical learning with a knowledge-based
               approach - a case study in intensive care monitoring},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {268--277},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{MorDoy00,
  author    = {Jun Morimoto and K. Doya},
  title     = {Acquisition of Stand-up Behavior by a Real Robot using
               Hierarchical Reinforcement Learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {623--630},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@book{Morduchovich_VariationalAnalysis_2018,
  title    = {Variational analysis and applications},
  isbn     = {978-3-319-92775-6},
  language = {English},
  author   = {Morduchovich, Boris S},
  year     = {2018},
  note     = {OCLC: 1050939445},
  file     = {Morduchovich - Variational analysis and applications
              (2018).pdf:/Users/zcranko/Documents/Library/Book/Morduchovich
              - Variational analysis and applications
              (2018).pdf:application/pdf}
}

@article{Mordukhovich_NonsmoothSequential_1996,
  title    = {Nonsmooth sequential analysis in {Asplund} spaces},
  volume   = {348},
  doi      = {10.1090/s0002-9947-96-01543-7},
  abstract = {We develop a generalized differentiation theory for
              nonsmooth functions and sets with nonsmooth boundaries
              defined in Asplund spaces. This broad subclass of Banach
              spaces provides a convenient framework for many important
              applications to optimization, sensitivity, variational
              inequalities, etc. Our basic normal and subdifferential
              constructions are related to sequential weak-star limits of
              Frechet normals and subdifferentials. Using a variational
              approach, we establish a rich calculus for these nonconvex
              limiting objects which turn out to be minimal among other
              set-valued differential constructions with natural
              properties. The results obtained provide new developments
              in infinite dimensional nonsmooth analysis and have useful
              applications to optimization and the geometry of Banach
              spaces.},
  language = {en},
  number   = {4},
  journal  = {Transactions of the American Mathematical Society},
  author   = {Mordukhovich, Boris S and Shao, Yongheng},
  month    = apr,
  year     = {1996},
  pages    = {1235--1280},
  file     = {Mordukhovich, Shao - Nonsmooth Sequential Analysis in
              Asplund Spaces
              (1996).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Mordukhovich, Shao - Nonsmooth Sequential Analysis
              in Asplund Spaces (1996).pdf:application/pdf}
}

@article{MorDun95,
  author  = {A.~de Moraes and I.~R.~Dunsmore},
  title   = {Predictive Comparisons in Ordinal Models},
  journal = {Communications in Statistics\,---\,Theory and Methods},
  year    = {1995},
  volume  = {24},
  pages   = {2145--2164},
  number  = {8}
}

@article{Moreau65,
  author  = {J. J. Moreau},
  title   = {Proximite et dualite dans un espace hilbertien},
  journal = {Bull. Soc. Math. Fr.},
  year    = {1965},
  volume  = {93},
  pages   = {273--299}
}

@article{Moreau_InfconvolutionSousadditivite_1970,
  title    = {Inf-convolution, sous-additivité, convexité des
              fonctions numériques},
  volume   = {49},
  issn     = {0021-7824},
  journal  = {Journal de Mathématiques Pures et Appliquées},
  author   = {Moreau, Jean-Jacques},
  year     = {1970},
  note     = {tex.fjournal: Journal de Mathématiques Pures et
              Appliquées. Neuvième Série tex.mrclass: 46.90
              tex.mrnumber: 288602 tex.mrreviewer: M. Z. Nashed},
  keywords = {⛔ No DOI found},
  pages    = {109--154}
}

@article{Moreau_SemicontinuiteSousgradient_1965,
  title    = {Semi-continuité du sous-gradient d'une fonctionnelle},
  volume   = {260},
  issn     = {0001-4036},
  journal  = {Comptes Rendus Hebdomadaires des Séances de l'Académie
              des Sciences},
  author   = {Moreau, Jean-Jacques},
  year     = {1965},
  note     = {tex.fjournal: Comptes Rendus Hebdomadaires des Séances de
              l'Académie des Sciences tex.mrclass: 46.90 (57.50)
              tex.mrnumber: 173936 tex.mrreviewer: P. C. Deliyannis},
  keywords = {⛔ No DOI found},
  pages    = {1067--1070}
}

@article{MorGarRawOKe82,
  author  = {R.~G.~M.~Morris and P.~Garrud and J.~N.~P.~Rawlins and
             J.~O'Keefe},
  title   = {Place Navigation impaired in Rats with hippocampal
             Lesions},
  journal = {Nature},
  year    = {1982},
  volume  = {297},
  pages   = {681--683}
}

@book{morgenstern1953theory,
  title     = {Theory of games and economic behavior},
  author    = {Morgenstern, Oskar and Von Neumann, John},
  year      = {1953},
  publisher = {Princeton university press}
}

@inproceedings{MorHarOku99,
  author    = {Nobuhiro Morita and Makoto Haraguchi and Yoshiaki Okubo},
  title     = {A Method of Similarity-Driven Knowledge Revision for Type
               Specification},
  booktitle = alt,
  year      = {1999},
  volume    = {1720},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {194--205},
  publisher = {Springer-Verlag}
}

@inproceedings{Morik00,
  author    = {Katharina Morik},
  title     = {The Representation Race\,---\,Preprocessing for Handling
               Time Phenomena},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {4--19},
  publisher = {Springer, Berlin}
}

@article{Morik93,
  author    = {Katharina Morik},
  title     = {Balanced Cooperative Modeling},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {11},
  pages     = {217--235},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Morik99,
  author    = {Katharina Morik},
  title     = {Tailoring Representations to Different Requirements},
  booktitle = alt,
  year      = {1999},
  volume    = {1720},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {1--12},
  publisher = {Springer-Verlag}
}

@article{MorIva82,
  author  = {P.~Morasso and F.~A.~Mussa Ivaldi},
  title   = {Trajectory Formation and Handwriting: {A} Computational
             Model},
  journal = {Biol.~Cyb.},
  year    = {1982},
  volume  = {45},
  pages   = {131--142}
}

@inproceedings{MorKirNevVis10,
  author    = {Sebastian Moreno and Sergey Kirshner and Jennifer Neville
               and S.~V.~N. Vishwanathan},
  title     = {Tied {K}ronecker Product Graph Models To Capture Variance
               in Network Populations},
  booktitle = {the 48$^\mathrm{th}$ Annual Allerton Conference on
               Communications, Control and Computing},
  year      = 2010
}

@inproceedings{MorMar00,
  author    = {Emmanuel Morin and Emmanuelle Martienne},
  title     = {Using a Symbolic Machine Learning Tool to Refine
               Lexico-syntactic Patterns},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {292--299},
  publisher = {Springer, Berlin}
}

@inproceedings{MorMii95,
  author    = {David E. Moriarty and Risto Miikkulainen},
  title     = {Efficient learning from delayed rewards through symbolic
               evolution},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {396--404},
  publisher = {Morgan Kaufmann}
}

@article{MorMii96,
  author    = {David E. Moriarty and Risto Miikkulainen},
  title     = {Efficient Reinforcement Learning through Symbiotic
               Evolution},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {22},
  pages     = {11--32},
  publisher = {Kluwer Academic Publishers, Boston}
}

@conference{MorNev09,
  title        = {{An investigation of the distributional characteristics of
                  generative graph models}},
  author       = {Moreno, S. and Neville, J.},
  booktitle    = {the The 1st Workshop on Information in Networks},
  year         = {2009},
  organization = {Citeseer}
}

@book{Morozov84,
  title     = {Methods for Solving Incorrectly Posed Problems},
  publisher = {Springer-Verlag},
  address   = {New York},
  year      = {1984},
  author    = {V.~A.~Morozov}
}

@inproceedings{MorQuaDar07,
  author    = {Louis-Philippe Morency and Ariadna Quattoni and Trevor
               Darrell},
  title     = {Latent-Dynamic Discriminative Models for Continuous
               Gesture Recognition},
  booktitle = cvpr,
  year      = {2007}
}

@inproceedings{MorSanTsu93,
  author    = {P.~Morasso and V.~Sanguineti and T.~Tsuji},
  title     = {Neural Architecture for Robot Planning},
  booktitle = {the International Conference on Artificial Neural
               Networks},
  year      = {1993},
  pages     = {256--261}
}

@inproceedings{MorSat93,
  author    = {T. Moriyama and M. Sato},
  title     = {Properties of Language Classes with Finite Elasticity},
  booktitle = alt-ws,
  year      = {1993},
  volume    = {744},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {187--196},
  publisher = {Springer-Verlag}
}

@article{Mortor89,
  author    = {Mor{\'e}, J. Jorge and Toraldo, Gerardo},
  journal   = {Numerische Mathematik},
  number    = {4},
  pages     = {377--400},
  publisher = {Springer},
  title     = {Algorithms for bound constrained quadratic programming
               problems},
  volume    = {55},
  year      = {1989}
}

@article{MorTor91,
  author  = {J.~J.~More and G.~Toraldo},
  title   = {On the Solution of Large Quadratic Programming Problems
             with Bound Constraints},
  journal = {SIAM Journal on Optimization},
  year    = {1991},
  volume  = {1},
  pages   = {93--113},
  number  = {1}
}

@inproceedings{Moschitti04,
  author    = {A. Moschitti},
  title     = {A Study on Convolution Kernels for Shallow Semantic
               Parsing.},
  booktitle = {ACL},
  year      = {2004},
  pages     = {335-342},
  url       = {http://acl.ldc.upenn.edu/acl2004/main/pdf/388_pdf_2-col.pdf}
}

@inproceedings{Moschitti06,
  author    = {A. Moschitti},
  title     = {Efficient Convolution Kernels for Dependency and
               Constituent Syntactic Trees},
  booktitle = {Machine Learning: ECML 2006, 17th European Conference on
               Machine Learning, Berlin, Germany},
  year      = {2006}
}

@article{moses2014using,
  title     = {Using big data for legal and law enforcement decisions:
               Testing the new tools},
  author    = {Moses, Lyria Bennett and Chan, Janet},
  journal   = {UNSWLJ},
  volume    = {37},
  pages     = {643},
  year      = {2014},
  publisher = {HeinOnline}
}

@article{MosRoc06,
  author  = {E. Mossel and S. Roch},
  title   = {Learning Nonsingular Phylogenies and Hidden Markov
             Models},
  journal = {Annals of Applied Probability},
  volume  = {16},
  number  = {2},
  pages   = {583--614},
  year    = 2006
}

@book{MosRou73,
  title     = {Sturdy Statistics},
  publisher = {Addison-Wesley},
  year      = {1973},
  author    = {F.~Mosteller and R.~Rourke},
  address   = {Reading, MA}
}

@techreport{MosUll91,
  author      = {Y.~Moses and S.~Ullman},
  title       = {Limitations of Non Model-Based Recognition Schemes},
  institution = {Artificial Intelligence Laboratory, Massachusetts
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@article{OshWei82b,
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@incollection{OshWei83,
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@unpublished{OshWei92,
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  title   = {Learning Graph Embedding with Adversarial Training
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@article{Paninski04b,
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@inproceedings{PanLee05,
  author    = {Bo Pang and Lillian Lee},
  title     = {Seeing Stars: Exploiting class relationships for
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  booktitle = {the ACL},
  year      = {2005}
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@inproceedings{PanLeeVai02,
  author    = {Bo Pang and Lillian Lee and Shivakumar Vaithyanathan},
  title     = {Thumbs up? Sentiment Classification using Machine Learning
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  year      = {2002}
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  author    = {P. Pantel and D. Lin},
  title     = {Discovering Word Senses from Text},
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  booktitle = {the Eighth {ACM} {SIGKDD} International Conference on
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  month     = jul,
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  volume  = {22},
  number  = {12},
  pages   = {1424 -- 1445}
}

@article{PanRot00b,
  author  = {M. Pantic and L.J.M. Rothkrantz},
  title   = {Automatic analysis of facial expressions: The state of the
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  year    = {2000},
  volume  = {22},
  number  = {12},
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@article{PanTre96b,
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@article{PanTsaKwoYan11,
  title   = {A Survey on Transfer Learning},
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  year    = {2011},
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  number  = {2},
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@article{PanYan10,
  author  = {Sinno Jialin Pan and Qiang Yang},
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  booktitle    = {2016 IEEE Symposium on Security and Privacy (SP)},
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@inproceedings{papernot2017practical,
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@article{PapFagCar18,
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  title     = {An {Introduction} to nonlinear analysis and fixed point
               theory},
  isbn      = {978-981-10-8865-0 978-981-10-8866-7},
  language  = {en},
  publisher = {Springer},
  author    = {Pathak, Hemant Kumar},
  year      = {2018},
  doi       = {10.1007/978-981-10-8866-7},
  file      = {Pathak - An Introduction to nonlinear analysis and fixed
               point theory
               (2018).pdf:/Users/zcranko/Documents/Library/Book/Pathak -
               An Introduction to nonlinear analysis and fixed point
               theory (2018).pdf:application/pdf}
}

@article{PatNatCus16,
  author  = {Patsorn Sangkloy and Nathan Burnell and Cusuh Ham and
             James Hays},
  title   = {The Sketchy Database: Learning to Retrieve Badly Drawn
             Bunnies},
  journal = {ACM Transactions on Graphics (proceedings of SIGGRAPH)},
  year    = {2016}
}

@inproceedings{PatRajRei89,
  author    = {R. Paturi and S. Rajasekaran and J. Reif},
  title     = {The light bulb problem},
  booktitle = {Proc. 2nd Annu. Workshop on Comput. Learning Theory},
  year      = {1989},
  pages     = {261--268},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@inproceedings{PatSak90,
  author    = {R. Paturi and M. E. Saks},
  title     = {On threshold circuits for parity},
  booktitle = {Proc. of the 31st Symposium on the Foundations of Comp.
               Sci.},
  year      = {1990},
  pages     = {397--404},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@article{Pavlopoulos2011,
  author   = {Pavlopoulos, Georgios A. and Secrier, Maria and
              Moschopoulos, Charalampos N. and Soldatos, Theodoros G. and
              Kossida, Sophia and Aerts, Jan and Schneider, Reinhard and
              Bagos, Pantelis G.},
  title    = {Using graph theory to analyze biological networks},
  journal  = {BioData Mining},
  year     = {2011},
  month    = {Apr},
  day      = {28},
  volume   = {4},
  number   = {1},
  pages    = {10},
  abstract = {Understanding complex systems often requires a bottom-up
              analysis towards a systems biology approach. The need to
              investigate a system, not only as individual components but
              as a whole, emerges. This can be done by examining the
              elementary constituents individually and then how these are
              connected. The myriad components of a system and their
              interactions are best characterized as networks and they
              are mainly represented as graphs where thousands of nodes
              are connected with thousands of vertices. In this article
              we demonstrate approaches, models and methods from the
              graph theory universe and we discuss ways in which they can
              be used to reveal hidden properties and features of a
              network. This network profiling combined with knowledge
              extraction will help us to better understand the biological
              significance of the system.},
  issn     = {1756-0381},
  doi      = {10.1186/1756-0381-4-10},
  url      = {https://doi.org/10.1186/1756-0381-4-10}
}

@article{PawKohMul96,
  author  = {K.~Pawelzik and J.~Kohlmorgen and K.-R.~M{\"u}ller},
  title   = {Annealed Competition of Experts for a Segmentation and
             Classification of Switching Dynamics},
  journal = {Neural Computation},
  year    = {1996},
  volume  = {8},
  pages   = {342--358},
  number  = {2}
}

@inproceedings{PawMulKoh96,
  author    = {K.~Pawelzik and K.-R.~M\"uller and J.~Kohlmorgen},
  title     = {Prediction of Mixtures},
  booktitle = {Artificial Neural Networks {ICANN'96}},
  year      = {1996},
  editor    = {C.~von der Malsburg and W.~von Seelen and
               J.~C.~Vorbr{\"u}ggen and B.~Sendhoff},
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  series    = {Lecture Notes in Computer Science},
  pages     = {127--133},
  address   = {Berlin},
  publisher = {Springer-Verlag}
}

@inproceedings{PayEdw00,
  author    = {Terry R. Payne and Peter Edwards},
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  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {331--343},
  publisher = {Springer, Berlin}
}

@article{PazKib92,
  author    = {M. Pazzani and Dennis Kibler},
  title     = {The Utility of Knowledge in Inductive Learning},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {9},
  pages     = {57--94},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{PazMerMurAlietal94,
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  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1994},
  pages     = {217--225},
  publisher = {Morgan Kaufmann}
}

@article{PazSar92,
  author    = {M. J. Pazzani and Wendy Sarrett},
  title     = {A Framework for Average Case Analysis of Conjunctive
               Learning Algorithms},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {9},
  pages     = {349--372},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Pazzani93,
  author    = {M. Pazzani},
  title     = {Learning Causal Patterns: Making a Transition from
               Data-Driven to Theory-Driven Learning},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {11},
  pages     = {173--194},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Pazzani93b,
  author    = {M. Pazzani},
  title     = {A Reply to Cohen's Book Review of Creating a Memory of
               Causal Relationships},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {10},
  pages     = {185--190},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Pazzani94,
  author    = {M. J. Pazzani},
  title     = {Guest Editor's Introduction},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {16},
  pages     = {7--9},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Pazzani96,
  author  = {M. Pazzani},
  title   = {Book review: inductive logic programming: techniques and
             applications},
  journal = {Machine Learning},
  year    = {1996},
  volume  = {23},
  pages   = {103--108},
  number  = {1}
}

@article{Pazzani96b,
  author    = {M. Pazzani},
  title     = {Review of {\it Inductive Logic Programming: Techniques and
               Applications} by Nada Lavra\u{c}, Sa\u{s}o {D}\u{z}eroski},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {23},
  pages     = {103--108},
  publisher = {Kluwer Academic Publishers, Boston}
}

@unpublished{pbmTool,
  note = {\url{http://napsu.karmitsa.fi/proxbundle/}}
}

@inproceedings{PeaDec89,
  author    = {J. Pearl and R. Dechter},
  title     = {Learning structure from data: a survey},
  booktitle = {Proc. 2nd Annu. Workshop on Comput. Learning Theory},
  year      = {1989},
  pages     = {230--244},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@inproceedings{PeaPar96,
  author    = {B. Pearlmutter and L. Parra},
  title     = {A Context-Sensitive Generalisation of {ICA}},
  booktitle = {Proc. ICONIP},
  year      = {1996}
}

@techreport{PeaPaz85,
  author      = {J. Pearl and A. Paz},
  title       = {{GRAPHOIDS}: {A} Graph-Based Logic for Reasoning about
                 Relevance Relations, or When would x tell you more about y
                 if you already know z},
  institution = {UCLA Computer Science Department},
  year        = {1985},
  month       = dec,
  comment     = {Axiomatic definition of graphoids.}
}

@inproceedings{PeaPaz86,
  author    = {J. Pearl and A. Paz},
  title     = {On the Logic of Representing Dependencies by Graphs},
  booktitle = {Proc. 1986 Canadian AI Conference},
  year      = {1986},
  month     = may,
  comment   = {Defines and studies graphoids for representing
               dependencies}
}

@book{Pearl01,
  title     = {Causality: Models, Reasoning and Inference},
  publisher = {Cambridge University Press},
  year      = {2001},
  author    = {J. Pearl}
}

@article{Pearl78,
  author  = {J. Pearl},
  title   = {On the Connection Between the Complexity and Credibility
             of Inferred Models},
  journal = {Journal of General Systems},
  year    = {1978},
  volume  = {4},
  pages   = {255--264},
  comment = {Studies tradeoff between uniqueness and ambiguity in the
             selection of hypotheses. Introduces the use of the
             Vapnik-Chervonenkis dimension.}
}

@article{Pearl79,
  author  = {J. Pearl},
  title   = {Capacity and Error Estimates for Boolean Classifiers with
             Limited Complexity},
  journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
  year    = {1979},
  volume  = {PAMI-1},
  pages   = {350--355},
  number  = {4},
  month   = oct,
  comment = {An extension of [Pe78].}
}

@book{Pearl84,
  title     = {Heuristics: Intelligent Search Strategies for computer
               problem solving},
  publisher = {Addison-Wesley},
  year      = {1984},
  author    = {J.~Pearl},
  address   = {Reading, {MA}}
}

@techreport{Pearl85,
  author      = {J. Pearl},
  title       = {Bayes Decision Methods},
  institution = {UCLA Computer Science Department},
  year        = {1985},
  number      = {CSD-850023},
  month       = jun
}

@techreport{Pearl85b,
  author      = {J. Pearl},
  title       = {Bayesian Networks: {A} Model of Self-Activated Memory for
                 Evidential Reasoning},
  institution = {UCLA Computer Science Department},
  year        = {1985},
  number      = {CSD-850021, R-43},
  month       = jun,
  comment     = {Describes Bayesian networks and propagation in
                 singly-connected networks.}
}

@techreport{Pearl85c,
  author      = {J. Pearl},
  title       = {How to do with Probabilities What People Say You Can't},
  institution = {UCLA Computer Science Department},
  year        = {1985},
  number      = {CSD-850031, R-49},
  month       = sep,
  comment     = {Describes Bayesian networks and propagation of beliefs.}
}

@techreport{Pearl85d,
  author      = {J. Pearl},
  title       = {On Evidential Reasoning In {A} Hierarchy Of Hypothesis},
  institution = {UCLA Computer Science Department},
  year        = {1985},
  number      = {CSD-850032},
  month       = sep
}

@book{Pearl88,
  title     = {Probabilistic Reasoning in Intelligent Systems: Networks
               of Plausible Inference},
  publisher = {Morgan Kaufman},
  year      = {1988},
  author    = { J. Pearl }
}

@book{pearl:88,
  title     = {Probabilistic {R}easoning in {I}ntelligent {S}ystems:
               {N}etworks of {P}lausible {I}nference},
  publisher = {Morgan Kaufman Publishers},
  year      = {1988},
  author    = {Judea Pearl},
  address   = {San Mateo, CA}
}

@article{Pearlmutter94,
  author  = {Barak A. Pearlmutter},
  title   = {Fast Exact Multiplication by the {H}essian},
  journal = {Neural Computation},
  year    = {1994},
  volume  = {6},
  pages   = {147-160},
  number  = {1}
}

@article{Pearson01,
  author  = {K.~Pearson},
  title   = {On Lines and Planes of Closest Fit to Points in Space},
  journal = {Philosophical Magazine},
  year    = {1901},
  volume  = {2 (sixth series)},
  pages   = {559--572},
  kwds    = {stat, svd, total least squares}
}

@book{Pearson83,
  title     = {Handbook of Applied Mathematics},
  publisher = {Van Nostrand Reinhold Company},
  year      = {1983},
  editor    = {C. E. Pearson},
  address   = {New York}
}

@inproceedings{PeaSmi96,
  author    = {R. A. Pearson and E. K. T. Smith},
  title     = {Reducing Complexity of Decision Trees with Two Variable
               Tests},
  booktitle = alt-ws,
  year      = {1996},
  volume    = {1160},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {91--99},
  publisher = {Springer-Verlag}
}

@techreport{PeaVer90,
  author      = {J. Pearl and T. S. Verma},
  title       = {A Formal Theory of Inductive Causation},
  institution = {UCLA Cognitive Systems Laboratory},
  year        = {1990},
  number      = {R-1555 (I)},
  month       = oct
}

@article{PeaWooZhaMil97,
  author  = {W.~R.~Pearson and T.~Wood and Z.~Zhang and W.~Miller},
  title   = {{Comparison of DNA Sequences with Protein Sequences}},
  journal = {Genomics},
  year    = {1997},
  volume  = {46},
  pages   = {24--36},
  number  = {1},
  month   = nov
}

@inproceedings{PedNie97,
  author    = {A.~G.~Pedersen and H.~Nielsen},
  title     = {{Neural Network Prediction of Translation Initiation Sites
               in Eukaryotes: Perspectives for EST and Genome analysis}},
  booktitle = {ISMB'97},
  year      = {1997},
  volume    = {5},
  pages     = {226--233}
}

@inproceedings{PelMoo00,
  author    = {Dan Pelleg and Andrew Moore},
  title     = {${X}$-means: {E}xtending ${K}$-means with Efficient
               Estimation of the Number of Clusters},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {727--734},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{Peng95,
  author    = {Jing Peng},
  title     = {Efficient memory-based dynamic programming},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {438--446},
  publisher = {Morgan Kaufmann}
}

@book{PenGin99,
  title     = {Decoupling: from Dependence to Independence},
  publisher = {Springer},
  year      = {1999},
  author    = {V. de la Pe\~na and E. Gin\'e},
  address   = {New York}
}

@inproceedings{PenMayGilHor00,
  author    = {David M. Pennock and Pedrito {Maynard-Reid II} and C. Lee
               Giles and Eric Horvitz},
  title     = {A Normative Examination of Ensemble Learning Algorithms},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {735--742},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{PenMcG98,
  author    = {Mark D. Pendrith and M. J. McGarity},
  title     = {An analysis of direct reinforcement learning in
               non-{M}arkovian domains},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {421--429},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{Pennock98,
  author    = {David M. Pennock},
  title     = {Logarithmic time parallel bayesian inference},
  booktitle = {Proc. 14th Conf. Uncertainty in Artificial Intelligence},
  year      = {1998},
  pages     = {431--438},
  publisher = {Morgan Kaufmann}
}

@book{penot2012calculus,
  title     = {Calculus without derivatives},
  author    = {Penot, Jean-Paul},
  volume    = {266},
  year      = {2012},
  publisher = {Springer Science \& Business Media}
}

@article{Penot_AreDualities_2010,
  title    = {Are dualities appropriate for duality theories in
              optimization?},
  volume   = {47},
  issn     = {0925-5001, 1573-2916},
  doi      = {10.1007/s10898-009-9478-z},
  language = {en},
  number   = {3},
  journal  = {Journal of Global Optimization},
  author   = {Penot, Jean-Paul},
  month    = jul,
  year     = {2010},
  pages    = {503--525},
  file     = {Penot - Are dualities appropriate for duality theories in
              optimization
              (2010).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Penot - Are dualities appropriate for duality
              theories in optimization (2010).pdf:application/pdf}
}

@incollection{Penot_BearingDuality_2005,
  address   = {Tokyo, Japan},
  title     = {The bearing of duality on microeconomics},
  isbn      = {978-4-431-27233-5},
  abstract  = {We present some observations about links between some
               classical theories of microeconomics and dualities which
               have been used in optimization theory and in the study of
               first-order Hamilton-Jacobi equations. We introduce a
               variant of the classical indirect utility function called
               the wary indirect utility function and a variant of the
               expenditure function. We focus the attention on the links
               between these functions, observing that they have better
               relationships with the direct functions than their
               classical forms and we give economic interpretations of
               them.},
  booktitle = {Advances in {Mathematical} {Economics}},
  publisher = {Springer},
  author    = {Penot, Jean-Paul},
  editor    = {Kusuoka, Shigeo and Yamazaki, Akira},
  year      = {2005},
  doi       = {10.1007/4-431-27233-X_5},
  pages     = {113--139},
  file      = {Penot - The bearing of duality on microeconomics
               (2005).pdf:/Users/zcranko/Documents/Library/Book
               Section/Penot - The bearing of duality on microeconomics
               (2005).pdf:application/pdf}
}

@incollection{Penot_BoundedHausdorff_2005,
  address    = {Boston, MA, USA},
  series     = {Nonconvex {Optim}. {Appl}.},
  title      = {Bounded ({Hausdorff}) convergence: basic facts and
                applications},
  volume     = {79},
  isbn       = {978-0-387-24209-5},
  shorttitle = {Bounded ({Hausdorff}) {Convergence}},
  language   = {en},
  booktitle  = {Variational {Analysis} and {Applications}},
  publisher  = {Springer},
  author     = {Penot, Jean-Paul and Zălinescu, Constantin},
  editor     = {Giannessi, Franco and Maugeri, Antonino},
  year       = {2005},
  doi        = {10.1007/0-387-24276-7_49},
  pages      = {827--854},
  file       = {Penot, Zălinescu - Bounded (Hausdorff) convergence
                (2005).pdf:/Users/zcranko/Documents/Library/Book
                Section/Penot, Zălinescu - Bounded (Hausdorff) convergence
                (2005).pdf:application/pdf}
}

@book{Penot_CalculusDerivatives_2013,
  address   = {New York, NY, USA},
  series    = {Graduate texts in mathematics},
  title     = {Calculus without derivatives},
  isbn      = {978-1-4614-4537-1},
  language  = {eng},
  number    = {266},
  publisher = {Springer},
  author    = {Penot, Jean-Paul},
  year      = {2013},
  note      = {OCLC: 794709591},
  file      = {Penot - Calculus without derivatives
               (2013).pdf:/Users/zcranko/Documents/Library/Book/Penot -
               Calculus without derivatives (2013).pdf:application/pdf}
}

@article{Penot_DirectionalSubdifferential_2011,
  title    = {The directional subdifferential of the difference of two
              convex functions},
  volume   = {49},
  issn     = {0925-5001, 1573-2916},
  doi      = {10.1007/s10898-010-9615-8},
  language = {en},
  number   = {3},
  journal  = {Journal of Global Optimization},
  author   = {Penot, Jean-Paul},
  month    = mar,
  year     = {2011},
  pages    = {505--519},
  file     = {Penot - The directional subdifferential of the difference
              of two convex functions
              (2011).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Penot - The directional subdifferential of the
              difference of two convex functions
              (2011).pdf:application/pdf}
}

@article{Penot_DualityAnticonvex_2001,
  title    = {Duality for anticonvex programs},
  volume   = {19},
  issn     = {1573-2916},
  doi      = {10.1023/a:1008327614099},
  abstract = {Calling anticonvex a program which is either a
              maximization of a convex function on a convex set or a
              minimization of a convex function on the set of points
              outside a convex subset, we introduce several dual problems
              related to each of these problems. We give conditions
              ensuring there is no duality gap. We show how solutions to
              the dual problems can serve to locate solutions of the
              primal problem.},
  number   = {2},
  journal  = {Journal of Global Optimization},
  author   = {Penot, Jean-Paul},
  month    = feb,
  year     = {2001},
  pages    = {163--182},
  file     = {Penot - Duality for anticonvex programs
              (2001).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Penot - Duality for Anticonvex Programs
              (2001).pdf:application/pdf}
}

@article{Penot_DualityRadiant_1997,
  title    = {Duality for radiant and shady programs},
  volume   = {22},
  number   = {2},
  journal  = {Acta Mathematica Vietnamica},
  author   = {Penot, Jean-Paul},
  year     = {1997},
  keywords = {⛔ No DOI found},
  pages    = {541--566},
  file     = {Penot - Duality for radiant and shady programs
              (1997).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Penot - Duality for radiant and shady programs
              (1997).pdf:application/pdf}
}

@article{Penot_ElementsQuasiconvex_2000,
  title    = {Elements of quasiconvex subdifferential calculus},
  volume   = {7},
  language = {eng},
  number   = {2},
  journal  = {Journal of Convex Analysis},
  author   = {Penot, Jean-Paul and Zălinescu, Constantin},
  year     = {2000},
  keywords = {⛔ No DOI found, generalized convex functions,
              Greenberg-Pierskalla's subdifferential, harmonic sum,
              incident derivative, lower subdifferential, normal cone,
              quasiconvex functions, sublevel-convolution},
  pages    = {243--269},
  file     = {Penot, Zălinescu - Elements of quasiconvex
              subdifferential calculus
              (2000).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Penot, Zălinescu - Elements of quasiconvex
              subdifferential calculus (2000).pdf:application/pdf}
}

@article{Penot_HarmonicSum_2000,
  title    = {Harmonic {Sum} and {Duality}},
  volume   = {7},
  number   = {1},
  journal  = {Journal of Convex Analysis},
  author   = {Penot, Jean-Paul and Zǎlinescu, Constantin},
  year     = {2000},
  keywords = {⛔ No DOI found},
  pages    = {95--114},
  file     = {Penot, Zǎlinescu - Harmonic Sum and Duality
              (2000).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Penot, Zǎlinescu - Harmonic Sum and Duality
              (2000).pdf:application/pdf}
}

@article{Penot_MinimizationDifference_1998,
  title    = {On the minimization of difference functions},
  volume   = {12},
  issn     = {09255001},
  doi      = {10.1023/a:1008233531797},
  number   = {4},
  journal  = {Journal of Global Optimization},
  author   = {Penot, Jean-Paul},
  year     = {1998},
  keywords = {DC programming},
  pages    = {373--382},
  file     = {Penot - On the minimization of difference functions
              (1998).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Penot - On the minimization of difference functions
              (1998).pdf:application/pdf}
}

@incollection{Penot_MonotonicityDualities_2006,
  title     = {Monotonicity and {Dualities}},
  volume    = {583},
  isbn      = {978-3-540-37006-2},
  language  = {en},
  booktitle = {Generalized {Convexity} and {Related} {Topics}},
  publisher = {Springer Berlin Heidelberg},
  author    = {Penot, Jean-Paul},
  year      = {2006},
  doi       = {10.1007/978-3-540-37007-9_24},
  pages     = {399--414},
  file      = {Penot - Monotonicity and Dualities
               (2006).pdf:/Users/zcranko/Documents/Library/Book
               Section/Penot - Monotonicity and Dualities
               (2006).pdf:application/pdf}
}

@article{Penot_RadiantCoradiant_2010,
  title    = {Radiant and coradiant dualities},
  volume   = {6},
  abstract = {We associate a dual problem to a constrained optimization
              problem in which the objective is quasiconvex and either
              attains at 0 its global minimum or its global maximum. The
              attractive features of such a duality are that it does not
              require an additional parameter to set the dual and that
              the dual problem has a form which is similar to the one of
              the primal problem. We present conditions ensuring strong
              duality using separation properties. We relate our approach
              to the Lagrangian theory.},
  number   = {2},
  journal  = {Pacific journal of optimization},
  author   = {Penot, Jean-Paul},
  year     = {2010},
  keywords = {⛔ No DOI found},
  pages    = {263--279},
  file     = {Penot - Radiant and coradiant dualities
              (2010).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Penot - Radiant and coradiant dualities
              (2010).pdf:application/pdf}
}

@article{Penot_RevisitingRules_2017,
  title    = {Revisiting some rules of convex analysis},
  volume   = {25},
  issn     = {1877-0533, 1877-0541},
  doi      = {10.1007/s11228-017-0462-3},
  language = {en},
  number   = {4},
  journal  = {Set-Valued and Variational Analysis},
  author   = {Penot, Jean-Paul},
  month    = dec,
  year     = {2017},
  pages    = {773--788},
  file     = {Penot - Revisiting some rules of convex analysis
              (2017).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Penot - Revisiting some rules of convex analysis
              (2017).pdf:application/pdf}
}

@article{Penot_SubdifferentialCalculus_1996,
  title    = {Subdifferential calculus without qualification
              assumptions},
  volume   = {3},
  issn     = {0944-6532},
  number   = {2},
  journal  = {J. Convex Anal.},
  author   = {Penot, Jean-Paul},
  year     = {1996},
  note     = {tex.fjournal: Journal of Convex Analysis tex.mrclass:
              49J52 (47N10 90C46) tex.mrnumber: 1448052},
  keywords = {⛔ No DOI found},
  pages    = {207--219},
  file     = {Penot - Subdifferential calculus without qualification
              assumptions
              (1996).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Penot - Subdifferential calculus without
              qualification assumptions (1996).pdf:application/pdf}
}

@incollection{Penot_UnilateralAnalysis_2005,
  address   = {New York, USA},
  title     = {Unilateral analysis and duality},
  isbn      = {978-0-387-25569-9},
  language  = {en},
  booktitle = {Essays and {Surveys} in {Global} {Optimization}},
  publisher = {Springer-Verlag},
  author    = {Penot, Jean-Paul},
  editor    = {Audet, Charles and Hansen, Pierre and Savard, Gilles},
  year      = {2005},
  doi       = {10.1007/0-387-25570-2_1},
  keywords  = {c-congugates, c-subdifferentials, generalised convexity},
  pages     = {1--37},
  file      = {Penot - Unilateral analysis and duality
               (2005).pdf:/Users/zcranko/Documents/Library/Book
               Section/Penot - Unilateral analysis and duality
               (2005).pdf:application/pdf}
}

@article{Penot_VariationalAnalysis_2013,
  title    = {Variational analysis for the consumer theory},
  volume   = {159},
  issn     = {0022-3239, 1573-2878},
  doi      = {10.1007/s10957-013-0289-5},
  language = {en},
  number   = {3},
  journal  = {Journal of Optimization Theory and Applications},
  author   = {Penot, Jean-Paul},
  month    = dec,
  year     = {2013},
  pages    = {769--794}
}

@article{Penot_WhatQuasiconvex_2000,
  title   = {What is quasiconvex analysis?},
  volume  = {47},
  issn    = {0233-1934},
  doi     = {10.1080/02331930008844469},
  number  = {1-2},
  journal = {Optimization},
  author  = {Penot, Jean-Paul},
  month   = jan,
  year    = {2000},
  pages   = {35--110},
  file    = {Penot - What is quasiconvex analysis
             (2000).pdf:/Users/zcranko/Documents/Library/Journal
             Article/Penot - What is quasiconvex analysis
             (2000).pdf:application/pdf}
}

@unpublished{PenRod16,
  author = {Javier Pena and Daniel Rodriguez},
  title  = { Polytope conditioning and linear convergence of the
            Frank-Wolfe algorithm, note = {arXiv:1512.06142}, year =
            {2016} }
}

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  year      = {1996},
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  journal   = {Machine Learning},
  year      = {1996},
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  annote    = {Earlier version in ML94},
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  author  = {Jing Peng and Ronald J. Williams},
  title   = {Technical note: incremental multi-step {Q}-learning},
  journal = {Machine Learning},
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  volume  = {22},
  pages   = {283--290},
  number  = {1/2/3}
}

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  year    = {1993},
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  year      = {2001},
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  address   = {Cambridge, MA},
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  editor    = {M.~Gasca and C.~A.~Miccheli},
  volume    = {359},
  series    = {Mathematics and its Applications},
  pages     = {1-35},
  month     = {March}
}

@article{PinLiuWu01,
  author  = {J. C. Pinheiro and C. Liu and Y. Wu},
  title   = {Efficient algorithms for robust estimation in linear
             mixed-effects models using the multivariate
             $t$-distribution},
  journal = {Journal of Computational and Graphical Statistics},
  year    = 2001,
  volume  = 10,
  pages   = {249--276}
}

@techreport{PinPri87,
  author      = {S. Pinker and A. Prince},
  title       = {On Language and Connectionism: Analysis of a Parallel
                 Distributed Processing Model of Language Acquisition},
  institution = {MIT Center for Cognitive Science},
  year        = {1987},
  number      = {Occasional Paper 33},
  comment     = {Critique of Rumelhart and McClelland's connectionist
                 approach to learning English past tense construction.}
}

@inproceedings{Pinter95,
  author    = {Charles C. Pinter},
  title     = {Complexity of Network Training for Classes of Neural
               Networks},
  booktitle = alt-ws,
  year      = {1995},
  volume    = {997},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {215--227},
  publisher = {Springer-Verlag}
}

@incollection{Pippig95,
  author    = {E. Pippig},
  title     = {Structuring Neural Networks and {PAC} Learning},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {417--434}
}

@inproceedings{Piramuthu96,
  author    = {Selwyn Piramuthu},
  title     = {Effects of Feature Selection with 'Blurring' on Neurofuzzy
               Systems},
  booktitle = alt-ws,
  year      = {1996},
  volume    = {1160},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {135--142},
  publisher = {Springer-Verlag}
}

@inproceedings{Pisier81,
  author    = {G.~Pisier},
  title     = {Remarques sur un resultat non publi\`e de {B.~Maurey}},
  booktitle = {Seminarie d'analyse fonctionelle 1980--1981},
  year      = {1981},
  editor    = {Centre de Mathematique},
  address   = {Palaiseau}
}

@book{Pisier89,
  title     = {The Volume of Convex Bodies and {B}anach Space Geometry},
  publisher = {Cambridge University Press},
  year      = {1989},
  author    = {G.~Pisier},
  address   = {Cambridge}
}

@article{PisSem95,
  author  = {Pistone, Giovanni and Sempi, Carlo},
  journal = {The Annals of Statistics},
  number  = {5},
  pages   = {1543--1561},
  title   = {An Infinite-Dimensional Geometric Structure on the Space
             of all the Probability Measures Equivalent to a Given One},
  url     = {https://doi.org/10.1214/aos/1176324311},
  volume  = {23},
  year    = {1995}
}

@article{Pistone13,
  title   = {Examples of the Application of Nonparametric Information
             Geometry to Statistical Physics},
  author  = {Giovanni Pistone},
  journal = {Entropy},
  year    = {2013},
  volume  = {15},
  pages   = {4042--4065}
}

@techreport{PitBoa87,
  author      = {L. Pitt and R. Board},
  title       = {Semi-supervised learning},
  institution = {University of Illinois, UIUCDCS-R-87-1372},
  year        = {1987}
}

@inproceedings{PitBoa90,
  author    = {L. Pitt and R. Board},
  title     = {On the necessity of {O}ccam algorithms},
  booktitle = {Proc. 23th Annu. ACM Sympos. Theory Comput.},
  year      = {1990},
  pages     = {54--63},
  publisher = {ACM Press}
}

@article{Pitman36,
  author  = {Pitman, E.},
  title   = {Sufficient statistics and intrinsic accuracy},
  journal = {the Cambridge Philosophical Society},
  year    = {1936},
  volume  = {32},
  pages   = {567-579}
}

@article{Pitman39,
  author  = {E.~J.~G.~Pitman},
  title   = {The Estimation of The Location and Scale Parameters of a
             Continuous Population of any given form},
  journal = {Biometrika},
  year    = {1939},
  volume  = {30},
  pages   = {391--421}
}

@article{Pitman95,
  author  = {Jim Pitman},
  journal = {Probability Theory and Related Fields},
  number  = {2},
  pages   = {145--158},
  title   = {Exchangeable and partially exchangeable random
             partitions},
  url     = {http://dx.doi.org/10.1007/BF01213386},
  volume  = {102},
  year    = {1995}
}

@article{PitRei88,
  author    = {Leonard Pitt and Robert E. Reinke},
  title     = {Criteria for Polynomial-Time (Conceptual) Clustering},
  journal   = {Machine Learning},
  year      = {1988},
  volume    = {2},
  pages     = {371--396},
  number    = {4},
  publisher = {Kluwer Academic Publishers, Boston}
}

@phdthesis{Pitsianis92,
  author = {N.~P.~Pitsianis},
  title  = {The {K}ronecker Product in Approximation and Fast
            Transform Generation},
  school = {Department of Computer Science, Cornell University},
  year   = {1992}
}

@article{PitSmi88,
  author  = {L. Pitt and C. Smith},
  title   = {Probability and Plurality for Aggregations of Learning
             Machines},
  journal = {Inform. Comput.},
  year    = {1988},
  volume  = {77},
  pages   = {77--92},
  annote  = {Earlier version in 1987 ICALP, and 1986 TR-1686 from Univ.
             of MD.},
  comment = {Show that one can not always trade off probabilism for
             plurality and vice-versa.}
}

@phdthesis{Pitt85,
  author  = {L. Pitt},
  title   = {Probabilistic {I}nductive {I}nference (Ph.{D} Thesis)},
  school  = {Yale University},
  year    = {1985},
  note    = {Computer Science Dept. TR-400},
  comment = {Ph.D. thesis. Defines probabilistic inference and shows
             probabilistic inference equivalence to teams of inductive
             inference machines; and shows strict hierarchy on
             probability with cut-points 1/2, 1/3, 1/4, ...}
}

@inproceedings{Pitt89,
  author    = {L.~Pitt},
  title     = {Inductive Inference, {DFA}s, and Computational
               Complexity},
  booktitle = {the 2nd Workshop on Analogical and Inductive Inference},
  year      = {1989},
  volume    = {397},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {18044},
  address   = {Berlin},
  publisher = {Springer-Verlag}
}

@article{Pitt89b,
  author  = {L. Pitt},
  title   = {Probabilistic Inductive Inference},
  journal = {J. ACM},
  year    = {1989},
  volume  = {36},
  pages   = {383--433},
  number  = {2},
  note    = {Earlier version in 1984 STOC conference. Part of his 1985
             Yale Ph.D Thesis.}
}

@article{Pitt90,
  author    = {Leonard Pitt},
  title     = {Introduction: Special Issue on Computational Learning
               Theory},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  pages     = {117--120},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Pitt97,
  author    = {Leonard Pitt},
  title     = {On Exploiting Knowledge and Concept Use in Learning
               Theory},
  booktitle = alt-ws,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {62--84},
  publisher = {Springer-Verlag}
}

@article{PitVal88,
  author  = {L. Pitt and L. Valiant},
  title   = {Computational limitations on learning from examples},
  journal = {J. ACM},
  year    = {1988},
  volume  = {35},
  pages   = {965--984},
  annote  = {Was Harvard University TR 6 in 1986},
  comment = {It is NP-Complete to learn disjunction of two monomials,
             Boolean threshold functions, Boolean formulae where each
             variable occurs at most once.}
}

@article{PitWar90,
  author  = {L. Pitt and M. K. Warmuth},
  title   = {Prediction Preserving Reducibility},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1990},
  volume  = {41},
  pages   = {430--467},
  number  = {3},
  month   = dec,
  note    = {Special issue of the for the {\em Third Annual Conference
             of Structure in Complexity Theory} (Washington, DC., June
             88)},
  annote  = {Was UCSC-CRL-88-26}
}

@article{PitWar93,
  author  = {L. Pitt and M. K. Warmuth},
  title   = {The minimum consistent {DFA} problem cannot be
             approximated within any polynomial},
  journal = {J. ACM},
  year    = {1993},
  volume  = {40},
  pages   = {95--142},
  number  = {1},
  annote  = {Earlier version in STOC89 and Univ of Ill TR 1499 in
             1989}
}

@article{PitYor97,
  author  = {J. Pitman and M. Yor},
  title   = {The two-parameter Poisson-Dirichlet distribution derived
             from a stable subordinator},
  journal = {Annals of Probability},
  year    = 1997,
  volume  = 25,
  number  = 2,
  pages   = {855-900}
}

@incollection{PivFin64,
  author    = {M. Pivar and M. Finkelstein},
  title     = {Automation, using {LISP}, of Inductive Inference on
               Sequences},
  booktitle = {The Programming Language LISP},
  publisher = {{MIT} Press},
  year      = {1964},
  pages     = {125--136},
  key       = {Pivar and Finkelstein}
}

@incollection{PivGor64,
  author    = {M. Pivar and E. Gord},
  title     = {The {LISP} Program for Inductive Inference on Sequences},
  booktitle = {The Programming Language LISP},
  publisher = {{MIT} Press},
  year      = {1964},
  pages     = {260--289}
}

@unpublished{pkdd2009challenge,
  note = {\url{https://www.kde.cs.uni-kassel.de/wp-content/uploads/ws/dc09/}}
}

@inproceedings{PlaCriSha00,
  author    = {J.~C.~Platt and N.~Cristianini and J.~Shawe-Taylor},
  title     = {Large Margin {DAG}s for Multiclass Classification},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A.~Solla and T.~K.~Leen and K.-R.~M{\"u}ller},
  pages     = {547--553},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{PlaTouNak09,
  author    = {Nils Plath and Marc Toussaint and Shinichi Nakajima},
  title     = {Multi-class image segmentation using Conditional Random
               Fields and Global Classification},
  booktitle = icml,
  year      = {2009}
}

@inproceedings{Platt00,
  author    = {J.~Platt},
  title     = {Probabilities for {SV} machines},
  booktitle = {Advances in Large Margin Classifiers},
  year      = {2000},
  editor    = {A. J. Smola and P.~L.~Bartlett and B.~Sch{\"o}lkopf and
               D.~Schuurmans},
  pages     = {61--73},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{Platt91,
  author  = {J.~C.~Platt},
  title   = {A Resource-Allocating Network for Function Interpolation},
  journal = {Neural Computation},
  year    = {1991},
  volume  = {3},
  pages   = {213--225},
  number  = {2}
}

@techreport{Platt98,
  author      = {J.~C.~Platt},
  title       = {Sequential Minimal Optimization: A Fast Algorithm for
                 Training Support Vector Machines},
  institution = {Microsoft Research},
  year        = {1998},
  number      = {MSR-TR-98-14}
}

@inproceedings{Platt99,
  author    = {J.~Platt},
  title     = {Fast Training of Support Vector Machines using Sequential
               Minimal Optimization},
  booktitle = {Advances in Kernel Methods\,---\,Support Vector Learning},
  year      = {1999},
  editor    = {B.~Sch{\"o}lkopf and C.~J.~C.~Burges and A. J. Smola},
  pages     = {185--208},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@incollection{Platt99a,
  author    = {John C. Platt},
  title     = {Fast Training of Support Vector Machines Using Sequential
               Minimal Optimization},
  booktitle = {Advances in Kernel Methods\,---\,Support Vector Learning},
  publisher = {{MIT} Press},
  year      = {1999},
  pages     = {185--208}
}

@inproceedings{Platt99b,
  author    = {John C. Platt},
  title     = {Probabilities for {SV} Machines},
  booktitle = {Advances in Large Margin Classifiers},
  year      = {1999},
  editor    = {A. J. Smola and P.~L. Bartlett and B. Sch{\"o}lkopf and D.
               Schuurmans},
  pages     = {61--74},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@incollection{Plotkin70,
  author    = {G. D. Plotkin},
  title     = {A note on inductive generalization},
  booktitle = {Machine Intelligence 5},
  publisher = {American Elsevier},
  year      = {1970},
  pages     = {153--163}
}

@phdthesis{Plotkin71,
  author = {G. D. Plotkin},
  title  = {Automatic {M}ethods of {I}nductive {I}nference},
  school = {Edinburgh University},
  year   = {1971}
}

@incollection{Plotkin71b,
  author    = {G. D. Plotkin},
  title     = {A further note on inductive generalization},
  booktitle = {Machine Intelligence 6},
  publisher = {American Elsevier},
  year      = {1971},
  pages     = {101--124}
}

@inproceedings{Plumbley04,
  author    = {M. Plumbley},
  title     = {Lie Group Methods for Optimization with Orthogonality
               Constraints},
  booktitle = {Fifth International Conference on ICA},
  pages     = {1245--1252},
  year      = {2004},
  series    = {Lecture Notes in Computer Science},
  address   = {Berlin},
  publisher = {Springer}
}

@article{Podnieks74,
  author  = {K. Podnieks},
  title   = {Comparing Various Concepts of Function Prediction, {P}art
             1},
  journal = {Theory of Algorithms and Programs, Latvian State
             University, Riga},
  year    = {1974},
  volume  = {210},
  pages   = {68--81},
  annote  = {Journal also called {\it Latv. Gosudarst. Univ. Uch.
             Zapiski}}
}

@article{Podnieks75,
  author  = {K. Podnieks},
  title   = {Comparing Various Concepts of Function Prediction, {P}art
             2},
  journal = {Theory of Algorithms and Programs, Latvian State
             University, Riga},
  year    = {1975},
  volume  = {233},
  annote  = {Journal also called {\it Latv. Gosudarst. Univ Uch.
             Zapiski}}
}

@article{Podnieks75b,
  author  = {K. M. Podnieks},
  title   = {Probabilistic synthesis of enumerated classes of
             functions},
  journal = {Soviet Math. Doklady},
  year    = {1975},
  volume  = {16},
  pages   = {1042--1045}
}

@article{Podnieks77,
  author  = {K. Podnieks},
  title   = {Probabilistic Program Synthesis},
  journal = {Theory of Algorithms and Programs, Latvian State
             University, Riga},
  year    = {1977},
  volume  = {16},
  pages   = {57--88}
}

@book{PogAns16,
  title     = {Visual Cortex and Deep Networks: Learning Invariant
               Representations},
  publisher = {The MIT Press},
  year      = {2016},
  author    = {Tomaso A. Poggio and Fabio Anselmi},
  address   = {Cambridge, MA, USA}
}

@techreport{PogBey93,
  author      = {T.~Poggio and D.~Beymer},
  title       = {Example Based Graphics for Grey-Level Images},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {in press, 1993},
  type        = {A.I. Memo No.},
  number      = {1431}
}

@article{PogBey96,
  author  = {T.~Poggio and D.~Beymer},
  title   = {Learning to See},
  journal = {IEEE Spectrum},
  year    = {1996},
  pages   = {60--69},
  month   = may
}

@techreport{PogBru92,
  author      = {T.~Poggio and R.~Brunelli},
  title       = {A novel approach to graphics},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1992},
  type        = {A.I.~Memo No.},
  number      = {1354}
}

@article{PogEde90,
  author  = {T.~Poggio and S.~Edelman},
  title   = {A network that learns to recognize 3{D} objects},
  journal = {Nature},
  year    = {1990},
  volume  = {343},
  pages   = {263--266}
}

@techreport{PogFahEde91,
  author      = {T.~Poggio and M.~Fahle and S.~Edelman},
  title       = {Synthesis of visual modules from examples: learning
                 hyperacuity},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1991},
  type        = {A.I.~Memo No.},
  number      = {1271}
}

@techreport{PogFahEde91b,
  author      = {T.~Poggio and M.~Fahle and S.~Edelman},
  title       = {Fast perceptual learning in visual hyperacuity},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1991},
  type        = {A.I.~Memo No.},
  number      = {1336}
}

@article{PogFahEde92,
  author  = {T.~Poggio and M.~Fahle and S.~Edelman},
  title   = {{Fast Perceptual Learning in Visual Hyperacuity}},
  journal = {Science},
  year    = {1992},
  volume  = {256},
  pages   = {1018--1021},
  month   = may
}

@article{PogGamLit88,
  author  = {T.~Poggio and E.~B.~Gamble and J.~J.~Little},
  title   = {Parallel integration of vision modules},
  journal = {Science},
  year    = {1988},
  volume  = {242},
  pages   = {436--440}
}

@article{Poggio75,
  author  = {T.~Poggio},
  title   = {On Optimal Nonlinear Associative Recall},
  journal = {Biological Cybernetics},
  year    = {1975},
  volume  = {19},
  pages   = {201--209}
}

@incollection{Poggio82,
  author    = {T.~Poggio},
  title     = {Visual Algorithms},
  booktitle = {Physical and Biological Processing of Images},
  publisher = {Springer},
  year      = {1982},
  editor    = {O.~J.~Braddick and A.~C.~Sleigh},
  pages     = {128--153},
  address   = {Berlin}
}

@techreport{Poggio90,
  author      = {T.~Poggio},
  title       = {3{D} Object Recognition: on a Result by {Basri and
                 Ullman}},
  institution = {IRST, Povo, Italy},
  year        = {1990},
  type        = {Technical {R}eport \# 9005-03}
}

@techreport{Poggio90b,
  author      = {T.~Poggio},
  title       = {A theory of how the brain might work},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1990},
  type        = {A.I.~Memo No.},
  number      = {1253}
}

@techreport{Poggio90c,
  author      = {T.~Poggio},
  title       = {{3D} Object Recognition: on a Result of {Basri and
                 Ullman}},
  institution = {IRST, Povo, Italy},
  year        = {1990},
  type        = {Technical Report \# 9005-03}
}

@inproceedings{Poggio90d,
  author    = {T.~Poggio},
  title     = {A Theory of How the Brain Might Work},
  booktitle = {Cold Spring Harbor Symposia on Quantitative Biology},
  year      = {1990},
  pages     = {899--910},
  publisher = {Cold Spring Harbor Laboratory Press}
}

@techreport{Poggio91,
  author      = {T.~Poggio},
  title       = {{3D} Object recognition and prototypes: one {2D} view may
                 be sufficient},
  institution = {I.R.S.T.},
  year        = {1991},
  type        = {Technical Report},
  number      = {9107-02},
  address     = {Povo, Italy},
  month       = jul
}

@techreport{PogGir89,
  author      = {T.~Poggio and F.~Girosi},
  title       = {A Theory of Networks for Approximation and Learning},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1989},
  number      = {{AIM}-1140},
  address     = {Cambridge, Massachusetts},
  month       = jul,
  pages       = {64},
  url         = {ftp://publications.ai.mit.edu/ai-publications/1000-1499/AIM-1140.ps.Z}
}

@article{PogGir89b,
  author  = {T. Poggio and F. Girosi},
  title   = {Representation Propoerties of Networks: Kolmogorov's
             Theorm Is Irrelevant},
  journal = {Neural Computation},
  year    = {1989},
  volume  = {1},
  pages   = {465--469}
}

@inproceedings{PogGir90,
  author    = {T.~Poggio and F.~Girosi},
  title     = {Extension of a Theory of Networks for Approximation and
               Learning: dimensionality reduction and Clustering},
  booktitle = {Proceedings Image Understanding Workshop},
  year      = {1990},
  pages     = {597--603},
  address   = {Pittsburgh, Pennsylvania},
  publisher = {Morgan Kaufmann Publishers}
}

@article{PogGir90b,
  author  = {T.~Poggio and F.~Girosi},
  title   = {Regularization algorithms for learning that are equivalent
             to multilayer networks},
  journal = {Science},
  year    = {1990},
  volume  = {247},
  pages   = {978--982}
}

@incollection{PogGir90c,
  author    = {T.~Poggio and F.~Girosi},
  title     = {{HyperBF}: {A} Powerful Approximation Technique for
               Learning},
  booktitle = {Artificial Intelligence at {MIT}: Expanding Frontiers,
               Vol.~1},
  publisher = {{MIT} Press},
  year      = {1990},
  editor    = {Patrick H.~Winston and Sarah A.~Shellard},
  address   = {Cambridge, MA}
}

@article{PogGir90d,
  author  = {T.~Poggio and F.~Girosi},
  title   = {Networks for Approximation and Learning},
  journal = {the {IEEE}},
  year    = {1990},
  volume  = {78},
  number  = {9},
  pages   = {1481--1497}
}

@techreport{PogGir90e,
  author      = {T.~Poggio and F.~Girosi},
  title       = {Continuous Stochastic Cellular Automata That Have a
                 Stationary Distribution and No Detailed Balance},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1990},
  type        = {A.I.~Memo},
  number      = {1168}
}

@article{PogGir90f,
  author  = {T.~Poggio and F.~Girosi},
  title   = {Regularization algorithms for learning that are equivalent
             to multilayer networks},
  journal = {Science},
  year    = {1990},
  volume  = {247},
  pages   = {978--982}
}

@inproceedings{PogGir93,
  author       = {T.~Poggio and F.~Girosi},
  title        = {{Learning Algorithms and Network Architectures}},
  booktitle    = {{Brain Theory: Spatio-Temporal Aspects of Brain
                  Function}},
  year         = {1993},
  editor       = {A.~Aertsen and W.~von Seelen},
  pages        = {29--49},
  address      = {Amsterdam},
  organization = {Trento Conference, 1992},
  publisher    = {Elsevier Science Publishers}
}

@article{PogGir94,
  author  = {T.~Poggio and F.~Girosi},
  title   = {Learning, networks and approximation theory},
  journal = {Official Bulletin of the Italian Society of Hospital
             Neurologists, Neurosurgeons and Neuroradiologists},
  year    = {1994},
  volume  = {XL},
  pages   = {375--380}
}

@article{PogGir98,
  author  = {T.~Poggio and F.~Girosi},
  title   = {A sparse representation for function approximation},
  journal = {Neural Computation},
  year    = {1998},
  volume  = {10},
  pages   = {1445--1454}
}

@inproceedings{PogGirJon93,
  author       = {T.~Poggio and F.~Girosi and M.~Jones},
  title        = {From regularization to radial, tensor and additive
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@article{qin2013introducing,
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@inproceedings{QiSzuMin05,
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@inproceedings{QuaCaeLimSch09,
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@incollection{QuaColDar04,
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@inproceedings{QuaPetSmo09,
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@inproceedings{QuaSmoCaeLe08,
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@article{Quattonietal07,
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@article{QuaWaMoretal07,
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@article{Quinlan90,
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@inproceedings{Quinlan91,
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@book{Quinlan93,
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@inbook{Quinlan94,
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@inproceedings{Quinlan95,
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@inproceedings{Quinlan96,
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@article{RabJua86,
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@article{RabLevSon83,
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@article{Rabut92,
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@book{Rachev96,
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@inproceedings{RacKasSalAha94,
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@article{Radermacher94,
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  institution = {Department of Computer Science, Royal Holloway, University
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  year        = {1998},
  number      = {NC-TR-1998-021},
  address     = {Egham, UK},
  note        = {To appear in {\em Machine Learning}.}
}

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  year    = {1998},
  note    = {submitted, also {NeuroCOLT 2} {TR-1998-21}}
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  year      = {2002}
}

@techreport{RatSchMikMul00,
  author      = {G.~{R\"atsch} and B.~{Sch\"olkopf} and S.~Mika and K.-R.
                 {M\"uller}},
  title       = {{SVM} and Boosting: One Class},
  institution = {{GMD}},
  year        = {2000},
  number      = {119},
  note        = {Submitted to {\em IEEE Transactions on Pattern Analysis
                 and Machine Intelligence}.}
}

@inproceedings{RatSchSmoMiketal00,
  author    = {G.~R\"atsch and B.~Sch\"olkopf and A. J. Smola and S.~Mika
               and T.~Onoda and K.~M\"uller},
  title     = {Robust ensemble learning},
  booktitle = {Advances in Large Margin Classifiers},
  year      = {2000},
  editor    = {A. J. Smola and P.~L.~Bartlett and B.~Sch{\"o}lkopf and
               D.~Schuurmans},
  pages     = {207--220},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{RatSchSmoMiketal00b,
  author    = {G.~R{\"a}tsch and B.~Sch{\"o}kopf and A. J. Smola and
               S.~Mika and T.~Onoda and K.-R.~M{\"u}ller},
  title     = {Robust Ensemble Learning for Data Mining},
  booktitle = {the Fourth Pacific-Asia Conference on Knowledge Discovery
               and Data Mining},
  year      = {2000},
  series    = {LNCS 1805},
  pages     = {341--344},
  publisher = {Springer}
}

@inproceedings{RatSchSmoMuletal00,
  author    = {G.~R{\"a}tsch and B.~Sch{\"o}lkopf and A. J. Smola and
               K.-R.~M{\"u}ller and T.~Onoda and S.~Mika},
  title     = {$\nu $-Arc: Ensemble Learning in the Presence of Outliers},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A.~Solla and T.~K.~Leen and K.-R.~M{\"u}ller},
  pages     = {561--567},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{RatSonSch05,
  author  = {G. Ratsch and S. Sonnenburg and B. Scholkopf},
  title   = {RASE: recognition of alternatively spliced exons in
             C.elegans},
  journal = {Bioinformatics},
  year    = {2005},
  volume  = {21 Suppl 1},
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@article{RatSonSriWitetal07,
  author  = {G.~{R\"atsch} and S. Sonnenburg and J. Srinivasan and H.
             Witte and K.-R. {M\"uller} and R. J. Sommer and B.
             Sch{\"ol}kopf},
  title   = {Improving the {C}aenorhabditis elegans Genome Annotation
             using Machine Learning},
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  number  = {2},
  pages   = {e20 doi:10.1371/journal.pcbi.0030020},
  year    = {2007}
}

@inproceedings{RatTscPer14,
  author    = {Martin Ratajczak and Sebastian Tschiatschek and Franz
               Pernkopf},
  title     = {Sum-Product Networks for Structured Prediction:
               Context-Specific Deep Conditional Random Fields},
  booktitle = {Workshop on Learning Tractable Probabilistic Models},
  year      = 2014
}

@inproceedings{RatWarMikLemetal00,
  author    = {G.~R{\"a}tsch and M. K. Warmuth and S.~Mika and S.~Lemm
               and K.-R.~M{\"u}ller},
  title     = {Barrier Boosting},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2000},
  address   = {Pittsburgh},
  month     = {feb},
  publisher = {ACM Press}
}

@inproceedings{RatWarMikOnoetal00,
  author    = {G. R{\"a}tsch and M. K. Warmuth and S. Mika and T. Onoda
               and S. Lemm and K. R. M{\"u}ller},
  title     = {Barrier Boosting},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {170--179},
  publisher = {Morgan Kaufmann, San Francisco}
}

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  author    = {P. Ravikumar and J. Lafferty},
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}

@inproceedings{RavLar17,
  title     = {Optimization as a Model for Few-Shot Learning},
  author    = {Sachin Ravi and Hugo Larochelle},
  booktitle = iclr,
  year      = {2017}
}

@inproceedings{RavSchPoc17,
  abstract  = {We propose to study equivariance in deep neural networks
               through parameter symmetries. In particular, given a group
               {\$}\backslashmathcal{\{}G{\}}{\$} that acts discretely on
               the input and output of a standard neural network layer
               {\$}\backslashphi{\_}{\{}W{\}}: \backslashRe{\^{}}{\{}M{\}}
               \backslashto \backslashRe{\^{}}{\{}N{\}}{\$}, we show that
               {\$}\backslashphi{\_}{\{}W{\}}{\$} is equivariant with
               respect to {\$}\backslashmathcal{\{}G{\}}{\$}-action iff
               {\$}\backslashmathcal{\{}G{\}}{\$} explains the symmetries
               of the network parameters {\$}W{\$}. Inspired by this
               observation, we then propose two parameter-sharing schemes
               to induce the desirable symmetry on {\$}W{\$}. Our
               procedures for tying the parameters achieve
               {\$}\backslashmathcal{\{}G{\}}{\$}-equivariance and, under
               some conditions on the action of
               {\$}\backslashmathcal{\{}G{\}}{\$}, they guarantee
               sensitivity to all other permutation groups outside {\$}\backslashmathcal{\{}G{\}}{\$}.},
  booktitle = icml,
  author    = {Ravanbakhsh, Siamak and Schneider, Jeff and Poczos,
               Barnabas},
  title     = {{Equivariance Through Parameter-Sharing}},
  year      = {2017}
}

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  oai     = {oai:arXiv.org:0910.0651},
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  subject = {Computer Science - Information Theory; Computer Science -
             Numerical Analysis; Mathematics - Optimization and Control},
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}

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  author  = {Benjamin Recht and Maryam Fazel and Pablo A. Parrilo},
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}

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@inproceedings{Reid_ConvexityProper_2010,
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               via a link function then applying a class probability
               estimation (CPE) loss. If the risk for a composite loss is
               always minimised by predicting the value associated with
               the true class probability the composite loss is proper. We
               provide a novel, explicit and complete characterisation of
               the convexity of any proper composite loss in terms of its
               link and its “weight function” associated with its
               proper CPE loss.},
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               {Statistics}},
  publisher = {PMLR},
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  editor    = {{Yee Whye Teh} and {Mike Titterington}},
  month     = mar,
  year      = {2010},
  pages     = {637--644},
  file      = {Reid, Williamson - Convexity of proper composite binary
               losses
               (2010).pdf:/Users/zcranko/Documents/Library/Conference
               Paper/Reid, Williamson - Convexity of proper composite
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}

@article{Reid_InformationDivergence_2011,
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  volume   = {12},
  issn     = {1532-4435},
  journal  = {Journal of Machine Learning Research},
  author   = {Reid, Mark D. and Williamson, Robert C.},
  month    = jul,
  year     = {2011},
  note     = {tex.acmid: 2021029 tex.issue\_date: 2/1/2011 tex.numpages:
              87 tex.publisher: JMLR.org},
  keywords = {⛔ No DOI found},
  pages    = {731--817},
  file     = {Reid, Williamson - Information, divergence and risk for
              binary experiments
              (2011).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Reid, Williamson - Information, divergence and risk
              for binary experiments (2011).pdf:application/pdf}
}

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@article{ReiWil11,
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  volume    = {12},
  pages     = {731--817},
  publisher = {{MIT} Press}
}

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}

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@article{RicMoo95,
  author    = {Bradley L. Richards and Raymond J. Mooney},
  title     = {Automated Refinement of First-Order Horn-Clause Domain
               Theories},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {19},
  pages     = {95--131},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{RicPraBri06,
  title     = {Beyond PageRank: machine learning for static ranking},
  author    = {M. Richardson and A. Prakash and E. Brill},
  booktitle = {the 15th international conference on World Wide Web,
               {WWW}},
  publisher = {ACM},
  year      = {2006},
  editor    = {L. Carr and D. De Roure and A. Iyengar and C.A. Goble and
               M. Dahlin},
  pages     = {707--715},
  url       = {http://doi.acm.org/10.1145/1135777.1135881}
}

@article{RicSil91,
  author  = {J. A. Rice and B. W. Silverman},
  title   = {Estimating the Mean and Covariance Structure
             Nonparametrically when the Data are Curves},
  journal = {Journal of the Royal Statistical Society, Series B
             (Methodological)},
  year    = {1991},
  volume  = {53},
  pages   = {233--243},
  number  = {1}
}

@article{RicSil91b,
  author  = {J. A. Rice and B. W. Silverman},
  title   = {Estimating the Mean and Covariance Structure
             Nonparametrically when the Data are Curves},
  journal = {Journal of the Royal Statistical Society, Series B
             (Methodological)},
  year    = {1991},
  volume  = {53},
  pages   = {233--243},
  number  = {1}
}

@proceedings{RicSmiWieZeu98,
  title        = alt,
  address      = {Otzenhausen, Germany},
  year         = {1998},
  editor       = {M. M. Richter and C. H. Smith and R. Wiehagen and T.
                  Zeugmann},
  volume       = {1501},
  series       = {Lecture Notes in Artificial Intelligence},
  publisher    = {Springer-Verlag, Berlin},
  month        = oct,
  crossrefonly = {1},
  isbn         = {3-540-65013-X}
}

@inproceedings{RicSmiWieZeu98b,
  author    = {M. M.~Richter and C. H.~Smith and R.~Wiehagen and T.
               Zeugmann},
  title     = {Editors' Introduction},
  booktitle = alt,
  year      = {1998},
  volume    = {1501},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {1--10},
  publisher = {Springer-Verlag}
}

@inproceedings{RicSta00,
  author    = {Corinna Richter and J{\"o}rg Stachowiak},
  title     = {Knowledge Propagation in Model-based Reinforcement
               Learning Tasks},
  booktitle = icml,
  year      = {2000},
  pages     = {791--798},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{RicTak16,
  author  = {Peter Richtarik and Martin Takac},
  title   = {Parallel Coordinate Descent Methods for Big Data
             Optimization},
  journal = {Mathematical Programming},
  year    = {2016},
  volume  = {156},
  number  = {1},
  pages   = {433--484}
}

@inbook{RicZie04,
  chapter   = {Oriented Matroids},
  title     = {Handbook of Discrete and Computational Geometry},
  publisher = {CRC Press},
  year      = {2004},
  author    = {J\"{u}rgen Richter-Gebert and G\"{u}nter M. Ziegler},
  edition   = {2nd Eddition}
}

@inbook{RicZie04b,
  chapter   = {Oriented Matroids},
  title     = {Handbook of Discrete and Computational Geometry},
  publisher = {CRC Press},
  year      = {2004},
  author    = {J\"{u}rgen Richter-Gebert and G\"{u}nter M. Ziegler},
  edition   = {2nd Eddition}
}

@inproceedings{RidMadRic98,
  author    = {G.~Ridgeway and D.~Madigan and T.~Richardson},
  title     = {Boosting Methodology for Regression Problems},
  booktitle = icml,
  year      = {1998},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{RieBra93,
  author    = {Martin Riedmiller and Heinrich Braun},
  title     = {A Direct Adaptive Method for Faster Backpropagation
               Learning: The {RPROP} Algorithm},
  booktitle = {Proc. International Conference on Neural Networks},
  year      = {1993},
  pages     = {586-591},
  address   = {San Francisco, CA},
  publisher = {IEEE, New York}
}

@article{rieley2018big,
  title   = {Big data adds up to opportunities in math careers},
  year    = {2018},
  url     = {https://www.bls.gov/opub/btn/volume-7/big-data-adds-up.htm},
  journal = {Bureau of Labor Statistics}
}

@book{RieNag55,
  title     = {Functional Analysis},
  publisher = {Frederick Ungar Publishing Co.},
  year      = {1955},
  author    = {F.~Riesz and B.~S.~Nagy}
}

@inproceedings{RiePreKuhJoh00,
  title     = {Lexicalized Stochastic Modeling of Constraint-Based
               Grammars using Log-Linear Measures and {EM} Training},
  author    = {Stefan Riezler and Detlef Prescher and Jonas Kuhn and Mark
               Johnson},
  booktitle = acl,
  year      = {2000}
}

@article{RieSeu97,
  author  = {P.~Riegler and H.~S.~Seung},
  title   = {{V}apnik-{C}hervonenkis entropy of the spherical
             perceptron},
  journal = {Physical Review E},
  year    = {1997},
  volume  = {55},
  pages   = {3283--3287}
}

@book{RieWardeRBia97,
  author    = {F. Rieke and D. Warland and R.~R. {de Ruyter Van
               Steveninck} and W. Bialek},
  title     = {Spikes: Exploring the Neural Code},
  publisher = {MIT Press},
  year      = {1997},
  address   = {Cambridge, MA}
}

@inproceedings{Rifaietal11,
  title     = {Contractive auto-encoders: Explicit invariance during
               feature extraction},
  author    = {Rifai, Salah and Vincent, Pascal and Muller, Xavier and
               Glorot, Xavier and Bengio, Yoshua},
  booktitle = icml,
  year      = {2011}
}

@article{RifKla04,
  abstract  = {We consider the problem of multiclass classification. Our
               main thesis is that a simple "one-vs-all" scheme is as
               accurate as any other approach, assuming that the
               underlying binary classifiers are well-tuned regularized
               classifiers such as support vector machines. This thesis is
               interesting in that it disagrees with a large body of
               recent published work on multiclass classification. We
               support our position by means of a critical review of the
               existing literature, a substantial collection of carefully
               controlled experimental work, and theoretical arguments.},
  author    = {R. Rifkin and A. Klautau},
  booktitle = {J. Mach. Learn. Res.},
  journal   = {Journal of Machine Learning Research},
  month     = {January},
  pages     = {101--141},
  title     = {In Defense of One-Vs-All Classification},
  url       = {http://jmlr.csail.mit.edu/papers/v5/rifkin04a.html},
  volume    = {5},
  year      = {2004}
}

@article{RifLip07,
  author  = {Ryan M. Rifkin and Ross A. Lippert},
  title   = {Value Regularization and {F}enchel Duality},
  journal = {Journal of Machine Learning Research},
  year    = 2007,
  volume  = 8,
  pages   = {441--479}
}

@techreport{RifPonVer99,
  author      = {R.~Rifkin and M.~Pontil and A.~Verri},
  title       = {A note on support vector machine degeneracy},
  institution = {Massachusetts Institute of Technology},
  year        = {1999},
  type        = {{AI} Memo},
  number      = {1661}
}

@inproceedings{RigPapMagSca08,
  author    = {Leonardo Rigutini and Tiziano Papini and Marco Maggini and
               Franco Scarselli},
  title     = {SortNet: Learning To Rank By a Neural-Based Sorting
               Algorithm},
  booktitle = {Learning to Rank for Information Retrieval},
  year      = {2008}
}

@book{Rijsbergen79,
  title     = {Information Retrieval},
  publisher = {Butterworths},
  year      = {1979},
  author    = {C.J. van Rijsbergen},
  address   = {London},
  edition   = {2}
}

@article{Rinaldo09,
  author  = {A. Rinaldo},
  title   = {{Properties and refinements of the fused lasso}},
  journal = {The Annals of Statistics},
  year    = {2009},
  volume  = {37},
  pages   = {2922--2952},
  number  = {5B}
}

@article{Ring97,
  author    = {Mark B. Ring},
  title     = {{CHILD}: a first step towards continual learning},
  journal   = {Machine Learning},
  year      = {1997},
  volume    = {28},
  pages     = {77--104},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Ripley94,
  author  = {B.~D.~Ripley},
  title   = {Neural Networks and Related Methods for Classification},
  journal = {Proc.~Royal Soc.~London},
  year    = {1994}
}

@book{Ripley96,
  title     = {Pattern Recognition and Neural Networks},
  publisher = {Cambridge University Press},
  year      = {1996},
  author    = {B.~D.~Ripley},
  address   = {Cambridge}
}

@techreport{Rippa84,
  author      = {S.~Rippa},
  title       = {Interpolation and smoothing of scattered data by radial
                 basis functions},
  institution = {Tel Aviv University},
  year        = {1984},
  type        = {M.Sc.~Thesis}
}

@inproceedings{RisAasLid03,
  title     = {Multi-Tier Architecture for Web Search Engines},
  author    = {K. M. Risvik and Y. Aasheim and M. Lidal},
  publisher = {{IEEE} Computer Society},
  year      = {2003},
  booktitle = {LA-WEB},
  pages     = {132--143}
}

@article{Rissanen78,
  author  = {J.~Rissanen},
  title   = {Modeling by Shortest Data Description},
  journal = {Automatica},
  year    = {1978},
  volume  = {14},
  pages   = {465--471}
}

@article{Rissanen83,
  author  = {J.~Rissanen},
  title   = {A Universal Prior for Integers and Estimation by Minimum
             Description Length},
  journal = {Annals of Statistics},
  year    = {1983},
  volume  = {11},
  pages   = {416--431}
}

@article{Rissanen85,
  author  = {J.~Rissanen},
  title   = {Minimum-description-length principle},
  journal = {Annals of Statistics},
  year    = {1985},
  volume  = {6},
  pages   = {461--464}
}

@article{Rissanen86,
  author  = {J. Rissanen},
  title   = {Stochastic Complexity and Modeling},
  journal = {Ann. of Statist.},
  year    = {1986},
  volume  = {14},
  pages   = {1080--1100},
  number  = {3},
  comment = {Minimum Description Length Principle, and applications.}
}

@techreport{Rissanen86b,
  author      = {J. Rissanen},
  title       = {Stochastic Complexity and Sufficient Statistics},
  institution = {IBM Research Laboratory (San Jose)},
  year        = {1986},
  comment     = {Defines notion of stochastic complexity of a string
                 relative to a class of models. Describes to ways to
                 approximate stochastic complexity.}
}

@book{Rissanen89,
  title     = {Stochastic Complexity in Statistical Inquiry},
  publisher = {World Scientific},
  year      = {1989},
  author    = {J.~Rissanen}
}

@article{Rissanen97,
  author  = {Jorma Rissanen},
  title   = {Stochastic Complexity in Learning},
  journal = {J. Comput. Syst. Sci.},
  year    = {1997},
  volume  = {55},
  pages   = {89--95},
  number  = {1},
  note    = {First appeared in EuroCOLT '95}
}

@inproceedings{RisYia97,
  author    = {Eric Sven Ristad and Peter N. Yianilos},
  title     = {Learning string edit distance},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {287--295},
  publisher = {Morgan Kaufmann}
}

@article{RitKoh89,
  author  = {H.~Ritter and T.~Kohonen},
  title   = {Self-organizing semantic maps},
  journal = {Biological Cybernetics},
  year    = {1989},
  volume  = {61},
  pages   = {241--254}
}

@book{RitMarSch90,
  title     = {Neuronale Netze: Eine Einf{\"{u}}hrung in die
               Neuroinformatik selbstorganisierender Abbildungen},
  publisher = {Addison-Wesley},
  year      = {1990},
  author    = {H.~J.~Ritter and T.~M.~Martinetz and K.~J.~Schulten},
  address   = {Munich, Germany},
  optpages  = {258}
}

@inproceedings{RitSch86,
  author    = {H.~Ritter and K.~Schulten},
  title     = {Topology conserving mappings for learning motor tasks},
  booktitle = {Proc.~{AIP} Conference: Neural networks of computing},
  year      = {1986},
  pages     = {376--380},
  address   = {Snowbird, Utah},
  publisher = {J.~S.~Denker}
}

@article{RitSch86b,
  author  = {H.~Ritter and K.~Schulten},
  title   = {On the stationary state of Kohonen's self-organizing
             sensory mapping},
  journal = {Biological Cybernetics},
  year    = {1986},
  volume  = {54},
  pages   = {99--106}
}

@incollection{RitSch87,
  author    = {H.~Ritter and K.~Schulten},
  title     = {Extending {Kohonen's} self-organizing mapping algorithm to
               learn ballistic movements},
  booktitle = {Neural Computers},
  publisher = {Springer},
  year      = {1987},
  editor    = {R.~Eckmiller and C.~von der Malsburg},
  pages     = {393--406},
  address   = {Berlin}
}

@article{RitSch88,
  author  = {H.~Ritter and K.~Schulten},
  title   = {Convergence properties of {Kohonen}'s topology conserving
             maps: fluctuations, stability, and dimension selection},
  journal = {Biological Cybernetics},
  year    = {1988},
  volume  = {60},
  pages   = {59--71}
}

@inproceedings{Ritter84,
  author    = {K Ritter},
  title     = {On parametric linear and quadratic programming problems},
  booktitle = {the International Congress on Mathematical Programming},
  pages     = {307-335},
  year      = 1984,
  editor    = {Richard W. Cottle and Milton Luiz Kelmanson and Bernhard
               H. Korte},
  address   = {Rio de Janeiro},
  month     = {April}
}

@inproceedings{RivEis90,
  author    = {R.~L.~Rivest and B.~Eisenberg},
  title     = {On the sample complexity of pac-learning using random and
               chosen examples},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1990},
  pages     = {154--162},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@article{Rivest87,
  author    = {Ronald L. Rivest},
  title     = {Learning Decision Lists},
  journal   = {Machine Learning},
  year      = {1987},
  volume    = {2},
  pages     = {229--246},
  publisher = {Kluwer Academic Publishers, Boston}
}

@unpublished{Rivest89,
  author = {R.~Rivest},
  title  = {Lecture Notes in Machine Learning},
  note   = {unpublished manuscript},
  year   = {1989}
}

@incollection{Rivest91,
  author    = {R. L. Rivest},
  title     = {Theory of Learning: What's Hard and What's Easy to Learn},
  booktitle = {Research Directions in Computer Science: An {MIT}
               Perspective},
  publisher = {{MIT} Press},
  year      = {1991},
  chapter   = {11},
  pages     = {217--227}
}

@proceedings{RivHauWar89,
  title     = {the Second Annu. Workshop on Comput. Learning Theory},
  year      = {1989},
  editor    = {R. Rivest and D. Haussler and M. K. Warmuth},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann, Inc.}
}

@article{RivRem88,
  author  = {R. L. Rivest and W. Remmele},
  title   = {Machine Learning: the Human Connection},
  journal = {Siemens Review},
  year    = {1988},
  volume  = {55},
  pages   = {33--38},
  number  = {2/88},
  month   = mar # {/} # apr
}

@incollection{RivRem91,
  author    = {R. L. Rivest and W. Remmele},
  title     = {Machine Learning},
  booktitle = {Applied Computer Science and Software: Turing Theory into
               Practice},
  publisher = {Springer-Verlag},
  year      = {1991},
  pages     = {186--200},
  comment   = {International Symposium, September 30 -- October 1, 1991,
               in Munich}
}

@inproceedings{RivSch87,
  author    = {R. L. Rivest and R. E. Schapire},
  title     = {Diversity-based inference of finite automata},
  booktitle = {Proc. 28th Annu. IEEE Sympos. Found. Comput. Sci.},
  year      = {1987},
  pages     = {78--87},
  publisher = {IEEE Computer Society Press, Los Alamitos, CA}
}

@unpublished{RivSch87b,
  author = {R. Rivest and R. Schapire},
  title  = {Inference of visible simple assignment automata with
            planned experiments},
  note   = {Preprint, MIT Laboratory for Computer Science.},
  year   = {1987}
}

@inproceedings{RivSch87c,
  author    = {R. L. Rivest and R. E. Schapire},
  title     = {A New Approach to Unsupervised Learning in Deterministic
               Environments},
  booktitle = {Proc. of the 4th International Workshop on Machine
               Learning},
  year      = {1987},
  pages     = {364--375},
  address   = {San Mateo, CA},
  month     = jun,
  publisher = {Morgan Kaufmann}
}

@incollection{RivSch90,
  author    = {R. L. Rivest and R. E. Schapire},
  title     = {A New Approach to Unsupervised Learning in Deterministic
               Environments (reprint)},
  booktitle = {Machine Learning, An Artificial Intelligence Approach},
  publisher = {Morgan Kaufmann},
  year      = {1990},
  volume    = {III},
  pages     = {670--684},
  comment   = {Also appeared in the 4th International Workshop on Machine
               Learning, in 1987. Basically a reprint of rs-nauld-87}
}

@article{RivSch93,
  author  = {R. L. Rivest and R. E. Schapire},
  title   = {Inference of Finite Automata using Homing Sequences},
  journal = {Inform. Comput.},
  year    = {1993},
  volume  = {103},
  pages   = {299--347},
  number  = {2},
  month   = apr,
  annote  = {Earlier version in FOCS89}
}

@article{RivShaAdl78,
  author  = {R. L. Rivest and A. Shamir and L. Adleman},
  title   = {A method for obtaining digital signatures and public key
             cryptosytems},
  journal = {Commun. ACM},
  year    = {1978},
  volume  = {21},
  pages   = {120--126},
  number  = {2}
}

@inproceedings{RivSlo88,
  author    = {R. L. Rivest and R. Sloan},
  title     = {Learning complicated concepts reliably and usefully},
  booktitle = {Proc. 1st Annu. Workshop on Comput. Learning Theory},
  year      = {1988},
  pages     = {69--79},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann},
  annote    = {Also in AAAI88}
}

@inproceedings{RivSlo88b,
  author    = {R. L. Rivest and R. Sloan},
  title     = {A New Model for Inductive Inference},
  booktitle = {Proc. of the Second Conference on Theoretical Aspects of
               Reasoning about Knowledge},
  year      = {1988},
  pages     = {13--27},
  month     = mar,
  publisher = {Morgan Kaufmann}
}

@article{RivSlo93,
  author  = {Ronald L. Rivest and Robert H. Sloan},
  title   = {On Choosing between Experimenting and Thinking when
             Learning},
  journal = {Inform. Comput.},
  year    = {1993},
  volume  = {106},
  pages   = {1--25},
  number  = {1}
}

@article{RivWin90,
  author  = {R. L. Rivest and P. Winston},
  title   = {Machine Learning Research at {MIT}},
  journal = {Siemens Review},
  year    = {1990},
  pages   = {12--14}
}

@inbook{RivYin94,
  chapter   = {17},
  pages     = {477--486},
  title     = {Simulation results for a new two-armed bandit heuristic},
  publisher = {{MIT} Press},
  year      = {1994},
  author    = {Ronald L. Rivest and Yiqun Yin},
  note      = {Earlier version in 1990 Conference on Computation Learning
               and Natural Learning at Princeton},
  booktitle = {Computational Learning Theory and Natural Learning Systems
               I Constraints and Prospects}
}

@inproceedings{RivYin95,
  author    = {Ronald L. Rivest and Yiqun L. Yin},
  title     = {Being taught can be faster than asking questions},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {144--151},
  publisher = {ACM Press, New York, NY}
}

@book{Roach70,
  title     = {Green's functions: introductory theory with applications},
  publisher = {Van Nostrand Reinhold},
  year      = {1970},
  author    = {G.~F.~Roach},
  address   = {London}
}

@book{RobCas04,
  author    = {C. Robert and G. Casella},
  title     = {Monte Carlo Statistical Methods},
  publisher = {Springer},
  year      = {2004},
  edition   = {Second}
}

@book{Robert94,
  title     = {The {B}ayesian choice: A decision theoretic motivation},
  publisher = {Springer},
  year      = {1994},
  author    = {Christian P.~Robert},
  address   = {New York}
}

@book{Roberts79,
  title     = {Measurement Theory},
  publisher = {Addison-Wesley},
  year      = {1979},
  author    = {F.~Roberts},
  address   = {Massachusetts}
}

@article{Robertson77,
  author  = {S.~E.~Robertson},
  title   = {The Probability Ranking Principle in {IR}},
  journal = {Journal of Documentation},
  year    = {1977},
  volume  = {33},
  pages   = {294--304},
  number  = {4}
}

@inproceedings{RobHul00,
  author    = {S. Robertson and D. A. Hull},
  title     = {The {TREC}-9 Filtering Track Final Report},
  booktitle = {the 9th {T}ext {RE}trieval {C}onference},
  pages     = {25--40},
  year      = {2000}
}

@inbook{Robinson82,
  author    = {Robinson, Stephen M.},
  editor    = {Guignard, Monique},
  title     = {Generalized equations and their solutions, part II:
               Applications to nonlinear programming},
  booktitle = {Optimality and Stability in Mathematical Programming},
  year      = {1982},
  publisher = {Springer Berlin Heidelberg}
}

@article{Robinson99,
  author  = {Stephen M. Robinson},
  title   = {Linear Convergence of epsilon-subgradient Descent Methods
             for a Class of Convex Functions},
  journal = {Mathematical Programming},
  year    = {1999},
  volume  = {86},
  number  = {1},
  pages   = {41--50}
}

@article{RobKasIra02,
  author  = {Eric S. Roberts and Marina Kassianidou and Lilly Irani},
  title   = {Encouraging women in computer science},
  journal = {ACM SIGCSE Bulletin},
  year    = {2002},
  volume  = {34},
  number  = {2},
  pages   = {84--88}
}

@inproceedings{RobKon97,
  author    = {M. Robnik-{\v S}ikonja and I. Kononenko},
  title     = {An adaptation of {R}elief for attribute estimation in
               regression},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {296--304},
  publisher = {Morgan Kaufmann}
}

@inproceedings{RobKon99,
  author    = {M. Robnik-{\v S}ikonja and I. Kononenko},
  title     = {Attribute dependencies, understandability and split
               selection in tree based models},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {344--353},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{RobMon51,
  author  = {Herbert E. Robbins and Sutton Monro},
  title   = {A Stochastic Approximation Method},
  journal = {Annals of Mathematical Statistics},
  year    = {1951},
  volume  = {22},
  pages   = {400-407}
}

@techreport{RobNirFal88,
  author      = {A.~J.~Robinson and M.~Niranjan and F.~Fallside},
  title       = {Generalizing the nodes of the error propagation network},
  institution = {Cambridge University},
  year        = {1988},
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  month  = jan,
  year   = {2000}
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@inproceedings{Rosset_BoostingDensity_2003,
  title     = {Boosting density estimation},
  booktitle = {Advances in neural information processing systems 15},
  publisher = {MIT Press},
  author    = {Rosset, Saharon and Segal, Eran},
  editor    = {Becker, S. and Thrun, S. and Obermayer, K.},
  year      = {2003},
  keywords  = {⛔ No DOI found},
  pages     = {657--664},
  file      = {Full
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@inproceedings{Rossmanith99,
  author    = {Peter Rossmanith},
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  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {132--144},
  publisher = {Springer-Verlag}
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  year    = {1998},
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  year    = {2001},
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  number  = {2}
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@inproceedings{RosZenIttDil98,
  author    = {Rolf Rossius and Gerard Zenker and Andreas Ittner and
               Werner Dilger},
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               with Fractional Degree in Support Vector Learning},
  booktitle = {the European Conference on Machine Learning},
  year      = {1998},
  publisher = {Springer}
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@article{RosZeu01,
  author    = {Peter Rossmanith and T. Zeugmann},
  title     = {Stochastic Finite Learning of the Pattern Languages},
  journal   = {Machine Learning},
  year      = {2001},
  volume    = {44},
  pages     = {67--91},
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  publisher = {Kluwer Academic Publishers, Boston}
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@inproceedings{RosZeu98,
  author    = {Peter Rossmanith and T. Zeugmann},
  title     = {Learning $k$-Variable Pattern Languages Efficiently
               Stochastically Finite on Average from Positive Data},
  booktitle = {Proc. 4th International Colloquium on Grammatical
               Inference - ICGI '98},
  year      = {1998},
  volume    = {1433},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {13--24},
  publisher = {Springer-Verlag}
}

@article{RosZhu07,
  author  = {Saharon Rosset and Ji Zhu},
  title   = {Piecewise Linear Regularized Solution Paths},
  journal = {Annals of Statistics},
  year    = {2007},
  volume  = {35},
  number  = {3},
  pages   = {1012--1033}
}

@inproceedings{RosZhuZouHas04,
  author    = {S. Rosset and J. Zhu and H. Zou and T. Hastie},
  title     = {A method for inferring label sampling mechanisms in
               semi-supervised learning},
  booktitle = {Advances in Neural Information Processing Systems 17},
  year      = {2004}
}

@article{Roth96,
  author  = {Dan Roth},
  title   = {On the Hardness of Approximate Reasoning},
  journal = {Artificial Intelligence},
  year    = {1996},
  volume  = {82},
  issue   = {1-2},
  pages   = {273--302}
}

@unpublished{RotKolBlaBro07,
  author = {Carsten Rother and Vladimir Kolmogorov and Andrew Blake
            and Matthew Brown},
  title  = {Grab{C}ut Ground Truth Database},
  note   = {{\small\url{http://research.microsoft.com/en-us/um/cambridge/
            projects/visionimagevideoediting/segmentation/grabcut.htm}}},
  year   = 2007
}

@inproceedings{RotKolLemSzu07,
  author    = {Carsten Rother and Vladimir Kolmogorov and Victor
               Lempitsky and Martin Szummer},
  title     = {Optimizing Binary {MRF}s via Extended Roof Duality},
  address   = {Minneapolis, MN},
  booktitle = {IEEE Conference on Computer Vision and Pattern
               Recognition},
  month     = jun,
  year      = 2007
}

@inproceedings{RotKumKolBla05,
  author    = {Carsten Rother and Sanjiv Kumar and Vladimir Kolmogorov
               and Andrew Blake},
  title     = {Digital Tapestry},
  address   = {Minneapolis, MN},
  booktitle = cvpr,
  year      = 2005
}

@inproceedings{RotLauBuhMul03,
  author    = {V. Roth and J. Laub and J. M. Buhmann and
               K.-R.~M{\"u}ller},
  title     = {Going Metric: Denoising Pairwise Data},
  booktitle = {Advances in Neural Information Processing Systems 15},
  year      = {2003},
  editor    = {S. Becker and S. Thrun and K. Obermayer},
  pages     = {817--824},
  publisher = {Cambridge, MA, MIT}
}

@unpublished{RotLucNowHof18,
  author = {Kevin Roth and Aurelien Lucchi and Sebastian Nowozin and
            Thomas Hofmann},
  title  = {Adversarially Robust Training through Structured Gradient
            Regularization},
  note   = {arXiv:1805.08736},
  year   = {2018}
}

@inproceedings{RotSte00,
  author    = {V.~Roth and V.~Steinhage},
  title     = {Nonlinear Discriminant Analysis Using Kernel Functions},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A.~Solla and T.~K.~Leen and K.-R.~M{\"u}ller},
  pages     = {568--574},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{RotZel00,
  author    = {D. Roth and D. Zelenko},
  title     = {Toward a Theory of Learning Coherent Concepts},
  booktitle = {the AAAI},
  year      = {2000}
}

@article{RouCraSaw06,
  title   = {Kernel-based learning of hierarchical multilabel
             classification models},
  author  = {Rousu, Juho and Saunders, Craig and Szedmak, Sandor and
             Shawe-Taylor, John},
  journal = jmlr,
  volume  = {7},
  number  = {Jul},
  pages   = {1601--1626},
  year    = {2006}
}

@book{RouLer87,
  author    = {P.~J.\ Rousseeuw and A.~M.\ Leroy},
  title     = {Robust Regression and Outlier Detection},
  publisher = {John Wiley and Sons},
  year      = 1987
}

@article{RouSunSzeSha06,
  author  = {Juho Rousu and Craig Sunders and Sandor Szedmak and John
             Shawe-Taylor},
  title   = {Kernel-Based Learning of Hierarchical Multilabel
             Classification Methods},
  year    = 2006,
  journal = {Journal of Machine Learning Research},
  volume  = 7,
  pages   = {1601--1626}
}

@article{Rouveirol94,
  author    = {C{\'{e}}line Rouveirol},
  title     = {Flattening and Saturation: Two Representation Changes for
               Generalization},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {14},
  pages     = {219--232},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Roverso96,
  author    = {D. Roverso},
  title     = {Analogy access by mapping spreading and abstraction in
               large, multifunctional knowledge bases},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {418--426},
  publisher = {Morgan Kaufmann}
}

@techreport{RowBalKan95,
  author      = {H.~Rowley and S.~Baluja and T.~Kanade},
  title       = {Human {F}ace {D}etection in {V}isual {S}cenes},
  institution = {CMU CS},
  year        = {1995},
  type        = {Technical Report},
  number      = {95-158},
  month       = jul
}

@article{RowBalKan98,
  author  = {H.~Rowley and S.~Baluja and T.~Kanade},
  title   = {Neural network-based face detection},
  journal = {IEEE Transactions on Pattern Analysis and Machine
             Intelligence},
  year    = {1998},
  volume  = {20},
  pages   = {23--38}
}

@inproceedings{Roweis98,
  author    = {S.~Roweis},
  title     = {{EM} Algorithms for {PCA} and {SPCA}},
  booktitle = {Advances in Neural Information Processing Systems 10},
  year      = {1998},
  editor    = {M.~I.~Jordan and M.~J.~Kearns and S.~A.~Solla},
  pages     = {626--632},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@techreport{RowGha97,
  author      = {S.~Roweis and Z.~Ghahramani},
  title       = {A Unifying Review of Linear {Gaussian} Models},
  institution = {California Institute of Technology},
  year        = {1997}
}

@article{RowGha99,
  author  = {S.~Roweis and Z.~Ghahramani},
  title   = {A Unifying Review of Linear {Gaussian} Models},
  journal = {Neural Computation},
  year    = {1999},
  volume  = {11},
  number  = {2}
}

@article{RowSau00,
  author  = {S.~Roweis and L.~K.~Saul},
  title   = {Nonlinear dimensionality reduction by locally linear
             embedding},
  journal = {Science},
  year    = {2000},
  volume  = {290},
  pages   = {2323--2326},
  month   = {December}
}

@book{Royden63,
  title     = {Real Analysis},
  publisher = {Macmillan},
  year      = {1963},
  author    = {H. Royden},
  address   = {New York, NY}
}

@article{Royer86,
  author  = {J. Royer},
  title   = {Inductive Inference of approximations},
  journal = {Inform. Control},
  year    = {1986},
  volume  = {70},
  pages   = {156--178}
}

@article{RoyGovMir95,
  author  = {A.~Roy and S.~Govil and R.~Miranda},
  title   = {An algorithm to generate radial basis function
             ({RBF})-like nets for classification problems},
  journal = {Neural Networks},
  year    = {1995},
  volume  = {8},
  pages   = {179--202},
  number  = {2}
}

@article{RoyKimMuk93,
  author  = {A.~Roy and L.~S.~Kim and S.~Mukhopadhyay},
  title   = {A Polynomial Time Algorithm for the Construction and
             Training of a Class of Multilayer Perceptrons},
  journal = {Neural Networks},
  year    = {1993},
  volume  = {6},
  pages   = {535--545}
}

@inproceedings{RoyLanWer09,
  author    = {S. Roy and T. Lane and M. {Werner-Washburne}},
  title     = {Learning Structurally Consistent Undirected Probabilistic
               Graphical Models},
  booktitle = {the 26th Annual International Conference on Machine
               Learning},
  year      = {2009},
  pages     = {905--912},
  publisher = {ACM},
  address   = {New York, NY, USA}
}

@article{RoyMuk97,
  author  = {A.~Roy and S.~Mukhopadhyay},
  title   = {Iterative Generation of Higher-Order Nets in Polynomial
             Time Using Linear Programming},
  journal = {IEEE Transactions on Neural Networks},
  year    = {1997},
  volume  = {8},
  pages   = {402--412},
  number  = {2}
}

@inproceedings{RoySiuOrlKai91,
  author    = {V. Roychowdhury and K. Siu and A. Orlitsky and T.
               Kailath},
  title     = {A geometric approach to threshold circuit complexity},
  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {97--111},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@book{Rozanov82,
  title     = {Markov random fields},
  publisher = {Springer},
  year      = {1982},
  author    = {I.~A.~Rozanov},
  address   = {New York}
}

@article{RuaVenHaoHiretal05,
  author  = {J. F. Rual and K. Venkatesan and T. Hao and T.
             Hirozane-Kishikawa and A. Dricot and N. Li and others},
  title   = {Towards a proteome-scale map of the human protein-protein
             interaction network},
  journal = {Nature},
  year    = {2005},
  volume  = {437},
  pages   = {1173-1178},
  number  = {7062},
  month   = {Oct}
}

@book{Rubinov_AbstractConvexity_2011,
  address   = {New York, NY, USA},
  title     = {Abstract convexity and global optimization},
  isbn      = {978-1-4419-4831-1},
  language  = {English},
  publisher = {Springer},
  author    = {Rubinov, Aleksandr Moiseevich},
  year      = {2011},
  note      = {OCLC: 752482751},
  file      = {Rubinov - Abstract convexity and global optimization
               (2011).pdf:/Users/zcranko/Documents/Library/Book/Rubinov -
               Abstract convexity and global optimization
               (2011).pdf:application/pdf}
}

@article{Rubinov_AntihomogeneousConjugacy_1995,
  title    = {Antihomogeneous conjugacy operators in convex analysis},
  volume   = {2},
  issn     = {0944-6532},
  number   = {1-2},
  journal  = {Journal of Convex Analysis},
  author   = {Rubinov, Aleksandr Moiseevich},
  year     = {1995},
  note     = {tex.fjournal: Journal of Convex Analysis tex.mrclass:
              49N15 (06A23) tex.mrnumber: 1363376 tex.mrreviewer:
              Juan-Enrique Martínez-Legaz},
  keywords = {⛔ No DOI found},
  pages    = {291--307},
  file     = {Rubinov - Antihomogeneous conjugacy operators in convex
              analysis
              (1995).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Rubinov - Antihomogeneous conjugacy operators in
              convex analysis (1995).pdf:application/pdf}
}

@incollection{Rubinov_RadiantSets_2000,
  address   = {Boston, MA, USA},
  title     = {Radiant {Sets} and {Their} {Gauges}},
  isbn      = {978-1-4757-3137-8},
  abstract  = {Links between radiant and co-radiant sets and their
               Minkowski gauges are studied in this paper. We describe
               radiant sets with Lipschitz continuous Minkowski gauges and
               give a new result related to the separability of two
               star-shaped sets by a finite collection of linear
               functions. We also discuss duality of radiant sets in the
               framework of abstract convexity.},
  booktitle = {Quasidifferentiability and {Related} {Topics}},
  publisher = {Springer},
  author    = {Rubinov, Aleksandr Moiseevich},
  editor    = {Demyanov, Vladimir and Rubinov, Alexander},
  year      = {2000},
  doi       = {10.1007/978-1-4757-3137-8_10},
  pages     = {235--261},
  file      = {Rubinov - Radiant Sets and Their Gauges
               (2000).pdf:/Users/zcranko/Documents/Library/Book
               Section/Rubinov - Radiant Sets and Their Gauges
               (2000).pdf:application/pdf}
}

@incollection{Rubinov_SpaceStarshaped_1986,
  address   = {Berlin, Germany},
  title     = {The space of star-shaped sets and its applications in
               nonsmooth optimization},
  isbn      = {978-3-642-00929-7},
  abstract  = {The study of quasidifferentiable functions is based on the
               properties of the space of convex sets. One very important
               concept in convex analysis is that of the gauge of a set.
               However, the definition of a gauge does not require
               convexity, and therefore the notion of a gauge can be
               extended beyond convex sets to a much wider class of sets.
               In this paper the authors develop a theory of gauge
               functions and study some properties of star-shaped sets.
               The results are then used to study nonsmooth extremal
               problems (of which problems involving quasidifferentiable
               functions represent a special class).},
  booktitle = {Quasidifferential {Calculus}},
  publisher = {Springer},
  author    = {Rubinov, Aleksandr Moiseevich and Yagubov, A. A.},
  editor    = {Demyanov, V. F. and Dixon, L. C. W.},
  year      = {1986},
  doi       = {10.1007/BFb0121146},
  pages     = {176--202},
  file      = {Rubinov, Yagubov - The space of star-shaped sets and its
               applications in nonsmooth optimization
               (1986).pdf:/Users/zcranko/Documents/Library/Book
               Section/Rubinov, Yagubov - The space of star-shaped sets
               and its applications in nonsmooth optimization
               (1986).pdf:application/pdf}
}

@article{Rubinov_SublinearFunctionals_1970,
  title    = {Sublinear functionals defined on a cone},
  volume   = {11},
  issn     = {0037-4466, 1573-9260},
  doi      = {10.1007/bf00967306},
  language = {en},
  number   = {2},
  journal  = {Siberian Mathematical Journal},
  author   = {Rubinov, A. M.},
  year     = {1970},
  pages    = {327--336},
  file     = {Rubinov - Sublinear functionals defined on a cone
              (1970).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Rubinov - Sublinear functionals defined on a cone
              (1970).pdf:application/pdf}
}

@article{Rubinov_SublinearOperators_1977,
  title    = {Sublinear operators and their applications},
  volume   = {32},
  issn     = {0036-0279, 1468-4829},
  doi      = {10.1070/rm1977v032n04abeh001640},
  language = {en},
  number   = {4},
  journal  = {Russian Mathematical Surveys},
  author   = {Rubinov, A M},
  month    = aug,
  year     = {1977},
  pages    = {115--175},
  file     = {Rubinov - Sublinear operators and their applications
              (1977).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Rubinov - Sublinear operators and their
              applications (1977).pdf:application/pdf}
}

@article{RubTomGui00,
  author    = {Y. Rubner and C. Tomasi and L.J. Guibas},
  title     = {The Earth Mover's Distance as a Metric for Image
               Retrieval},
  journal   = {Int. J. Comput. Vision},
  volume    = {40},
  number    = {2},
  year      = {2000},
  pages     = {99--121},
  doi       = {http://dx.doi.org/10.1023/A:1026543900054},
  publisher = {Kluwer Academic Publishers},
  address   = {Hingham, MA, USA}
}

@article{RudBia94,
  author  = {D.L. Ruderman and W. Bialek},
  title   = {Statistics of natural images: Scaling in the woods},
  journal = {Phys.\ Rev.\ Letters},
  year    = {1994},
  volume  = {73},
  pages   = {814-817}
}

@article{RudDauSch04,
  author  = {Cynthia Rudin and Ingrid Daubechies and Robert E.
             Schapire},
  title   = {The Dynamics of AdaBoost: Cyclic Behavior and Convergence
             of Margins},
  journal = {Journal of Machine Learning Research},
  volume  = {5},
  pages   = {1557-1595},
  year    = {2004}
}

@inproceedings{Rudich85,
  author    = {S. Rudich},
  title     = {Inferring the structure of a {M}arkov chain from its
               output},
  booktitle = {Proc. 27th Annu. IEEE Sympos. Found. Comput. Sci.},
  year      = {1985},
  pages     = {321--326},
  month     = oct,
  comment   = {No journal version, as of 3/5/91. (Unlikely to be
               one...)}
}

@inproceedings{Rudin06,
  author    = {C. Rudin},
  title     = {Ranking with a {P}-Norm push},
  booktitle = {the 19th Conference on Learning Theory (COLT)},
  year      = {2006},
  series    = {Lecture Notes in Artificial Intelligence},
  volume    = {4005},
  publisher = {Springer, Berlin},
  pages     = {589--604}
}

@inproceedings{Rudin06b,
  author    = {C. Rudin},
  title     = {Ranking with a {P}-Norm push},
  booktitle = {Conference on Learning Theory (COLT)},
  year      = {2006}
}

@book{Rudin62,
  title     = {Fourier analysis on groups},
  publisher = {Interscience Publishers},
  year      = {1962},
  author    = {W. Rudin},
  address   = {New York}
}

@book{Rudin73,
  title     = {Functional Analysis},
  publisher = {McGraw-Hill},
  year      = {1973},
  author    = {W.~Rudin},
  address   = {New York}
}

@book{Rudin_FunctionalAnalysis_1991,
  address   = {Boston, MA, USA},
  edition   = {2},
  series    = {International series in pure and applied mathematics},
  title     = {Functional analysis},
  isbn      = {978-0-07-054236-5},
  language  = {en},
  publisher = {McGraw-Hill},
  author    = {Rudin, Walter},
  year      = {1991},
  note      = {OCLC: 551886599},
  file      = {Rudin - Functional analysis
               (1991).pdf:/Users/zcranko/Documents/Library/Book/Rudin -
               Functional analysis (1991).pdf:application/pdf}
}

@article{RudWatLes95,
  author  = {Stephan M.~Rudolfer and Peter C.~Watson and Emmanuel
             Lesaffre},
  title   = {Are Ordinal Models Useful for Classification? A Revised
             Analysis},
  journal = {Journal of Statistical Computation},
  year    = {1995},
  volume  = {52},
  pages   = {105--132}
}

@article{Ruger98,
  author  = {S. M. R{\"u}ger},
  title   = {A Class of Asymptotically Stable Algorithms for
             Learning-Rate Adaptation},
  journal = {Algorithmica},
  year    = {1998},
  volume  = {22},
  pages   = {198--210},
  number  = {1/2}
}

@book{Rugh81,
  title     = {Nonlinear system theory: the Volterra/Wiener approach},
  publisher = {The Johns Hopkins University Press},
  year      = {1981},
  author    = {W.~J.~Rugh},
  address   = {Baltimore}
}

@inproceedings{RuiEspGar98,
  author    = {J. Ruiz and S. Espana and P. Garci{\'a}},
  title     = {Locally threshold testable languages in strict sense:
               application to the inference problem},
  booktitle = {Proc. 4th International Colloquium on Grammatical
               Inference - ICGI '98},
  year      = {1998},
  volume    = {1433},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {150--161},
  publisher = {Springer-Verlag}
}

@inproceedings{RuiGar96,
  author    = {Jos{\'e} Ruiz and Pedro Garcia},
  title     = {Learning $k$-piecewise testable languages from positive
               data},
  booktitle = {Grammatical Inference: {L}earning Syntax from Sentences,
               3rd International Colloquium, ICGI-96, Montpellier, France,
               September 25--27, 1996, Proceedings},
  year      = {1996},
  volume    = {1147},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {203--210},
  publisher = {Springer, Berlin}
}

@inproceedings{RuiLop98,
  author    = {A.~Ruiz and P.~E.~{L\'opez de Teruel}},
  title     = {Random falsifiability and Support Vector Machines},
  booktitle = {Learning 98},
  year      = {1998},
  address   = {Getafe}
}

@article{RuiRuiSchAsgetal05,
  author  = {F. van Ruissen and J. M. Ruijter and G. J. Schaaf and L.
             Asgharnegad and D. A. Zwijnenburg and M. Kool and F. Baas},
  title   = {Evaluation of the similarity of gene expression data
             estimated with SAGE and Affymetrix GeneChips},
  journal = {BMC Genomics},
  year    = {2005},
  volume  = {6},
  pages   = {91},
  month   = {Jun}
}

@techreport{Ruiz97,
  author      = {A.~Ruiz},
  title       = {Practical prediction of generalization by the method of
                 random falsifiability},
  institution = {Department of Computer Science},
  year        = {1997},
  number      = {DIS-9-97},
  address     = {University of Murcia, Spain}
}

@article{Rujan93,
  author  = {P.~Ruj\'an},
  title   = {A fast method for calculating the perceptron with maximal
             stability},
  journal = {Journal de Physique I France},
  year    = {1993},
  volume  = {3},
  pages   = {277--290}
}

@article{Rujan96,
  author  = {P. Ruj\'an},
  title   = {Playing Billiard in Version Space},
  journal = {Neural Computation},
  year    = {1996},
  volume  = {9},
  pages   = {197--238}
}

@article{Rujan97,
  author  = {P\'al Ruj\'an},
  title   = {Playing Billiards in Version Space},
  journal = {Neural Computation},
  year    = {1997},
  volume  = {9},
  pages   = {99--122}
}

@inproceedings{RujMar00,
  author    = {P.~{Ruj\'an} and M.~Marchand},
  title     = {Computing the {Bayes} kernel classifier},
  booktitle = {Advances in Large Margin Classifiers},
  year      = {2000},
  editor    = {A. J. Smola and P.~L.~Bartlett and B.~Sch{\"o}lkopf and
               D.~Schuurmans},
  pages     = {329--347},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{RujMar89,
  author  = {P.~Ruj\'an and M.~Marchand},
  title   = {Learning by Minimizing Resources in Neural Networks},
  journal = {Complex Systems},
  year    = {1989},
  volume  = {3},
  pages   = {229--242}
}

@incollection{RumHinMcC86,
  author    = {D. E. Rumelhart and G. E. Hinton and J. L. McClelland},
  title     = {A General Framework for Parallel Distributed Processing},
  booktitle = {Parallel Distributed Processing (Volume I: Foundations)},
  publisher = {{MIT} Press},
  year      = {1986},
  chapter   = {2},
  pages     = {45--76},
  comment   = {Overview of various models}
}

@book{RumHinWil86,
  title     = {Parallel Distributed Processing},
  publisher = {{MIT} Press},
  year      = {1986},
  author    = {D.~E.~Rumelhart and G.~E.~Hinton and R.~J.~Williams},
  address   = {Cambridge, MA}
}

@article{RumHinWil86b,
  author  = {D.~E.~Rumelhart and G.~E.~Hinton and R.~J.~Williams},
  title   = {Learning Representations by Back-propagating Errors},
  journal = {Nature},
  year    = {1986},
  volume  = {323},
  pages   = {533--536},
  number  = {9},
  month   = {October}
}

@incollection{RumHinWil86c,
  author    = {D.~E.~Rumelhart and G.~E.~Hinton and R.~J.~Williams},
  title     = {Learning Internal Representations by Error Propagation},
  booktitle = {Parallel Distributed Processing},
  publisher = {{MIT} Press},
  year      = {1986},
  chapter   = {8},
  pages     = {318--362},
  address   = {Cambridge, MA}
}

@book{RumMcC86,
  title     = {Parallel Distributed Processing (Volume {I}:
               Foundations)},
  publisher = {{MIT} Press},
  year      = {1986},
  editor    = {D. E. Rumelhart and J. L. McClelland},
  comment   = {Overview of various models},
  pages     = {45--76}
}

@book{RumMcCPDP-Res-Gro86,
  title     = {Parallel Distributed Processing: Explorations in the
               Microstructure of Cognition: {F}oundations},
  publisher = {{MIT} Press},
  year      = {1986},
  author    = {D.~E.\ Rumelhart and J.~L.\ McClelland and the PDP
               Research Group},
  volume    = {1},
  address   = {Cambridge, MA}
}

@article{RumZip85,
  author  = {D. E. Rumelhart and D. Zipser},
  title   = {Feature Discovery by Competitive Learning},
  journal = {Cognitive Science},
  year    = {1985},
  volume  = {9},
  pages   = {75--112},
  comment = {Historical survey of perceptrons etc.; competitive
             learning in a layered network with inhibitory clusters}
}

@book{RupCar03,
  title     = {Semiparametric Regression},
  publisher = {Wiley},
  year      = {2003},
  author    = {D. Ruppert and R.J. Carroll}
}

@article{Ruschendorf_MongeKantorovichTransportation_2007,
  title    = {Monge-{Kantorovich} transportation problem and optimal
              couplings},
  volume   = {109},
  issn     = {0012-0456},
  number   = {3},
  journal  = {Jahresbericht der Deutschen Mathematiker-Vereinigung},
  author   = {Rüschendorf, Ludger},
  year     = {2007},
  note     = {tex.fjournal: Jahresbericht der Deutschen
              Mathematiker-Vereinigung tex.mrclass: 60B11 (49Q20 60-03
              60E15 65K10) tex.mrnumber: 2356041 tex.mrreviewer: Luigi De
              Pascale},
  keywords = {⛔ No DOI found},
  pages    = {113--137},
  file     = {Rüschendorf - Monge-Kantorovich transportation problem
              and optimal couplings
              (2007).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Rüschendorf - Monge-Kantorovich transportation
              problem and optimal couplings (2007).pdf:application/pdf}
}

@article{Ruscio00,
  author  = {D. di Ruscio},
  title   = {A Weighted View on the Partial Least Squares Algorithm},
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  year    = {2000},
  volume  = {36},
  pages   = {831--850}
}

@book{RusNor95,
  title     = {Artificial Intelligence: a Modern Approach},
  publisher = {Pearson},
  year      = {1995},
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}

@article{Russakovskyetal15,
  author  = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan
             Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang
             and Andrej Karpathy and Aditya Khosla and Michael Bernstein
             and Alexander C. Berg and Li Fei-Fei},
  title   = {{ImageNet} Large Scale Visual Recognition Challenge},
  journal = ijcv,
  volume  = {115},
  number  = 3,
  pages   = {211--252},
  year    = {2015}
}

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  author    = {Stuart Russell},
  title     = {Learning agents for uncertain environments},
  booktitle = {Proc. 11th Annu. Conf. on Comput. Learning Theory},
  year      = {1998},
  pages     = {101--103},
  publisher = {ACM Press, New York, NY}
}

@book{Rustagi76,
  title     = {Variational Methods in Statistics},
  publisher = {Academic Press},
  year      = {1976},
  author    = {J. Rustagi},
  address   = {New York}
}

@inproceedings{RyaHut06,
  author    = {Daniil Ryabko and M. Hutter},
  title     = {Asymptotic Learnability of Reinforcement Problems with
               Arbitrary Dependence},
  booktitle = {Proc. 17th International Conf. on Algorithmic Learning
               Theory ({ALT'06})},
  address   = {Barcelona},
  series    = {LNAI},
  volume    = {4264},
  editor    = {Jose L. Balcázar and Phil Long and Frank Stephan},
  publisher = {Springer, Berlin},
  pages     = {334--347},
  month     = oct,
  year      = {2006},
  pdf       = {http://www.hutter1.net/ai/actopt.pdf},
  ps        = {http://www.hutter1.net/ai/actopt.ps},
  issn      = {0302-9743},
  isbn      = {3-540-46649-5},
  doi       = {10.1007/11894841_27}
}

@inproceedings{RyaHut07,
  author    = {Daniil Ryabko and Marcus Hutter},
  title     = {On Sequence Prediction for Arbitrary Measures},
  booktitle = {Proc. IEEE International Symposium on Information Theory
               ({ISIT'07})},
  pages     = {2346--2350},
  editor    = {A. Goldsmith and M. Medard and A. Shokrollahi and R.
               Zamir},
  publisher = {IEEE},
  address   = {Nice, France},
  month     = jun,
  year      = {2007},
  bibtex    = {http://www.hutter1.net/official/bib.htm#pquest},
  url       = {http://arxiv.org/abs/cs.LG/0606077},
  ftp       = {http://www.idsia.ch/idsiareport/IDSIA-13-06.pdf},
  pdf       = {http://www.hutter1.net/ai/pquest.pdf},
  ps        = {http://www.hutter1.net/ai/pquest.ps},
  latex     = {http://www.hutter1.net/ai/pquest.tex},
  slides    = {http://www.hutter1.net/ai/spquest.pdf},
  project   = {http://www.hutter1.net/official/projects.htm#bayes},
  doi       = {??},
  isbn      = {1-4244-1429-6},
  keywords  = {sequence prediction, local absolute continuity,
               non-stationary measures, average/expected criteria,
               absolute/KL divergence, mixtures of measures.},
  abstract  = {Suppose we are given two probability measures on the set
               of one-way infinite finite-alphabet sequences. Consider the
               question when one of the measures predicts the other, that
               is, when conditional probabilities converge (in a certain
               sense), if one of the measures is chosen to generate the
               sequence. This question may be considered a refinement of
               the problem of sequence prediction in its most general
               formulation: for a given class of probability measures,
               does there exist a measure which predicts all of the
               measures in the class? To address this problem, we find
               some conditions on local absolute continuity which are
               sufficient for prediction and generalize several different
               notions that are known to be sufficient for prediction. We
               also formulate some open questions to outline a direction
               for finding the conditions on classes of measures for which
               prediction is possible.},
  support   = {SNF grant 200020-107616}
}

@book{Ryan_IntroductionTensor_2002,
  address   = {London},
  series    = {Springer monographs in mathematics},
  title     = {Introduction to tensor products of {Banach} spaces},
  isbn      = {978-1-85233-437-6},
  language  = {en},
  publisher = {Springer},
  author    = {Ryan, Raymond A.},
  year      = {2002},
  file      = {Ryan - Introduction to tensor products of Banach spaces
               (2002).pdf:/Users/zcranko/Documents/Library/Book/Ryan -
               Introduction to tensor products of Banach spaces
               (2002).pdf:application/pdf}
}

@inproceedings{RyaPen98,
  author    = {Malcolm R. K. Ryan and Mark D. Pendrith},
  title     = {{RL-TOP}s: an architecture for modularity and re-use in
               reinforcement learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {481--487},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{RyaRei00,
  author    = {Malcolm Ryan and Mark Reid},
  title     = {Learning to Fly: {A}n Application of Hierarchical
               Reinforcement Learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {807--814},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{Rychtar_GateauxDifferentiability_2005,
  title    = {On {Gâteaux} differentiability of convex functions in
              {WCG} spaces},
  doi      = {10.4153/cmb-2005-042-7},
  abstract = {It is shown, using the Borwein–Preiss variational
              principle that for every continuous convex function f on a
              weakly compactly generated space X, every x0 ∈ X and
              every weakly compact convex symmetric set K such that span
              K = X, there is a point of Gaˆteaux differentiability of f
              in x0 + K. This extends a Klee’s result for separable
              spaces.},
  language = {en},
  author   = {Rychtář̩, Jan},
  year     = {2005},
  pages    = {5},
  file     = {Rychtář̩ - On Gâteaux differentiability of convex
              functions in WCG spaces
              (2005).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Rychtář̩ - On Gâteaux differentiability of
              convex functions in WCG spaces (2005).pdf:application/pdf}
}

@inproceedings{RycShaGle00,
  author    = {Matthias Rychetsky and John Shawe-Taylor and Manfred
               Glesner},
  title     = {Direct {B}ayes Point Machines},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000}
}

@inproceedings{S-W-ShaGre75,
  author       = {S. W. {Shaw, D} and C. Green},
  title        = {Inferring {LISP} programs from example problems},
  booktitle    = {Proc. 4th IJCAI},
  year         = {1975},
  pages        = {260--267},
  organization = {IJCAI}
}

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  author    = {Virginia R.~de Sa},
  title     = {Learning Classification with Unlabeled Data},
  booktitle = {Advances in Neural Information Processing Systems 6},
  year      = {1994},
  editor    = {J.~D.~Cowan and G.~Tesauro and J.~Alspector},
  pages     = {112--119},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@book{Saad99,
  title     = {On-Line Learning in Neural Networks},
  publisher = {Cambridge University Press},
  year      = {1999},
  editor    = {David Saad},
  series    = {Publications of the Newton Institute},
  booktitle = {On-Line Learning in Neural Networks}
}

@book{Saaty81,
  title     = {Modern nonlinear equations},
  publisher = {Dover},
  year      = {1981},
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  address   = {New York}
}

@inproceedings{SadHar95,
  author    = {Ken Sadohara and Makoto Haraguchi},
  title     = {Analogical Logic Program Synthesis Algorithm that can
               Refute Inappropriate Similarities},
  booktitle = alt-ws,
  year      = {1995},
  volume    = {997},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {266--281},
  publisher = {Springer-Verlag}
}

@inproceedings{Saerens96,
  author    = {Marco Saerens},
  title     = {Non Mean Square Error Criteria for the Training of
               Learning Machines},
  booktitle = icml,
  year      = {1996},
  pages     = {427--434},
  publisher = {Morgan Kaufmann}
}

@article{Sager79,
  author  = {T.~W.~Sager},
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             Isopleth},
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  year    = {1979},
  volume  = {74},
  pages   = {329--339},
  number  = {366}
}

@article{Sagiv_WassersteinDistances_2019,
  title    = {The wasserstein distances between pushed-forward measures
              with applications to uncertainty quantification},
  abstract = {In the study of dynamical and physical systems, the input
              parameters are often uncertain or randomly distributed
              according to a measure ̺. The system’s response f pushes
              forward ̺ to a new measure f ◦ ̺ which we would like to
              study. However, we might not have access to f , but to its
              approximation g. We thus arrive at a fundamental question
              – if f and g are close in Lq, does the measure g ◦ ̺
              approximate f ◦ ̺ well, and in what sense? Previously,
              it was demonstrated that the answer to this question might
              be negative in terms of the Lp distance between probability
              density functions (PDF). Instead, we show in this paper
              that the Wasserstein metric is the proper framework for
              this question. We bound the Wasserstein distance Wp(f ◦
              ̺, g ◦ ̺) from above by f − g q , and provide lower
              bounds for the cases where p = 1, 2. From a numerical
              analysis standpoint, since the Wasserstein distance is
              related to the cumulative distribution functions (CDF), we
              show that the latter is well approximated by methods such
              as spline interpolation and generalized polynomial chaos
              (gPC).},
  language = {en},
  journal  = {arXiv:1902.05451 [math]},
  author   = {Sagiv, Amir},
  month    = feb,
  year     = {2019},
  note     = {arXiv: 1902.05451},
  keywords = {⛔ No DOI found},
  file     = {Sagiv - The wasserstein distances between pushed-forward
              measures with applications to
              (2019).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Sagiv - The wasserstein distances between
              pushed-forward measures with applications to
              (2019).pdf:application/pdf}
}

@inproceedings{SahHeaSau96,
  author    = {M. Sahami and M. Hearst and E. Saund},
  title     = {Applying the multiple cause mixture model to text
               categorization},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {435--443},
  publisher = {Morgan Kaufmann}
}

@inproceedings{SahVisZha11,
  author    = {Ankan Saha and S.~V.~N. Vishwanathan and Xinhua Zhang},
  title     = {New Approximation Algorithms for Minimum Enclosing Convex
               Shapes},
  booktitle = {ACM-SIAM Syposium on Discrete Algorithms (SODA)},
  year      = {2011}
}

@article{SaiBer93,
  author  = {L. Saitta and F. Bergadano},
  title   = {Pattern Recognition and Valiant's Learning Framework},
  journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
  year    = {1993},
  volume  = {15},
  pages   = {145--155},
  number  = {2},
  month   = feb
}

@article{SaiBotNer93,
  author    = {Lorenza Saitta and Marco Botta and Filippo Neri},
  title     = {Multistrategy Learning and Theory Revision},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {11},
  pages     = {153--172},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{SaiDorWuSoa01,
  author    = {P. Saisan and G. Doretto and Y.N. Wu and S. Soatto},
  title     = {Dynamic Texture Recognition},
  booktitle = {{CVPR}},
  year      = {2001},
  volume    = {2},
  pages     = {58--63}
}

@article{SaiNer98,
  author    = {Lorenzo Saitta and Filippo Neri},
  title     = {Learning in the 'real world'},
  journal   = {Machine Learning},
  year      = {1998},
  volume    = {30},
  pages     = {133--163},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{SaiTay98,
  author    = {A. S. Saidi and S. Tayeb-bey},
  title     = {Grammatical inference in document recognition},
  booktitle = {Proc. 4th International Colloquium on Grammatical
               Inference - ICGI '98},
  year      = {1998},
  volume    = {1433},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {175--186},
  publisher = {Springer-Verlag}
}

@book{Saitoh88,
  title     = {Theory of Reproducing Kernels and its Applications},
  publisher = {Longman Scientific \& Technical},
  year      = {1988},
  author    = {S.~Saitoh},
  address   = {Harlow, England}
}

@inproceedings{Sakaki92,
  author    = {Y. Sakaki},
  title     = {The Logic of Molecular Geneticists for the Understanding
               of Genetic Information},
  booktitle = alt-ws,
  year      = {1992},
  pages     = {3--5},
  publisher = {IOS Press}
}

@techreport{Sakakibara87,
  author      = {Y. Sakakibara},
  title       = {Inductive Inference of Logic Programs Based on Algebraic
                 Semantics},
  institution = {IIAS-SIS, Fujitsu Limited, Research Report No. 79},
  year        = {1987}
}

@techreport{Sakakibara87b,
  author      = {Y. Sakakibara},
  title       = {Inferring Parsers of Context Free Languages from
                 Structural Examples},
  institution = {Fujitsu Ltd.},
  year        = {1987},
  number      = {81}
}

@techreport{Sakakibara87c,
  author      = {Y. Sakakibara},
  title       = {Inferring Parsers of Context-Free Languages from
                 Structural Examples},
  institution = {IIAS-SIS, Fujitsu Limited, Research Report No. 81},
  year        = {1987}
}

@inproceedings{Sakakibara89,
  author    = {Y. Sakakibara},
  title     = {Learnability in the Presence of Classification Noise},
  booktitle = {Fujitsu IIAS-SIS Workshop on Computational Learning
               Theory},
  year      = {1989},
  pages     = {1--27},
  month     = nov
}

@techreport{Sakakibara90,
  author      = {Y. Sakakibara},
  title       = {An Efficient Robust Algorithm for Learning Decision
                 Lists},
  institution = {International Institute for Advanced Study of Social
                 Information Science, Fujitsu Laboratories Ltd},
  year        = {1990},
  number      = {No. 105},
  month       = aug,
  comment     = {Learns k-DL in the presence of classification noise}
}

@article{Sakakibara90b,
  author  = {Y. Sakakibara},
  title   = {Learning context-free grammars from structural data in
             polynomial time},
  journal = {Theoret. Comput. Sci.},
  year    = {1990},
  pages   = {223--242},
  annote  = {Earlier version in COLT88}
}

@phdthesis{Sakakibara91,
  author = {Y. Sakakibara},
  title  = {Algorithmic Learning of Formal Languages and Decision
            Trees},
  school = {Tokyo Institute of Technology},
  year   = {1991},
  month  = oct,
  note   = {(International Institute for Advanced Study of Social
            Information Science, Fujitsu Laboratories Ltd, Research
            Report IIAS-RR-91-22E)}
}

@article{Sakakibara91b,
  author  = {Y. Sakakibara},
  title   = {On Learning from Queries and Counterexamples in the
             Presence of Noise},
  journal = {Inform. Proc. Lett.},
  year    = {1991},
  volume  = {37},
  pages   = {279--284},
  number  = {5},
  month   = mar
}

@article{Sakakibara92,
  author  = {Yasubumi Sakakibara},
  title   = {Efficient learning of context-free grammars from positive
             structural examples},
  journal = {Inform. Comput.},
  year    = {1992},
  volume  = {97},
  pages   = {23--60}
}

@article{Sakakibara93,
  author    = {Yasubumi Sakakibara},
  title     = {Noise-Tolerant Occam Algorithms and Their Applications to
               Learning Decision Trees},
  journal   = {Machine Learning},
  year      = {1993},
  volume    = {11},
  pages     = {37--62},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Sakakibara97,
  author  = {Yasubumi Sakakibara},
  title   = {Recent Advances of Grammatical Inference},
  journal = {Theoret. Comput. Sci.},
  year    = {1997},
  volume  = {185},
  pages   = {15--45},
  number  = {1},
  note    = {Special issue for ALT '95}
}

@inproceedings{Sakamoto95,
  author    = {Hiroshi Sakamoto},
  title     = {Language Learning from Membership Queries and
               Characteristic Examples},
  booktitle = altws,
  year      = {1995},
  volume    = {997},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {55--65},
  publisher = {Springer-Verlag}
}

@inproceedings{Sakamoto97,
  author    = {Hiroshi Sakamoto},
  title     = {Learning Simple Deterministic Finite-Memory Automata},
  booktitle = alt-ws,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {416--431},
  publisher = {Springer-Verlag}
}

@inproceedings{Sakamoto98,
  author    = {H. Sakamoto},
  title     = {Finding a One-Variable Pattern from Incomplete Data},
  booktitle = alt,
  year      = {1998},
  volume    = {1501},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {234--246},
  publisher = {Springer-Verlag}
}

@inproceedings{SakAriAri00,
  author    = {Hiroshi Sakamoto and Hiroki Arimura and Setsuo Arikawa},
  title     = {Identification of Tree Translation Rules from Examples},
  booktitle = {Grammatical Inference: {A}lgorithms and {A}pplications,
               5th International Colloquium, ICGI 2000, Lisbon, Portugal,
               September 11--13, 2000 ; Proceedings},
  year      = {2000},
  volume    = {1891},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {241--255},
  publisher = {Springer, Berlin}
}

@inproceedings{SakBroHugMiaetal94,
  author    = {Yasubumi Sakakibara and M. Brown and R. Hughey and I.
               Saira Mian and Kimmen Sj{\"o}lander and Rebecca C.
               Underwood and David Haussler},
  title     = {Recent Methods for {RNA} Modeling Using Stochastic
               Context-Free Grammars},
  booktitle = {Proc. of the Asilomar Conference on Combinatorial Pattern
               Matching},
  year      = {1994},
  address   = {New York, NY},
  publisher = {Springer-Verlag}
}

@article{SakBroHugMiaetal94b,
  author  = {Yasubumi Sakakibara and M. Brown and R. Hughey and I.
             Saira Mian and Kimmen Sj{\"o}lander and Rebecca C.
             Underwood and David Haussler},
  title   = {Stochastic Context-Free Grammars for t{RNA} modeling},
  journal = {Nucleic Acids Research},
  year    = {1994},
  volume  = {22},
  pages   = {5112--5120}
}

@inproceedings{SakHar92,
  author    = {S. Sakurai and M. Haraguchi},
  title     = {Towards Learning by Abstraction},
  booktitle = alt-ws,
  year      = {1992},
  pages     = {288--298},
  publisher = {IOS Press}
}

@inproceedings{SakJanLan94,
  author    = {Y. Sakakibara and K. P. Jantke and S. Lange},
  title     = {Learning Languages by Collecting Classes and Tuning
               Parameters},
  booktitle = alt-ws,
  year      = {1994},
  pages     = {532--546},
  publisher = {Springer-Verlag}
}

@inproceedings{SakKon99,
  author    = {Yasubumi Sakakibara and Mitsuhiro Kondo},
  title     = {{GA}-Based Learning of Context-Free Grammars Using Tabular
               Representations},
  booktitle = icml,
  year      = {1999},
  pages     = {354--360},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{SakLevChiHoretal97,
  author    = {M. F. Sakr and S. P. Levitan and D. M. Chiarulli and B. G.
               Horne and C. L. Giles},
  title     = {Predicting multiprocessor memory access patterns with
               learning models},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {305--312},
  publisher = {Morgan Kaufmann}
}

@article{SakMar00,
  author  = {Y. Sakai and A. Maruoka},
  title   = {Learning Monotone Log-Term {DNF} Formulas under the
             Uniform Distribution},
  journal = {Theory of Computing Systems},
  year    = {2000},
  volume  = {33},
  pages   = {17--33},
  number  = {1}
}

@inproceedings{SakMar93,
  author    = {Y. Sakai and A. Maruoka},
  title     = {Learning $k$-Term Monotone {B}oolean Formulae},
  booktitle = alt-ws,
  year      = {1993},
  volume    = {743},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {197--207},
  publisher = {Springer-Verlag}
}

@inproceedings{SakMar94,
  author    = {Y. Sakai and A. Maruoka},
  title     = {Learning Monotone Log-Term {DNF} Formulas},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {165--172},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{SakMur00,
  author    = {Yasubumi Sakakibara and Hidenori Muramatsu},
  title     = {Learning Context-Free Grammars from Partially Structured
               Examples},
  booktitle = {Grammatical Inference: {A}lgorithms and {A}pplications,
               5th International Colloquium, ICGI 2000, Lisbon, Portugal,
               September 11--13, 2000 ; Proceedings},
  year      = {2000},
  volume    = {1891},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {229--240},
  publisher = {Springer, Berlin}
}

@inproceedings{SakSir92,
  author    = {Y. Sakakibara and R. Siromoney},
  title     = {A Noise Model on Learning Sets of Strings},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {295--302},
  publisher = {ACM Press, New York, NY}
}

@article{SakTakMar99,
  author  = {Y. Sakai and E. Takimoto and A. Maruoka},
  title   = {Proper Learning Algorithm for Functions of $k$ Terms Under
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@book{Schalkoff89b,
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  year      = {2001},
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@mastersthesis{Schapire88,
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  year   = {1988},
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@article{Schapire90,
  author  = {R.~Schapire},
  title   = {The strength of weak learnability},
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  volume  = {5},
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  number  = {2}
}

@article{Schapire90b,
  author    = {Robert E. Schapire},
  title     = {The Strength of Weak Learnability},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  pages     = {197--227},
  number    = {2},
  annote    = {Earlier version in FOCS89},
  publisher = {Kluwer Academic Publishers, Boston}
}

@book{Schapire92,
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  publisher = {{MIT} Press},
  year      = {1992},
  author    = {R. E. Schapire},
  address   = {Cambridge, MA},
  comment   = {His PhD thesis in book form. Ph.D was in 1991.}
}

@article{Schapire94,
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  year      = {1994},
  volume    = {14},
  pages     = {47--81},
  number    = {1},
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  publisher = {Kluwer Academic Publishers, Boston}
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@inproceedings{Schapire97,
  author    = {Robert E. Schapire},
  title     = {Using output codes to boost multiclass learning problems},
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  year      = {1997},
  pages     = {313--321},
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@inproceedings{Schapire99,
  author    = {Robert E. Schapire},
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  year      = {1999},
  pages     = {114--124},
  publisher = {ACM Press, New York, NY}
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@inproceedings{Schapire99b,
  author    = {Robert E. Schapire},
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  year      = {1999},
  volume    = {1572},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {1--10},
  publisher = {Springer-Verlag}
}

@inproceedings{Schapire99c,
  author    = {Robert E. Schapire},
  title     = {Theoretical Views of Boosting and Applications},
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  year      = {1999},
  volume    = {1720},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {13--25},
  publisher = {Springer-Verlag}
}

@article{Schapire_ImprovedBoosting_1999,
  title   = {Improved boosting algorithms using confidence-rated
             predictions},
  volume  = {37},
  issn    = {08856125},
  doi     = {10.1023/a:1007614523901},
  number  = {3},
  journal = {Machine Learning},
  author  = {Schapire, Robert E. and Singer, Yoram},
  year    = {1999},
  pages   = {297--336},
  file    = {Schapire, Singer - Improved boosting algorithms using
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@article{Schapire_StrengthWeak_1990,
  title    = {The strength of weak learnability},
  volume   = {5},
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  doi      = {10.1007/bf00116037},
  abstract = {This paper addresses the problem of improving the accuracy
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              source of examples of the unknown concept, the learner with
              high probability is able to output an hypothesis that is
              correct on all but an arbitrarily small fraction of the
              instances. The concept class isweakly learnable if the
              learner can produce an hypothesis that performs only
              slightly better than random guessing. In this paper, it is
              shown that these two notions of learnability are
              equivalent.},
  number   = {2},
  journal  = {Machine Learning},
  author   = {Schapire, Robert E.},
  month    = jun,
  year     = {1990},
  pages    = {197--227},
  file     = {Schapire - The strength of weak learnability
              (1990).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Schapire - The strength of weak learnability
              (1990).pdf:application/pdf}
}

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  volume    = {1327},
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  author    = {H.~Schwenk and Y.~Bengio},
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@article{SchBla93,
  author  = {N.~A.~Schmajuk and H.~T.~Blair},
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  year        = {1993},
  type        = {{A.I.~Memo}},
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               Signal Processing},
  year      = {1990},
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@inproceedings{SchCoh00,
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  title     = {Less is More: {A}ctive Learning with Support Vector
               Machines},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {839--846},
  publisher = {Morgan Kaufmann, San Francisco, CA}
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@article{Scheffe47,
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@inproceedings{Scheffer00,
  author    = {Tobias Scheffer},
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  year      = {2000},
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  series    = {Lecture Notes in Artificial Intelligence},
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@inproceedings{Scheffer00b,
  author    = {Tobias Scheffer},
  title     = {Nonparametric Regularization of Decision Trees},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
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  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {344--356},
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@inproceedings{Scheffer00c,
  author    = {Tobias Scheffer},
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               Validatory Model Selection Criteria},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {831--838},
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@article{Scheinberg06,
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  title   = {An Efficient Implementation of an Active Set Method for
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  year    = {2006},
  volume  = {7},
  pages   = {2237--2257},
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@inproceedings{Scheinetal02,
  author    = {A. I. Schein and A. Popescul and L. H. Ungar and D. M.
               Pennock},
  title     = {Methods and metrics for cold-start recommendations},
  pages     = {253--260},
  editor    = {M. Beaulieu and R. Baeza-Yates and S. Hyon Myaeng and K.
               J{\"a}rvelin},
  booktitle = {the 25th annual international {ACM} {SIGIR} Conference on
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  address   = {New York},
  year      = {2002}
}

@techreport{Scheler94,
  author      = {Gabi Scheler},
  title       = {Pattern Classification with Adaptive Distance Measures},
  institution = {Technische Universit{\"a}t M{\"u}nchen},
  year        = {1994}
}

@book{Schetzen80,
  title     = {The Volterra and Wiener Theories of Nonlinear Systems},
  publisher = {John Wiley and Sons},
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  address   = {New York}
}

@techreport{Schewe97,
  author      = {Sandra Schewe},
  title       = {Automatische {K}ategorisierung von {V}olltexten unter
                 {A}nwendung von {NLP} {T}echniken},
  institution = {University Dortmund, Department of Artificial
                 Intelligence},
  year        = {1997},
  note        = {LS-8 Report 22}
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@book{SchFre10,
  author    = {Robert E.\ Schapire and Yoav Freund},
  title     = {Boosting: Foundation and Algorithms},
  publisher = {Unknown},
  year      = 2010,
  note      = {draft}
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@inproceedings{SchFreBarLee97,
  author    = {R. E. Schapire and Y. Freund and P. Bartlett and W.-S.
               Lee},
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               Effectiveness of Voting Methods},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {322--330},
  publisher = {Morgan Kaufmann}
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@article{SchFreBarLee98,
  author  = {R. Schapire and Y. Freund and P. L. Bartlett and W. S.
             Lee},
  title   = {Boosting the Margin: {A} New Explanation for the
             Effectiveness of Voting Methods},
  journal = {Ann. Statist.},
  year    = {1998},
  volume  = {26},
  pages   = {1651--1686},
  issue   = {5}
}

@inproceedings{SchGia00,
  author    = {Nicol N. Schraudolph and Xavier Giannakopoulos},
  title     = {Online Independent Component Analysis With Local Learning
               Rate Adaptation},
  booktitle = {Neural Information Processing Systems},
  year      = {2000},
  editor    = {S.~A. Solla and T.~K. Leen and K.-R. MÂÃ¼ller},
  volume    = {12},
  pages     = {789-795},
  address   = {Vancouver, Canada},
  publisher = {{MIT} Press}
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@inproceedings{SchGis96,
  author    = {M. Schmidt and H. Gish},
  title     = {Speaker Identification via Support Vector Classifiers},
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  year      = {1996},
  pages     = {105--108},
  address   = {Atlanta, GA},
  month     = may
}

@inproceedings{SchGra03,
  author    = {Nicol N. Schraudolph and Thore Graepel},
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  booktitle = {Proc.\ 9th Intl.\ Workshop Artificial Intelligence and
               Statistics},
  year      = {2003},
  editor    = {Christopher M. Bishop and Brendan J. Frey},
  pages     = {7-13},
  address   = {Key West, Florida},
  isbn      = {0-9727358-0-1}
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@article{SchGra86,
  author    = {Jeffrey C. Schlimmer and Jr. R. H. Granger},
  title     = {Incremental Learning from Noisy Data},
  journal   = {Machine Learning},
  year      = {1986},
  volume    = {1},
  pages     = {317--354},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{SchGre94,
  author    = {Dale Schuurmans and Russell Greiner},
  title     = {Learning Default Concepts},
  booktitle = {Proc. of the Tenth Canadian Conference on Artificial
               Intelligence},
  year      = {1994},
  pages     = {519--523}
}

@inproceedings{SchGre95,
  author    = {Dale Schuurmans and Russell Greiner},
  title     = {Practical {PAC} Learning},
  booktitle = {the International Joint Conference on Artificial
               Intelligence},
  year      = {1995},
  addres    = {Montreal}
}

@inproceedings{SchGre95b,
  author    = {Dale Schuurmans and Russell Greiner},
  title     = {Sequential {PAC} learning},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {377--384},
  publisher = {ACM Press, New York, NY}
}

@inbook{SchGre97,
  chapter   = {10},
  title     = {Fast Distribution-Specific Learning},
  publisher = {{MIT} Press},
  year      = {1997},
  author    = {Dale Schuurmans and Russell Greiner},
  booktitle = {Computational Learning Theory and Natural Learning Systems
               IV: Making Learning Systems Practical}
}

@inbook{SchGre97b,
  chapter   = {6},
  pages     = {87--105},
  title     = {Learning to Classify Incomplete Examples},
  publisher = {{MIT} Press},
  year      = {1997},
  author    = {Dale Schuurmans and Russell Greiner},
  booktitle = {Computational Learning Theory and Natural Learning Systems
               IV: Making Learning Systems Practical}
}

@inproceedings{SchGreDar97,
  author    = {Tobias Scheffer and Russell Greiner and Christian Darken},
  title     = {Why experimentation can be better than Perfect Guidance},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {331--339},
  publisher = {Morgan Kaufmann}
}

@inproceedings{SchGueVis07b,
  author    = {Nicol N. Schraudolph and Simon G\"unter and S.~V.~N.
               Vishwanathan},
  title     = {Fast Iterative Kernel {PCA}},
  booktitle = {Advances in Neural Information Processing Systems 19},
  month     = {June},
  year      = {2007},
  editor    = {B. {Sch\"olkopf} and J. Platt and T. Hofmann},
  address   = {Cambridge MA},
  publisher = {{MIT} Press}
}

@inproceedings{SchGunVis07,
  crossref = {SchGunVis07b}
}

@article{SchHan94,
  author  = {Robert W. Schwanke and Stephen Jose{\'e} Hanson},
  title   = {Using neural networks to modularize software},
  journal = {Machine Learning},
  year    = {1994},
  volume  = {15},
  pages   = {137--168},
  number  = {2}
}

@inproceedings{SchHer97,
  author    = {T. Scheffer and R. Herbrich},
  title     = {Unbiased Assessment of Learning Algorithms},
  booktitle = {the International Joint Conference on Artificial
               Intelligence},
  year      = {1997},
  pages     = {798--803},
  adress    = {Nagoya, Japan}
}

@inproceedings{SchHerSmo01,
  author    = {B.~Sch{\"o}lkopf and R.~Herbrich and A.~J.~Smola},
  title     = {A Generalized Representer Theorem},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2001},
  editor    = {D.~P.~Helmbold and B.~Williamson},
  number    = {2111},
  series    = {Lecture Notes in Comput. Sci.},
  pages     = {416--426},
  address   = {London, UK},
  publisher = {Springer-Verlag}
}

@techreport{SchHerSmoWil00,
  author      = {B.~Sch\"olkopf and R.~Herbrich and A. J. Smola and
                 R.~C.~Williamson},
  title       = {A Generalized Representer Theorem},
  institution = {{NeuroCOLT}},
  year        = {2000},
  number      = {2000-81},
  note        = {To appear in {\em the Annual Conference on Learning Theory
                 2001}}
}

@article{SchHerWys96,
  author  = {Tobias Scheffer and Ralf Herbrich and Fritz Wysotzki},
  title   = {Efficient theta-subsumption based on graph algorithms},
  journal = {Lecture Notes in Artificial Intelligence},
  year    = {1996},
  volume  = {1314},
  pages   = {212--228}
}

@inproceedings{SchHerWys96b,
  author    = {Tobias Scheffer and Ralf Herbrich and Fritz Wysotzki},
  title     = {Graph based subsumption algorithms for machine learning},
  booktitle = {Proceedings zum Fachgruppentreffen Maschinelles Lernen},
  year      = {1996},
  pages     = {101--105}
}

@inproceedings{SchHerWys96c,
  author    = {Tobias Scheffer and Ralf Herbrich and Fritz Wysotzki},
  title     = {Efficient theta-subsumption based on graph algorithms},
  booktitle = {Proceedings International Workshop on Inductive Logic
               Programming},
  year      = {1996},
  pages     = {312--329}
}

@inproceedings{SchHowHub89,
  author    = {D. B. Schwartz and R. E. Howard and W. E. Hubbard},
  title     = {Adaptive Neural Networks Using {MOS} Charge Storage},
  booktitle = {Proc. 1988 Neural Information Processing Conference},
  year      = {1989},
  pages     = {761--768}
}

@techreport{SchJoa98,
  author      = {Tobias Scheffer and Thorsten Joachims},
  title       = {Estimating the Expected Error of Empirical Minimizers for
                 Model Selection},
  institution = {TU Berlin},
  year        = {1998},
  note        = {TR-98/09}
}

@inproceedings{SchJoa99,
  author    = {Tobias Scheffer and Thorsten Joachims},
  title     = {Expected Error Analysis for Model Selection},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  adress    = {Slovenia}
}

@techreport{SchKam08,
  author      = {Nicol N. Schraudolph and Dmitry Kamenetsky},
  title       = {Efficient Exact Inference in Planar {I}sing Models},
  number      = {0810.4401},
  institution = {arXiv},
  note        = {{\small\url{http://aps.arxiv.org/abs/0810.4401}}},
  month       = oct,
  year        = 2008
}

@inproceedings{SchKam09,
  author    = {Nicol N. Schraudolph and Dmitry Kamenetsky},
  title     = {Efficient Exact Inference in Planar {I}sing Models},
  editor    = nips21e,
  booktitle = nips21,
  publisher = {MIT Press},
  address   = {Cambridge, MA},
  year      = 2009
}

@inproceedings{SchKam09b,
  crossref = {SchKam09}
}

@inproceedings{SchKipBloetal18,
  title        = {Modeling relational data with graph convolutional
                  networks},
  author       = {Schlichtkrull, Michael and Kipf, Thomas N and Bloem, Peter
                  and Van Den Berg, Rianne and Titov, Ivan and Welling, Max},
  booktitle    = {European Semantic Web Conference},
  pages        = {593--607},
  year         = {2018},
  organization = {Springer}
}

@inproceedings{SchKipBloVan18,
  title        = {Modeling relational data with graph convolutional
                  networks},
  author       = {Schlichtkrull, Michael and Kipf, Thomas N and Bloem, Peter
                  and van den Berg, Rianne and Titov, Ivan and Welling, Max},
  booktitle    = eswc,
  pages        = {593--607},
  year         = {2018},
  organization = {Springer}
}

@inproceedings{SchKniSmoBur98,
  author    = {B. Sch\"olkopf and P. Knirsch and A. J. Smola and C.
               Burges},
  title     = {Fast Approximation of Support Vector Kernel Expansions,
               and an Interpretation of Clustering as Approximation in
               Feature Spaces},
  booktitle = {Mustererkennung 1998\,---\,20.~DAGM-Symposium},
  year      = {1998},
  editor    = {P. Levi and M. Schanz and R.-J. Ahlers and F. May},
  series    = {Informatik aktuell},
  pages     = {124--132},
  address   = {Berlin},
  publisher = {Springer}
}

@inproceedings{SchLeRBac11,
  author    = {M. Schmidt and N. Le Roux and F. Bach},
  title     = {Convergence Rates of Inexact Proximal-Gradient Methods for
               Convex Optimization},
  booktitle = nips,
  year      = {2011}
}

@inproceedings{SchLib99,
  author    = {J Michael Schultz and Mark Liberman},
  title     = {Topic Detection and Tracking using idf-Weighted Cosine
               Coefficient},
  booktitle = {Broadcast News Workshop},
  year      = {1999}
}

@article{SchLin96,
  author    = {William C. Schmidt and Charles X. Ling},
  title     = {A Decision-Tree Model of Balance Scale Development},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {24},
  pages     = {203--229},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{SchMarSch94,
  author  = {T. R. Schultz and Denis Mareschal and William C. Schmidt},
  title   = {Modeling cognitive development on balance-scale
             phenomena},
  journal = {Machine Learning},
  year    = {1994},
  volume  = {16},
  pages   = {57--86},
  number  = {1/2}
}

@inproceedings{Schmidhuber95,
  author    = {J{\"u}rgen Schmidhuber},
  title     = {Discovering solutions with low {K}olmogorov complexity and
               high generalization capability},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {488--496},
  publisher = {Morgan Kaufmann}
}

@article{Schmidhuber97,
  author  = {J\"urgen Schmidhuber},
  title   = {Discovering Neural Nets with Low Kolmogorov Complexity and
             High Generalization Ability},
  journal = {Neural Networks},
  year    = {1997},
  volume  = {10},
  pages   = {857--873},
  number  = {5}
}

@article{schmidt2017minimizing,
  title     = {Minimizing finite sums with the stochastic average
               gradient},
  author    = {Schmidt, Mark and Le Roux, Nicolas and Bach, Francis},
  journal   = {Mathematical Programming},
  volume    = {162},
  number    = {1-2},
  pages     = {83--112},
  year      = {2017},
  publisher = {Springer}
}

@inproceedings{Schmidt96,
  author    = {M. Schmidt},
  title     = {Identifying Speakers with Support Vectors Networks},
  booktitle = {Interface '96},
  year      = {1996},
  address   = {Sydney},
  month     = jul
}

@misc{Schmidt98,
  author = {M. Schmidt},
  title  = {Private Communication},
  year   = {1998}
}

@inproceedings{Schmidtetal15,
  author    = {M. Schmidt and R. Babanezhad and M.O. Ahmed and A. Defazio
               and A. Clifton and A. Sarkar},
  title     = {Non-Uniform Stochastic Average Gradient Method for
               Training Conditional Random Fields},
  booktitle = aistats,
  year      = {2015}
}

@inproceedings{Schmidtetal19,
  author    = {Ludwig Schmidt and Shibani Santurkar and Dimitris Tsipras
               and Kunal Talwar and Aleksander Madry},
  title     = {Adversarially robust generalization requires more data},
  booktitle = nips,
  year      = {2018}
}

@article{SchMikBurKnietal99,
  author  = {B.~{Sch\"olkopf} and S.~Mika and C.~Burges and P.~Knirsch
             and K.-R.~{M\"uller} and G.~{R\"atsch} and A. J. Smola},
  title   = {Input Space vs.\ Feature Space in Kernel-Based Methods},
  journal = {IEEE Transactions on Neural Networks},
  year    = {1999},
  volume  = {10},
  pages   = {1000--1017},
  number  = {5}
}

@inproceedings{SchMikSmoRatetal98,
  author    = {B.~{Sch\"olkopf} and S.~Mika and A. J. Smola and
               G.~{R\"atsch} and K.-R.~{M\"uller}},
  title     = {Kernel {PCA} Pattern Reconstruction \emph{via} Approximate
               Pre-Images},
  booktitle = {the International Conference on Artificial Neural
               Networks},
  year      = {1998},
  editor    = {L.~Niklasson and M.~{Bod\'en} and T.~Ziemke},
  series    = {Perspectives in Neural Computing},
  pages     = {147--152},
  address   = {Berlin},
  publisher = {Springer}
}

@inproceedings{Schmitt98,
  author    = {M. Schmitt},
  title     = {On the Sample Complexity for Neural Trees},
  booktitle = alt,
  year      = {1998},
  volume    = {1501},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {375--384},
  publisher = {Springer-Verlag}
}

@article{Schmitt99,
  author    = {M. Schmitt},
  title     = {On the Sample Complexity for Nonoverlapping Neural
               Networks},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {37},
  pages     = {131--141},
  number    = {2},
  publisher = {Kluwer Academic Publishers, Boston}
}

@incollection{SchMou00,
  title     = {Factors affecting small-group tutorial learning: A review
               of research},
  publisher = {Routledge},
  year      = {2000},
  editor    = {Dorothy H. Evensen and Cindy E. Hmelo and Cindy E.
               Hmelo-Silver},
  author    = {Schmidt, H. G. and Moust, J. H. C.},
  booktitle = {Problem-based Learning: A Research Perspective on Learning
               Interactions},
  address   = {Mahwah, NJ}
}

@article{SchMulSmo99,
  author  = {B.~{Sch\"olkopf} and K.~{M\"uller} and A. J. Smola},
  title   = {Lernen mit {K}ernen},
  journal = {Informatik Forschung und Entwicklung},
  year    = {1999},
  volume  = {14},
  pages   = {154--163},
  issue   = {3}
}

@article{Schneider01,
  author  = {T. Schneider},
  title   = {Analysis of incomplete climate data},
  journal = {Journal of Climate},
  year    = {2001},
  volume  = {14},
  pages   = {853ÃÂ-871}
}

@article{Schneider53,
  author  = {Schneider, Hans},
  title   = {An Inequality for Latent Roots Applied To Determinants
             with Dominant Principal Diagonal},
  journal = {Journal of the London Mathematical Society},
  volume  = {s1-28},
  number  = {1},
  pages   = {8-20},
  year    = {1953}
}

@book{Schneider_ConvexBodies_2014,
  address    = {New York, NY, USA},
  edition    = {2},
  series     = {Encyclopedia of mathematics and its applications},
  title      = {Convex bodies: the {Brunn}-{Minkowski} theory},
  isbn       = {978-1-107-60101-7},
  shorttitle = {Convex bodies},
  publisher  = {Cambridge University Press},
  author     = {Schneider, Rolf},
  year       = {2014},
  file       = {Schneider - Convex bodies
                (2014).pdf:/Users/zcranko/Documents/Library/Book/Schneider
                - Convex bodies (2014).pdf:application/pdf}
}

@article{Schoenberg38,
  author  = {I.~J.~Schoenberg},
  title   = {Metric spaces and completely monotone functions},
  journal = {Ann. Math.},
  year    = {1938},
  volume  = {39},
  pages   = {811--841}
}

@article{Schoenberg38b,
  author  = {I.~J.~Schoenberg},
  title   = {Metric spaces and positive definite functions},
  journal = {Transactions of the American Mathematical Society},
  year    = {1938},
  volume  = {44},
  pages   = {522--536}
}

@article{Schoenberg42,
  author  = {I.~Schoenberg},
  title   = {Positive definite functions on spheres},
  journal = {Duke Math.~J.},
  year    = {1942},
  volume  = {9},
  pages   = {96--108}
}

@article{Schoenberg46,
  author  = {I.~J.~Schoenberg},
  title   = {Contribution to the problem of approximation of
             equidistant data by analytic functions},
  journal = {Quart.~Appl.~Math.},
  year    = {1946},
  volume  = {4},
  pages   = {45--99, 112--141}
}

@article{Schoenberg46b,
  author  = {I.~J.~Schoenberg},
  title   = {Contributions to the problem of approximation of
             equidistant data by analytic functions, Part {A}: On the
             problem of smoothing of graduation, a first class of
             analytic approximation formulae},
  journal = {Quart.~Appl.~Math.},
  year    = {1946},
  volume  = {4},
  pages   = {45--99}
}

@article{Schoenberg46c,
  author  = {I.~J.~Schoenberg},
  title   = {Contributions to the problem of approximation of
             equidistant data by analytic functions, Part {B}: On the
             problem of osculatory interpolation, a second class of
             analytic approximation formulae},
  journal = {Quart.~Appl.~Math.},
  year    = {1946},
  volume  = {4},
  pages   = {112--141}
}

@article{Schoenberg63,
  author  = {I.~J.~Schoenberg},
  title   = {Spline interpolation and the higher derivatives},
  journal = {Proc.~N.~A.~S.},
  year    = {1963},
  volume  = {51},
  pages   = {24--28}
}

@article{Schoenberg64,
  author  = {I.~J.~Schoenberg},
  title   = {Spline functions and the problem of graduation},
  journal = {Proc.~N.~A.~S.},
  year    = {1964},
  volume  = {52},
  pages   = {947}
}

@inproceedings{Schoenberg64b,
  author       = {I.~J.~Schoenberg},
  title        = {On Interpolation by spline functions and its minimal
                  properties},
  booktitle    = {{\"Uber Approximationstheorie (On Approximation Theory)}},
  year         = {1964},
  editor       = {P.~L.~Butzer and J.~Korevaar},
  pages        = {109--129},
  address      = {Basel},
  organization = {International Series of Numerical Mathematics},
  publisher    = {{Birk\"auser Verlag}}
}

@article{Schoenberg69,
  author  = {I.~J.~Schoenberg},
  title   = {Cardinal interpolation and spline functions},
  journal = {Journal of Approximation theory},
  year    = {1969},
  volume  = {2},
  pages   = {167--206}
}

@techreport{Scholkopf00,
  author      = {B.~Sch\"olkopf},
  title       = {Statistical Learning and Kernel Methods},
  institution = {Microsoft Research},
  year        = {2000},
  type        = {{TR} {MSR} 2000 -},
  number      = {23},
  address     = {Redmond, WA},
  note        = {Also: Kursunterlagen {Interdisziplin\"ares} Kolleg
                 Kognitions- und Neurowissenschaften 2000, {G\"unne} am
                 {M\"ohnesee}},
  url         = {http://www.research.microsoft.com/scripts/pubs/view.asp?TR_ID=MSR-TR-2000-23}
}

@techreport{Scholkopf00b,
  author      = {B.~Sch\"olkopf},
  title       = {The Kernel Trick for Distances},
  institution = {Microsoft Research},
  year        = {2000},
  type        = {{TR} {MSR} 2000 -},
  number      = {51},
  address     = {Redmond, WA},
  note        = {Published in: T.~K.~Leen, T.~G.~Dietterich and V.~Tresp
                 (eds.), {\em Advances in Neural Information Processing
                 Systems 13}, {MIT} Press, 2001},
  url         = {http://www.research.microsoft.com/scripts/pubs/view.asp?TR_ID=MSR-TR-2000-51}
}

@book{scholkopf2001learning,
  title     = {Learning with kernels: support vector machines,
               regularization, optimization, and beyond},
  author    = {Scholkopf, Bernhard and Smola, Alexander J},
  year      = {2001},
  publisher = {MIT press}
}

@incollection{Scholkopf96,
  author    = {B.~{Sch\"olkopf}},
  title     = {{K\"unstliches} {Lernen}},
  booktitle = {Komplexe adaptive Systeme (Forum {f\"ur}
               {Interdisziplin\"are} Forschung Bd.\ 15)},
  publisher = {{R\"oll}},
  year      = {1996},
  editor    = {S.~Bornholdt and P.~H.~Feindt},
  pages     = {93--117},
  address   = {Dettelbach}
}

@book{Scholkopf97,
  title     = {Support Vector Learning},
  publisher = {R.~Oldenbourg Verlag},
  year      = {1997},
  author    = {B.~{Sch\"olkopf}},
  address   = {Munich},
  note      = {Download: {http://www.kernel-machines.org}.},
  comment   = {ISBN 3-486-24632-1}
}

@article{Scholkopf98,
  author  = {B.~{Sch\"olkopf}},
  title   = {{SVMs}\,---\,a practical consequence of learning theory},
  journal = {IEEE Intelligent Systems},
  year    = {1998},
  volume  = {13},
  pages   = {18--21},
  note    = {In: Trends and Controversies\,---\,Support Vector Machines},
  issue   = {4}
}

@incollection{Scholkopf98b,
  author    = {B.~{Sch\"olkopf}},
  title     = {Support-{V}ektor-{L}ernen},
  booktitle = {Ausgezeichnete Informatikdissertationen 1997},
  publisher = {Teubner},
  year      = {1998},
  editor    = {G.\ Hotz and H.~Fiedler and P.~Gorny and W.~Grass and S.
               {H\"olldobler} and I.~O.~Kerner and R.~Reischuk},
  pages     = {135--150},
  address   = {Stuttgart}
}

@book{Schone80,
  title     = {Orientierung im Raum},
  publisher = {Wissenschaftliche Verlagsgesellschaft mbH},
  year      = {1980},
  author    = {H.~Sch{\"o}ne},
  address   = {Stuttgart}
}

@article{SchPlaShaSmoetal01,
  author  = {B.~Sch\"olkopf and J.~Platt and J.~Shawe-Taylor and A. J.
             Smola and R.~C.~Williamson},
  title   = {Estimating the support of a high-dimensional
             distribution},
  journal = {Neural Comput.},
  year    = {2001},
  volume  = {13},
  pages   = {1443-1471},
  number  = {7}
}

@techreport{SchPlaShaSmoetal99,
  author      = {B.~Sch\"olkopf and J.~Platt and J.~Shawe-Taylor and A. J.
                 Smola and R.~C.~Williamson},
  title       = {Estimating the support of a high-dimensional
                 distribution},
  institution = {Microsoft Research},
  year        = {1999},
  type        = {{TR}},
  number      = {87},
  address     = {Redmond, WA},
  note        = {http://www.research.microsoft.com/scripts/pubs/view.asp?TR\_ID=MSR-TR-99-87}
}

@techreport{SchPlaSmo00,
  author      = {B.~Sch\"olkopf and J.~Platt and A. J. Smola},
  title       = {Kernel method for percentile feature extraction},
  institution = {Microsoft Research},
  year        = {2000},
  type        = {{TR}},
  number      = {22},
  address     = {Redmond, WA}
}

@article{Schraudolph02,
  author  = {Nicol N. Schraudolph},
  title   = {Fast Curvature Matrix-Vector Products for Second-Order
             Gradient Descent},
  journal = {Neural Computation},
  year    = {2002},
  volume  = {14},
  pages   = {1723-1738},
  number  = {7}
}

@inproceedings{Schraudolph99,
  author    = {Nicol N. Schraudolph},
  title     = {Local Gain Adaptation in Stochastic Gradient Descent},
  booktitle = {Proc.\ Intl.\ Conf.\ Artificial Neural Networks},
  year      = {1999},
  pages     = {569-574},
  address   = {Edinburgh, Scotland},
  publisher = {IEE, London}
}

@inproceedings{Schraudolph99b,
  author    = {Nicol N. Schraudolph},
  title     = {Online Learning with Adaptive Local Step Sizes},
  booktitle = {Neural Nets\,---\,WIRN Vietri-99: the 11th Italian
               Workshop on Neural Nets},
  year      = {1999},
  editor    = {Maria Marinaro and Roberto Tagliaferri},
  series    = {Perspectives in Neural Computing},
  pages     = {151-156},
  address   = {Vietri sul Mare, Salerno, Italy},
  publisher = {Springer Verlag, Berlin}
}

@book{Schrijver03,
  author    = {A. Schrijver},
  title     = {Combinatorial Optimization},
  year      = 2003,
  series    = {Algorithms and Combinatorics},
  volume    = 24,
  publisher = {Springer}
}

@book{Schrijver86,
  title     = {Theory of Linear and Integer Programming},
  publisher = {John Wiley \& Sons},
  year      = {1986},
  author    = {Alexander Schrijver},
  edition   = {1st}
}

@inproceedings{SchRouBac11,
  author    = {M. Schmidt and N. Le Roux and F. Bach},
  title     = {Convergence Rates of Inexact Proximal-Gradient Methods for
               Convex Optimization},
  booktitle = nips,
  year      = {2011}
}

@article{SchRouBac16,
  author  = {Mark Schmidt and Nicolas Le Roux and Francis Bach},
  title   = {Minimizing Finite Sums with the Stochastic Average
             Gradient},
  journal = {Mathematical Programming},
  year    = {2016}
}

@article{SchSel96,
  author  = {Robert E. Schapire and Linda M. Sellie},
  title   = {Learning sparse multivariate polynomials over a field with
             queries and counterexamples},
  journal = {J. of Comput. Syst. Sci.},
  year    = {1996},
  volume  = {52},
  pages   = {201--213},
  number  = {2},
  note    = {Earlier version in 6th COLT, 1993}
}

@techreport{SchShaSmoWil99,
  author      = {B.~Sch\"olkopf and J.~Shawe-Taylor and A. J. Smola and
                 R.~C.~Williamson},
  title       = {Generalization Bounds via Eigenvalues of the {G}ram
                 matrix},
  institution = {NeuroCOLT},
  year        = {1999},
  number      = {99-035},
  note        = {Available from http:/$\!$/www.kernel-machines.org},
  opttype     = { {Technical} {Report}}
}

@inproceedings{SchShaSmoWil99b,
  author    = {B.~Sch\"olkopf and J.~Shawe-Taylor and A. J. Smola and
               R.~C.~Williamson },
  title     = {Kernel-Dependent Support Vector Error Bounds},
  booktitle = {Ninth International Conference on Artificial Neural
               Networks},
  year      = {1999},
  series    = {Conference Publications No.~470},
  pages     = {103--108},
  address   = {London},
  publisher = {IEE}
}

@inproceedings{SchSimSmoVap98,
  author    = {B.~{Sch\"olkopf} and P.~Simard and A. J. Smola and
               V.~Vapnik},
  title     = {Prior knowledge in support vector kernels},
  booktitle = {Advances in Neural Information Processing Systems 10},
  year      = {1998},
  editor    = {M.~I.~Jordan and M.~J.~Kearns and S.~A.~Solla},
  pages     = {640--646},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{SchSin00,
  author    = {Robert E. Schapire and Y. Singer},
  title     = {BoosTexter: {A} Boosting-based System for Text
               Categorization},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {39},
  pages     = {135--168},
  number    = {2/3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{SchSin98,
  author    = {R.~Schapire and Y.~Singer},
  title     = {Improved boosting algorithms using confidence-rated
               predictions},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {1998},
  pages     = {80--91},
  address   = {New York},
  publisher = {ACM Press}
}

@article{SchSin99,
  author    = {Robert E. Schapire and Y. Singer},
  title     = {Improved Boosting Algorithms Using Confidence-rated
               Predictions},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {37},
  pages     = {297--336},
  number    = {3},
  comment   = {first in 11th COLT '98},
  publisher = {Kluwer Academic Publishers, Boston}
}

@book{SchSmo02,
  title     = {Learning with Kernels},
  publisher = {{MIT} Press},
  year      = {2002},
  author    = {B. Sch{\"o}lkopf and A. Smola},
  address   = {Cambridge, MA}
}

@inproceedings{SchSmo02b,
  author    = {B. Sch{\"o}lkopf and A. J. Smola},
  title     = {Support Vector Machines and Kernel Algorithms},
  booktitle = {The Handbook of Brain Theory and Neural Networks},
  year      = {2002},
  editor    = {M.~A.~Arbib},
  pages     = {1119--1125},
  publisher = {{MIT} Press}
}

@incollection{SchSmo03,
  author    = {B. Sch{\"o}lkopf and A. J. Smola},
  title     = {Support Vector Machines},
  booktitle = {The Handbook of Brain Theory and Neural Networks},
  publisher = {{MIT} Press},
  year      = {2003},
  editor    = {M. A. Arbib},
  pages     = {1119--1125},
  edition   = {2nd}
}

@inproceedings{SchSmo03b,
  author    = {B. Sch{\"o}lkopf and A. J. Smola},
  title     = {Support Vector Machines and Kernel Algorithms},
  booktitle = {Encyclopedia of Biostatistics},
  year      = {2003},
  editor    = {P. Armitage and T. Colton},
  publisher = {John Wiley and Sons},
  note      = {in press}
}

@inproceedings{SchSmo03c,
  author    = {B. Sch{\"o}lkopf and A. J. Smola},
  title     = {A Short Introduction to Learning with Kernels},
  booktitle = {Machine Learning, the Summer School, Australian National
               University},
  year      = {2003},
  editor    = {S. Mendelson and A. J. Smola},
  pages     = {41--64},
  publisher = {Springer}
}

@techreport{SchSmoMul96,
  author      = {B.~{Sch\"olkopf} and A. J. Smola and K.-R.~{M\"uller}},
  title       = {Nonlinear component analysis as a kernel Eigenvalue
                 problem},
  institution = {Max-Planck-Institut {f\"ur} biologische Kybernetik},
  year        = {1996},
  number      = {44}
}

@inproceedings{SchSmoMul97,
  author    = {B.~{Sch\"olkopf} and A. J. Smola and K.-R.~{M\"uller}},
  title     = {Kernel principal component analysis},
  booktitle = {Artificial Neural Networks {ICANN'97}},
  year      = {1997},
  editor    = {W.~Gerstner and A.~Germond and M.~Hasler and
               J.-D.~Nicoud},
  volume    = {1327},
  series    = {Lecture Notes in Computer Science},
  pages     = {583--588},
  address   = {Berlin},
  publisher = {Springer-Verlag}
}

@article{SchSmoMul98,
  author  = {B.~{Sch\"olkopf} and A. J. Smola and K.-R.~{M\"uller}},
  title   = {Nonlinear component analysis as a kernel Eigenvalue
             problem},
  journal = {Neural Comput.},
  year    = {1998},
  volume  = {10},
  pages   = {1299--1319},
  issue   = {5}
}

@incollection{SchSmoMul99,
  author    = {B.~{Sch\"olkopf} and A. J. Smola and K.-R.~{M\"uller}},
  title     = {Kernel Principal Component Analysis},
  booktitle = {Advances in Kernel Methods\,---\,Support Vector Learning},
  publisher = {{MIT} Press},
  year      = {1999},
  editor    = {B.~Sch{\"o}lkopf and C.~J.~C.~Burges and A. J. Smola},
  pages     = {327--352},
  address   = {Cambridge, MA}
}

@inproceedings{SchSmoMulBuretal98,
  author    = {B.~{Sch\"olkopf} and A. J. Smola and K.-R.~{M\"uller} and
               C.~Burges and V.~Vapnik},
  title     = {Support Vector methods in learning and feature
               extraction},
  booktitle = {the Ninth Australian Conference on Neural Networks},
  year      = {1998},
  editor    = {T.~Downs and M.~Frean and M.~Gallagher},
  pages     = {72--78},
  address   = {Brisbane, Australia},
  publisher = {University of Queensland},
  comment   = {Invited talk}
}

@article{SchSmoMulBuretal98b,
  author  = {B.~{Sch\"olkopf} and A. J. Smola and K.-R.~{M\"uller} and
             C.~Burges and V.~Vapnik},
  title   = {Support Vector methods in learning and feature
             extraction},
  journal = {Australian Journal on Intelligent Information Processing
             Systems},
  year    = {1998},
  note    = {Special issue with selected papers from ACNN'98}
}

@article{SchSmoWilBar00,
  author  = {B.~{Sch\"olkopf} and A. J. Smola and R.~C.~Williamson and
             P.~L.~Bartlett},
  title   = {New Support Vector Algorithms},
  journal = {Neural Computation},
  year    = {2000},
  volume  = {12},
  pages   = {1207--1245},
  issue   = {5}
}

@techreport{SchSmoWilBar98,
  author      = {B.~{Sch\"olkopf} and A. J. Smola and R.~C.~Williamson and
                 P.~L.~Bartlett},
  title       = {New Support Vector Algorithms},
  institution = {Royal Holloway College},
  year        = {1998},
  type        = {{NeuroCOLT} {Technical} {Report}},
  number      = {NC-TR-98-031},
  address     = {University of London, UK},
  note        = {Appeared in {\em Neural Computation}, 2000.}
}

@inproceedings{SchSou00,
  author    = {Dale Schuurmans and Finnegan Southey},
  title     = {An Adaptive Regularization Criterion for Supervised
               Learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {847--854},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@unpublished{SchSunBurGiretal96,
  author = {B.~{Sch\"olkopf} and K.~Sung and C.~Burges and F.~Girosi
            and P.~Niyogi and T.~Poggio and V.~Vapnik},
  title  = {Comparing support vector machines with {G}aussian kernels
            to radial basis function classifiers},
  note   = {A.I.~Memo No.~1599, Massachusetts Institute of Techology},
  year   = {1996}
}

@article{SchSunBurGiretal97,
  author  = {B.~{Sch\"olkopf} and K.~Sung and C.~Burges and F.~Girosi
             and P.~Niyogi and T.~Poggio and V.~Vapnik},
  title   = {Comparing support vector machines with {Gaussian} kernels
             to radial basis function classifiers},
  journal = {IEEE Transactions on Signal Processing},
  year    = {1997},
  volume  = {45},
  pages   = {2758--2765},
  issue   = {11}
}

@article{SchTakBle94,
  author  = {J.~C.~Schouten and F.~Takens and C.~M.~van den Bleek},
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  year    = {1994},
  volume  = {50},
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  number  = {3}
}

@inproceedings{SchTre03,
  author    = {A. Schwaighofer and V. Tresp},
  title     = {Transductive and inductive methods for approximate
               Guassian process regression},
  booktitle = {Neural Information Processing Systems},
  year      = {2003},
  publisher = {{MIT} Press}
}

@book{SchTsuVer04,
  title     = {Kernel Methods in Computational Biology},
  publisher = {MIT Press},
  year      = {2004},
  author    = {B. Sch{\"o}lkopf and K. Tsuda and J.-P. Vert},
  address   = {Cambridge, MA}
}

@inproceedings{Schuler99,
  author    = {Rainer Schuler},
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  booktitle = {Proc.~16th Annual Symposium on Theoretical Aspects of
               Computer Science - STACS '99},
  year      = {1999},
  volume    = {1563},
  series    = {Lecture Notes in Computer Science},
  pages     = {434--443},
  publisher = {Springer-Verlag}
}

@article{schultz2017goal,
  title  = {Goal-Predictive Robotic Teleoperation from Noisy Sensors},
  author = {Schultz, Christopher and Gaurav, Sanket and Monfort,
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  year   = {2017}
}

@inproceedings{Schulze-Kreme93,
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  pages     = {262--279},
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@book{Schumaker81,
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@incollection{Schumaker85,
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  author    = {Dale Schuurmans and Lyle H.~Ungar and Dean P.~Foster},
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  year      = {1997},
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@inproceedings{Schuurmans00,
  author    = {Dale Schuurmans},
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  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A.~Solla and T.~K.~Leen and K.-R.~M{\"u}ller},
  pages     = {596--602},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@phdthesis{Schuurmans96,
  author  = {Dale Eric Schuurmans},
  title   = {Effective Classification Learning},
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  year    = {1996},
  address = {Toronto, Canada}
}

@inproceedings{Schuurmans97,
  author    = {D.~Schuurmans},
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  booktitle = {the 14th National Conference on Artificial Intelligence
               and 9th Innovative Applications of Artificial Intelligence
               Conference},
  year      = {1997},
  pages     = {552--558},
  address   = {Menlo Park},
  publisher = {{AAAI} Press}
}

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@article{SchWar96,
  author    = {Robert E. Schapire and M. K. Warmuth},
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@article{Schwartz64b,
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  volume  = {13},
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@unpublished{Schwartz88,
  author = {J.~Schwartz},
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@inproceedings{SchWilSmoSha99,
  author    = {B.~Sch\"olkopf and R.~C.~Williamson and A. J. Smola and J.
               Shawe-Taylor},
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  year      = {1999},
  editor    = {J.~Buhmann and W.~Maass and H.~Ritter and N.~Tishby},
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@inproceedings{SchWilSmoShaetal00,
  author    = {B.\ Sch{\"o}lkopf and R.~C.~Williamson and A. J. Smola and
               J.~Shawe-Taylor and J.~C.~Platt},
  title     = {Support Vector Method for Novelty Detection},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A.~Solla and T.~K.~Leen and K.-R.~M{\"u}ller},
  pages     = {582--588},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@book{SchWol99,
  title     = {Topological Vector Spaces},
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@inproceedings{SchWonMooRie99,
  author    = {Jeff Schneider and Weng-Keen Wong and Andrew Moore and
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@inproceedings{SchYuAbe06,
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@inproceedings{SchYuGue07,
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               Statistics (AIstats)},
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  year      = {2007}
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@article{SchZhaWie97,
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@article{ScoMar93,
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@book{Searle71,
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@inproceedings{Sears07,
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  year      = {2007}
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@phdthesis{Sears08,
  author = {Timothy D. Sears},
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  year   = 2008
}

@inproceedings{SeaSun07,
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               Maximum Entropy Methods in Science and Engineering},
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  title     = {Induced Semantics for Undirected Graphs},
  year      = {2007}
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@inproceedings{SeaVis06,
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  editor    = {B. {Sch\"olkopf} and J. Platt and T. Hofmann},
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  publisher = {{MIT} Press},
  note      = {submitted}
}

@inproceedings{Sebag94,
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@inproceedings{Sebag96,
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@inproceedings{SebNoc00,
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@article{SebRou00,
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@techreport{Seeger00,
  author      = {M. Seeger},
  title       = {Relationships between Gaussian Processes, Support Vector
                 machines and Smoothing Splines},
  institution = {University of Edinburgh},
  year        = {2000}
}

@inproceedings{Seeger00b,
  author    = {M. Seeger},
  title     = {Bayesian Model Selection for Support Vector Machines,
               Gaussian Processes and Other Kernel Classifiers},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A. Solla and T.~K. Leen and K.-R. MÂÃ¼ller},
  volume    = {12},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@mastersthesis{Seeger99,
  author  = {M.~Seeger},
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  pages     = {5--9},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{ShaIde06,
  author  = {R. Sharan and T. Ideker},
  title   = {Modeling Cellular Machinery Through Biological Network
             Comparison},
  journal = {Nature Biotechnology},
  volume  = {24},
  number  = {4},
  pages   = {427--433},
  month   = {Apr},
  year    = {2006}
}

@article{ShaIdeKelShaetal05,
  author  = {R. Sharan and T. Ideker and B. Kelley and R. Shamir and R.
             M. Karp},
  title   = {Identification of protein complexes by comparative
             analysis of yeast and bacterial protein interaction data},
  journal = {J Comput Biol},
  volume  = {12},
  number  = {6},
  pages   = {835-846},
  month   = {Jul},
  year    = {2005}
}

@inproceedings{ShaKae98,
  author    = {Hagit Shatkay and Leslie P. Kaelbling},
  title     = {Heading in the right direction},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {531--539},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{ShaKar00,
  author    = {J.~Shawe-Taylor and G.~Karakoulas},
  title     = {Towards a strategy for boosting regressors},
  booktitle = {Advances in Large Margin Classifiers},
  year      = {2000},
  editor    = {A. J. Smola and P.~L.~Bartlett and B.~Sch{\"o}lkopf and
               D.~Schuurmans},
  pages     = {247--258},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{ShaKuhEsf17,
  title   = {Regularization via mass transportation},
  author  = {Shafieezadeh-Abadeh, Soroosh and Kuhn, Daniel and
             Esfahani, Peyman Mohajerin},
  journal = {arXiv:1710.10016},
  year    = {2017}
}

@inproceedings{ShaKuhEsf1715,
  title     = {Distributionally Robust Logistic Regression},
  author    = {Shafieezadeh-Abadeh, Soroosh and Mohajerin Esfahani,
               Peyman Mohajerin and Kuhn, Daniel},
  booktitle = nips,
  year      = {2015}
}

@phdthesis{Shalev-Shwartz07,
  author = {Shai Shalev-Shwartz},
  title  = {Online Learning: Theory, Algorithms, and Applications},
  school = {The Hebrew University of Jerusalem},
  year   = 2007,
  month  = {July}
}

@inproceedings{Shalev16,
  author    = {Shai Shalev-Shwartz},
  title     = {{SDCA} without Duality, Regularization, and Individual
               Convexity},
  booktitle = icml,
  year      = {2016}
}

@article{Shalevetal11,
  author  = {Shai Shalev-Shwartz and Yoram Singer and Nathan Srebro and
             Andrew Cotter},
  title   = {{Pegasos: Primal Estimated sub-GrAdient SOlver for SVM}},
  journal = {Mathematical Programming, Series B},
  year    = {2011},
  volume  = {127},
  number  = {1},
  pages   = {3--30}
}

@article{ShalevShwartzetal10,
  author  = {Shai Shalev-{S}hwartz and Nathan Srebro and Tong Zhang},
  title   = {Trading Accuracy for Sparsity in Optimization Problems
             with Sparsity Constraints},
  journal = {SIAM Journal on Optimization},
  year    = {2010},
  volume  = {20},
  pages   = {2807--2832},
  number  = {6}
}

@inproceedings{ShalevShwartzetal11,
  author    = {Shai Shalev-{S}hwartz and Alon Gonen and Ohad Shamir},
  title     = {Large-Scale Convex Minimization with a Low-{R}ank
               Constraint},
  booktitle = icml,
  year      = {2011}
}

@article{ShalevShwartzSinger10,
  author  = {Shai Shalev-Shwartz and Yoram Singer},
  title   = {On the Equivalence of Weak Learnability and Linear
             Separability: New Relaxations and Efficient Boosting
             Algorithms},
  journal = {Machine Learning},
  year    = {2010},
  volume  = {80},
  pages   = {141--163}
}

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  author  = {E. Shamir},
  title   = {A remark on discovery algorithms for grammars},
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  volume  = {5},
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}

@inproceedings{ShamirZhang13,
  author    = {Ohad Shamir and Tong Zhang},
  title     = {Stochastic Gradient Descent for Non-Smooth Optimization:
               Convergence Results and Optimal Averaging Schemes},
  booktitle = icml,
  year      = {2013}
}

@article{ShaMooTow91,
  author    = {Jude W. Shavlik and Raymond J. Mooney and Geoffrey G.
               Towell},
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  volume    = {6},
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}

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  author  = {R.~Shadmehr and F.~Mussa-Ivaldi},
  title   = {Adaptive representation of dynamics during learning of a
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  volume  = {14},
  pages   = {3208--322}
}

@article{ShaMusBiz93,
  author  = {R.~Shadmehr and F.~Mussa-Ivaldi and E.~Bizzi},
  title   = {Postural Force Fields of the Human Arm and their Role in
             Generating Multi-joint Movements},
  journal = {The Journal of Neuroscience},
  year    = {1993},
  volume  = {13},
  pages   = {45--62}
}

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  author  = {C.~E.~Shannon},
  title   = {A Mathematical Theory of Communication},
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  year    = {1948},
  volume  = {27},
  pages   = {379--423, 623--656}
}

@inproceedings{ShaPer03,
  author    = {F. Sha and F. Pereira},
  title     = {Shallow Parsing with Conditional Random Fields},
  booktitle = {HLT-NAACL},
  year      = {2003},
  pages     = {213-220},
  publisher = {Association for Computational Linguistics},
  address   = {Edmonton, Canada}
}

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  author  = {H.~S.~Shapiro},
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}

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  author  = {N. Shapiro},
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  volume  = {36},
  pages   = {342}
}

@inproceedings{Shapiro81,
  author    = {E. Shapiro},
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  year      = {1981}
}

@techreport{Shapiro81b,
  author      = {E. Y. Shapiro},
  title       = {Inductive Inference of Theories From Facts},
  institution = {Yale University Department of Computer Science},
  year        = {1981},
  number      = {Research Report 192},
  month       = feb,
  comment     = {Uses Horn clauses as representation and backtracing as a
                 method to discover axiom system for a given set of
                 examples}
}

@book{Shapiro83,
  title     = {Algorithmic Program Debugging},
  publisher = {{MIT} Press},
  year      = {1983},
  author    = {E. Y. Shapiro},
  address   = {Cambridge, MA}
}

@book{Shapiro87,
  title     = {Encyclopedia of Artificial Intelligence},
  publisher = {John Wiley and Sons},
  year      = {1987},
  author    = {S.~C. Shapiro},
  volume    = {1},
  address   = {New York}
}

@article{Shapiro88,
  author = {Shapiro, E.},
  title  = {Systolic programming: a paradigm of parallel processing},
  book   = {Concurrent Prolog},
  year   = {1988}
}

@article{Sharma00,
  author  = {Arun Sharma},
  title   = {Editorial},
  journal = {Theoret. Comput. Sci.},
  year    = {2000},
  volume  = {241},
  pages   = {1--2},
  number  = {1--2},
  note    = {Special issue for ALT '96}
}

@phdthesis{Sharma90,
  author = {A. Sharma},
  title  = {Program Size and Teams for Computational Learning},
  school = {SUNY at Buffalo},
  year   = {1990}
}

@inproceedings{ShaSch95,
  author    = {Eli Shamir and Clara Schwartzman},
  title     = {Learning by Extended Statistical Queries and its Relation
               to {PAC} Learning},
  booktitle = {Computational Learning Theory: EuroColt '95},
  year      = {1995},
  pages     = {357--366},
  publisher = {Springer-Verlag}
}

@inproceedings{ShaShaFah01,
  author    = {J. Shavlik and M. Shavlik and M. Fahland},
  title     = {Evaluating Software Sensors for Actively Profiling Windows
               2000 Computer Users},
  booktitle = {Recent Advances in Intrusion Detection {RAID}},
  year      = {2001},
  series    = {LNCS},
  publisher = {Springer}
}

@article{ShaShaSri11,
  author  = {Shalev-Shwartz, S. and Shamir, O. and Sridharan, K.},
  title   = {Learning Kernel-Based Halfspaces with the 0-1 Loss},
  journal = {SIAM Journal on Computing},
  volume  = {40},
  number  = {6},
  pages   = {1623-1646},
  year    = {2011}
}

@techreport{Shashua91,
  author      = {A.~Shashua},
  title       = {Correspondence and Affine Shape from two Orthographic
                 Views: Motion and Recognition},
  institution = {Artificial Intelligence Laboratory, Massachusetts
                 Institute of Technology},
  year        = {1991},
  type        = {{A.I.~Memo 1327}}
}

@phdthesis{Shashua92,
  author = {A.~Shashua},
  title  = {Geometry and Photometry in 3{D} visual recognition},
  school = {Artificial Intelligence Laboratory, Massachusetts
            Institute of Technology},
  year   = {1992},
  month  = nov
}

@inproceedings{Shashua92b,
  author    = {A.~Shashua},
  title     = {Illumination and View Position in {3D} Visual
               Recognition},
  booktitle = {Advances in Neural Information Processing Systems 4},
  year      = {1992},
  editor    = {J.~E.~Moody and S.~J.~Hanson and R.~Lippmann},
  pages     = {404--411},
  publisher = {Morgan Kaufmann Publishers}
}

@article{Shashua99,
  author  = {A.~Shashua},
  title   = {On the Relationship Between the Support Vector Machine for
             Classification and Sparsified Fisher's Linear
             Discriminant},
  journal = {Neural Processing Letters},
  year    = {1999},
  volume  = {9},
  pages   = {129--139},
  number  = {2},
  month   = {April}
}

@inproceedings{ShaSin05,
  author    = {Shai Shalev-Shwartz and Y. Singer},
  title     = {A New Perspective on an Old Perceptron Algorithm},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2005},
  editor    = {P.~Auer and R.~Meir},
  number    = {3559},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {264--279},
  address   = {Bertinoro, Italy},
  month     = {June},
  publisher = {Springer-Verlag}
}

@inproceedings{ShaSin06,
  author    = {S. Shalev-Shwartz and Y. Singer},
  title     = {Online Learning meets Optimization in the Dual},
  booktitle = {Computational Learning Theory (COLT)},
  year      = {2006},
  editor    = {H.U. Simon and G. Lugosi},
  series    = {LNCS},
  publisher = {Springer},
  note      = {extended version}
}

@techreport{ShaSin07,
  author      = {S. Shalev-Shwartz and Y. Singer},
  title       = {Logarithmic Regret Algorithms for Strongly Convex Repeated
                 Games},
  institution = {School of Computer Science, Hebrew University},
  year        = 2007
}

@inproceedings{ShaSinSre07,
  author    = {Shai Shalev-Shwartz and Yoram Singer and Nathan Srebro},
  title     = {Pegasos: Primal Estimated sub-GrAdient Solver for {SVM}},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2007}
}

@article{ShaSorKuhEsf17,
  title   = {Regularization via mass transportation},
  author  = {Shafieezadeh-Abadeh, Soroosh and Kuhn, Daniel and
             Esfahani, Peyman Mohajerin},
  journal = {arXiv preprint arXiv:1710.10016},
  year    = {2017}
}

@inproceedings{ShaSre08,
  author    = {S. Shalev-Schwartz and N. Srebro},
  title     = {{SVM} Optimization: Inverse Dependence on Training Set
               Size},
  booktitle = {the 25th Annual International Conference on Machine
               Learning (ICML 2008)},
  year      = 2008,
  editor    = {W. Cohen and A. McCallum and S. Roweis},
  publisher = {Omnipress}
}

@inproceedings{ShaSteVen97,
  author    = {Arun Sharma and Frank Stephan and Yuri Ventsov},
  title     = {Generalized Notions of Mind Change Complexity},
  booktitle = {Proc. 10th Annu. Conf. on Comput. Learning Theory},
  year      = {1997},
  pages     = {96--108},
  publisher = {ACM Press, New York, NY}
}

@article{ShaSutKelKuhetal05,
  author  = {R. Sharan and S. Suthram and R. M. Kelley and T. Kuhn and
             S. McCuine and P. Uetz and T. Sittler and R. M. Karp and T.
             Ideker},
  title   = {Conserved patterns of protein interaction in multiple
             species},
  journal = {Proc Natl Acad Sci U S A},
  volume  = {102},
  number  = {6},
  pages   = {1974-1979},
  month   = {Feb},
  year    = {2005}
}

@inproceedings{ShaThe92,
  author    = {U.~Shaked and Y.~Theodor},
  title     = {${H}_{\infty}-$optimal estimation: {A} tutorial},
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  year      = {1992},
  pages     = {2278--2286},
  month     = dec,
  publisher = {IEEE},
  location  = {Tucon, AZ}
}

@inproceedings{ShaUll88,
  author    = {A.~Shashua and S.~Ullman},
  title     = {Structural saliency},
  booktitle = {the International Conference on Computer Vision},
  year      = {1988},
  pages     = {482--488},
  address   = {Washington D.C.},
  publisher = {IEEE}
}

@article{Shavlik90,
  author    = {Jude W. Shavlik},
  title     = {Acquiring Recursive and Iterative Concepts with
               Explanation-Based Learning},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  pages     = {39--40},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Shavlik94,
  author    = {Jude W. Shavlik},
  title     = {Combining Symbolic and Neural Learning},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {14},
  pages     = {321--331},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Shavlik94b,
  author  = {Jude Shavlik},
  title   = {Combining symbolic and neural learning, extended
             abstract},
  journal = {Machine Learning},
  year    = {1994},
  volume  = {14},
  pages   = {321--331},
  number  = {3}
}

@inproceedings{ShaVol88,
  author    = {G. Shackelford and D. Volper},
  title     = {Learning $k$-{DNF} with Noise in the Attributes},
  booktitle = {Proc. 1st Annu. Workshop on Comput. Learning Theory},
  year      = {1988},
  pages     = {97--103},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@article{shaw1988using,
  title     = {Using an expert system with inductive learning to evaluate
               business loans},
  author    = {Shaw, Michael J and Gentry, James A},
  journal   = {Financial Management},
  pages     = {45--56},
  year      = {1988},
  publisher = {JSTOR}
}

@inproceedings{Shawe-Taylor95,
  author    = {John Shawe-Taylor},
  title     = {Sample sizes for sigmoidal neural networks},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {258--264},
  publisher = {ACM Press, New York, NY}
}

@techreport{Shawe-Taylor96,
  author      = {John Shawe-Taylor},
  title       = {Confidence Estimates of Classification Accuracy on New
                 Examples},
  institution = {Royal Holloway, University of London},
  year        = {1996},
  note        = {NC2-TR-1996-054}
}

@inproceedings{Shawe-Taylor97,
  author    = {John Shawe-Taylor},
  title     = {Confidence estimates of classification accuracy on new
               examples},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  pages     = {260--271},
  publisher = {Springer-Verlag}
}

@article{Shawe-Taylor98,
  author  = {John Shawe-Taylor},
  title   = {Classification Accuracy Based on Observed Margin},
  journal = {Algorithmica},
  year    = {1998},
  volume  = {22},
  pages   = {157--172},
  number  = {1/2}
}

@article{Shawe-Taylor99,
  author    = {John Shawe-Taylor},
  title     = {Introducing the Special Issue of Machine Learning Selected
               from Papers Presented at the 1997 Conference on
               Computational Learning Theory, {COLT}'97},
  journal   = {Machine Learning},
  year      = {1999},
  volume    = {35},
  pages     = {191--192},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{ShaWil02,
  author    = {Shawe-Taylor, John and Williams, Christopher K. I.},
  title     = {The Stability of Kernel Principal Components Analysis and
               Its Relation to the Process Eigenspectrum},
  booktitle = nips,
  year      = {2002}
}

@techreport{ShaWil97,
  author      = {John Shawe-Taylor and R.~C.~Williamson},
  title       = {A {PAC} Analysis of a {B}ayesian Estimator},
  institution = {Royal Holloway, University of London},
  year        = {1997},
  note        = {NC2-TR-1997-013}
}

@inproceedings{ShaWil99,
  author    = {J.~Shawe-Taylor and R.~C.~Williamson},
  title     = {Generalization Performance of Classifiers in Terms of
               Observed Covering Numbers},
  booktitle = {the European Conference on Computational Learning Theory},
  year      = {1999},
  editor    = {P.~Fischer and H.~U.~Simon},
  volume    = {1572},
  series    = {LNAI},
  pages     = {285--300},
  address   = {Berlin},
  publisher = {Springer}
}

@article{ShaWilCriKan05,
  author  = {J. {Shawe-Taylor} and C. Williams and N. Cristianini and
             J. Kandola},
  title   = {On the Eigenspectrum of the {G}ram Matrix and the
             Generalisation Error of Kernel {PCA}},
  journal = {IEEE Transactions on Information Theory},
  year    = {2005},
  volume  = {51},
  number  = {7},
  pages   = {2510--2522}
}

@article{ShaWilSchSmo00,
  author  = {J.~Shawe-Taylor and R.~C.~Williamson and B.~Sch\"olkopf
             and A. J. Smola},
  title   = {Sample-based generalization bounds},
  journal = {IEEE Transactions on Information Theory},
  year    = {2000},
  note    = {Submitted.}
}

@article{ShaWilSchSmo99,
  author  = {J. Shawe-Taylor and R.~C.~Williamson and B. Sch{\"o}lkopf
             and A. J. Smola},
  title   = {Sample-based generalization bounds},
  journal = {IEEE Transactions on Information Theory},
  year    = {1999},
  note    = {Submitted.}
}

@article{ShaYamNeg18,
  title   = {Understanding adversarial training: Increasing local
             stability of supervised models through robust optimization},
  journal = {Neurocomputing},
  volume  = {307},
  pages   = {195--204},
  year    = {2018},
  author  = {Uri Shaham and Yutaro Yamada and Sahand Negahban}
}

@inproceedings{ShaZem99,
  author    = {N. Shavit and A. Zemach},
  title     = {Scalable Concurrent Priority Queue Algorithms},
  booktitle = {In the 18th Annual ACM Symposium on Principals of
               Distributed Computing (PODC), Atlanta},
  year      = {1999},
  pages     = {113--122}
}

@article{ShaZha13,
  author  = {Shai Shalev-Shwartz and Tong Zhang},
  title   = {Stochastic Dual Coordinate Ascent Methods for Regularized
             Loss Minimization},
  journal = jmlr,
  year    = {2013},
  volume  = {14},
  pages   = {567--599}
}

@inproceedings{ShaZha14,
  author    = {Shai Shalev-Shwartz and Tong Zhang},
  title     = {Accelerated Proximal Stochastic Dual Coordinate Ascent for
               Regularized Loss Minimization},
  booktitle = icml,
  year      = {2014}
}

@inproceedings{SheBor10,
  title     = {Fast subtree kernels on graphs},
  author    = {Nino Shervashidze and Karsten Borgwardt},
  year      = {2010},
  booktitle = {Neural Information Processing Systems},
  url       = {http://books.nips.cc/papers/files/nips22/NIPS2009_0533.pdf}
}

@article{SheBroHuaGet17,
  title   = {Hinge-Loss Markov Random Fields and Probabilistic Soft
             Logic},
  author  = {SherBach, SH and Broecheler, M and Huang, B and Getoor,
             L},
  journal = jmlr,
  volume  = {18},
  year    = {2017},
  pages   = {1--67}
}

@inproceedings{SheHup06,
  author    = {H. Shen and K. H{\"u}per},
  title     = {Local Convergence Analysis of {FastICA}},
  booktitle = {6th international conference on ICA and BSS },
  pages     = {893--900},
  year      = {2006},
  volume    = {3889},
  series    = {Lecture Notes in Computer Science},
  address   = {Heidelberg},
  publisher = {Springer}
}

@inproceedings{SheHup06b,
  author    = {H. Shen and K. H{\"u}per},
  title     = {Newton-like methods for parallel independent component
               analysis},
  booktitle = {IEEE International Workshop on Machine Learning for Signal
               processing (MLSP 2006)},
  year      = {2006}
}

@inproceedings{SheHupSmo06,
  author       = {H. Shen and K. H{\"u}per and A. Smola},
  title        = {Newton-like Methods for nonparametric independent
                  component analysis},
  booktitle    = {International Conference on Neural Information
                  Processing},
  year         = {2006},
  editor       = {},
  volume       = {},
  number       = {},
  series       = {},
  pages        = {},
  address      = {},
  month        = {},
  organization = {},
  publisher    = {},
  note         = {to appear},
  abstract     = {},
  keywords     = {},
  file         = f
}

@inproceedings{SheJegGre07,
  author    = {H. Shen and S. Jegelka and A. Gretton},
  title     = {Fast Kernel {ICA} using an Approximate {N}ewton Method},
  booktitle = {AISTATS 11},
  pages     = {476--483},
  year      = {2007}
}

@article{SheJegGre09,
  author  = {Hao Shen and Stefanie Jegelka and Arthur Gretton},
  title   = {Fast Kernel-Based Independent Component Analysis},
  journal = {IEEE Transactions on Signal Processing},
  year    = 2009,
  note    = {In press}
}

@article{SheKeeBhaMur00,
  author  = {S.K. Shevade and S.S. Keerthi and C. Bhattacharyya and
             K.R.K. Murthy},
  title   = {Improvements to the {SMO} Algorithm for {SVM} Regression},
  journal = {IEEE Trans. on Neural Networks},
  year    = {2000},
  volume  = {11},
  pages   = {1188-1193},
  number  = {5}
}

@techreport{SheKeeBhaMur99,
  author      = {S.~K.~Shevade and S.S. Keerthi and C.~Bhattacharyya and
                 K.~R.~K.~Murthy},
  title       = {Improvements to {SMO} Algorithm for {SVM} Regression},
  institution = {Dept.~of Mechanical and Production Engineering,
                 Natl.~Univ.~Singapore},
  year        = {1999},
  number      = {CD-99-16},
  address     = {Singapore}
}

@article{Shelah72,
  author  = {S. Shelah},
  title   = {A combinatorial problem; stability and order for models
             and theories in infinitary languages},
  journal = {Pacific Journal of Mathematics},
  year    = {1972},
  volume  = {41},
  pages   = {247--261}
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               and Victorri, Bernard},
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               M{\"u}ller, Klaus-Robert},
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  year    = {1997},
  volume  = {26},
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@inproceedings{SinBulBul95,
  author    = {P.~Sinha and H.~H.~{B\"ulthoff} and I.~{B\"ulthoff}},
  title     = {View-based representations for biological motion
               sequences},
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  year      = {1995},
  volume    = {36},
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@inproceedings{SinRos08,
  author    = {V. Sindhwani and D. Rosenberg},
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  booktitle = icml,
  year      = {2008}
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@inproceedings{SinDom08,
  title     = {Lifted First-Order Belief Propagation},
  author    = {Parag Singla and Pedro Domingos},
  bibdate   = {2008-08-15},
  booktitle = {the Twenty-Third {AAAI} Conference on Artificial
               Intelligence, {AAAI} 2008, Chicago, Illinois, {USA}, July
               13-17, 2008},
  publisher = {AAAI Press},
  year      = {2008},
  editor    = {Dieter Fox and Carla P. Gomes},
  pages     = {1094--1099}
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@article{SinFebRosJacetal02,
  author  = {D. Singh and P. G. Febbo and K. Ross and D. G. Jackson and
             J. Manola and C. Ladd and P. Tamayo and A. A. Renshaw and
             A. V. D'Amico and J. P. Richie and E. S. Lander and M. Loda
             and P. W. Kantoff and T. R. Golub and W. R. Sellers},
  title   = {Gene expression correlates of clinical prostate cancer
             behavior},
  journal = {Cancer Cell},
  volume  = {1},
  number  = {2},
  pages   = {203-209},
  month   = {Mar},
  year    = {2002}
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@article{SinFebRosJacetal02b,
  author  = {D. Singh and P. Febbo and K. Ross and D. Jackson and J.
             Manola and C. Ladd and P. Tamayo and A. Renshaw and A.
             DAmico and J. Richie },
  title   = {Gene expression correlates of clinical prostate cancer
             behavior},
  journal = {Cancer Cell},
  volume  = {1},
  number  = {2},
  year    = {2002}
}

@article{SinFei19,
  title   = {Robustness Certificates Against Adversarial Examples for
             {ReLU} Networks},
  author  = {Sahil Singla and Soheil Feizi},
  journal = {arXiv:1902.01235},
  year    = {2019}
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  author    = {Y.~Singer},
  title     = {Leveraged Vector Machines},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A.~Solla and T.~K.~Leen and K.-R.~M{\"u}ller},
  volume    = {12},
  pages     = {610--616},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{Singer01,
  author    = {Y. Singer},
  title     = {Guest Editors' Introduction},
  journal   = {Machine Learning},
  year      = {2001},
  volume    = {43},
  pages     = {171--172},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{Singer07,
  author  = {Amit Singer},
  title   = {A remark on global positioning from local distances},
  journal = pnas,
  volume  = {105},
  pages   = {9507--9511 },
  year    = {2007}
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@book{Singer70,
  title     = {Best approximation in normed linear spaces by elements of
               linear subspaces},
  publisher = {Springer},
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  author    = {I.~Singer},
  address   = {Berlin}
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@article{Singer_MaximizationLower_1979,
  title      = {Maximization of lower semi-continuous convex functionals
                on bounded subsets of locally convex spaces. {I}:
                {Hyperplane} theorems},
  volume     = {5},
  issn       = {0095-4616, 1432-0606},
  shorttitle = {Maximization of lower semi-continuous convex functionals
                on bounded subsets of locally convex spaces. {I}},
  doi        = {10.1007/bf01442563},
  language   = {en},
  number     = {1},
  journal    = {Applied Mathematics \& Optimization},
  author     = {Singer, Ivan},
  month      = mar,
  year       = {1979},
  pages      = {349--362},
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@article{Singer_MaximizationLower_1980,
  title      = {Maximization of lower semi-continuous convex functional on
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  volume     = {3},
  issn       = {0378-6218, 1420-9012},
  shorttitle = {Maximization of lower semi-continuous convex functional on
                bounded subsets of locally convex spaces. {II}},
  doi        = {10.1007/bf03323359},
  language   = {en},
  number     = {1-2},
  journal    = {Results in Mathematics},
  author     = {Singer, Ivan},
  month      = mar,
  year       = {1980},
  pages      = {235--248},
  file       = {Singer - Maximization of lower semi-continuous convex
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@article{Singh92,
  author    = {S.~P. Singh},
  title     = {Transfer of Learning by Composing Solutions of Elemental
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  journal   = {Machine Learning},
  year      = {1992},
  volume    = {8},
  pages     = {323--339},
  publisher = {Kluwer Academic Publishers, Boston}
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@inproceedings{SinGor08,
  title     = {A Unified View of Matrix Factorization Models},
  author    = {A.P. Singh and G.J. Gordon},
  booktitle = {Machine Learning and Knowledge Discovery in Databases,
               European Conference, {ECML}/{PKDD} 2008},
  publisher = {Springer},
  year      = {2008},
  volume    = {5212},
  editor    = {W. Daelemans and B. Goethals and K. Morik},
  pages     = {358--373},
  series    = {Lecture Notes in Computer Science},
  url       = {http://dx.doi.org/10.1007/978-3-540-87481-2_24}
}

@techreport{Sinha92,
  author      = {Pawan Sinha},
  title       = {The Apparent Non-Rigidity of Rigid Objects in Motion},
  institution = {Massachusetts Institute of Technology},
  year        = {1992},
  note        = {(preprint)}
}

@article{Sinha94,
  author  = {Pawan Sinha},
  title   = {Object Recognition via Image Invariants: A Case Study},
  journal = {Investigative Ophthalmology and Visual Science},
  year    = {1994},
  volume  = {35},
  pages   = {1735--1740},
  month   = may,
  address = {Sarasota, Florida}
}

@article{Sinha95,
  author  = {Pawan Sinha},
  title   = {Learning to Perceive 3{D} Structures in 2{D} Images},
  journal = {Percpetion},
  year    = {1995a},
  volume  = {24}
}

@phdthesis{Sinha95b,
  author = {Pawan Sinha},
  title  = {Perceiving and Recognizing 3{D} Forms},
  school = {Massachussetts Institute of Technology},
  year   = {1995b}
}

@inproceedings{Sinha_CertifiableDistributional_2018,
  title     = {Certifiable distributional robustness with principled
               adversarial training},
  booktitle = {International conference on learning representations},
  author    = {Sinha, Aman and Namkoong, Hongseok and Duchi, John},
  year      = {2018},
  keywords  = {⛔ No DOI found},
  file      = {Full
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@inproceedings{SinJaaJor94,
  author    = {S. P. Singh and T. Jaakkola and M.~I.~Jordan},
  title     = {Learning without state-estimation in partially observable
               {M}arkovian decision processes},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1994},
  pages     = {284--292},
  publisher = {Morgan Kaufmann}
}

@article{SinJaaLitSze00,
  author    = {S. Singh and T. Jaakkola and M. L. Littman and Csaba
               Szepesv{\'a}ri},
  title     = {Convergence Results for Single-Step On-Policy
               Reinforcement-Learning Algorithms},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {38},
  pages     = {287--308},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
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@misc{SinKas01,
  author = {J. Sinkkonen and S. Kaski},
  title  = {Clustering based on conditional distributions in an
            auxiliary space},
  year   = {2001},
  text   = {J. Sinkkonen and S. Kaski. Clustering based on conditional
            distributions in an auxiliary space. Neural Computation,
            2001, in press.}
}

@inproceedings{SinKee06,
  author    = {V. Sindhwani and S.S. Keerthi},
  title     = {Large scale semi-supervised linear {SVMs}},
  booktitle = {SIGIR '06: the 29th annual international ACM SIGIR
               conference on Research and development in information
               retrieval},
  year      = {2006},
  pages     = {477--484},
  location  = {Seattle, Washington, USA},
  publisher = {ACM Press},
  address   = {New York, NY, USA}
}

@inproceedings{SinNamDuc18,
  title     = {Certifying Some Distributional Robustness with Principled
               Adversarial Training},
  author    = {Sinha, Aman and Namkoong, Hongseok and Duchi, John},
  booktitle = iclr,
  year      = {2018}
}

@inproceedings{SinNiyBel05,
  author    = {Vikas Sindhwani and Partha Niyogi and Mikhail Belkin},
  title     = {Beyond the point cloud: from transductive to
               semi-supervised learning.},
  year      = {2005},
  pages     = {824-831},
  editor    = {Luc De Raedt and Stefan Wrobel},
  booktitle = {Machine Learning, the Twenty-Second International
               Conference (ICML 2005), Bonn, Germany, August 7-11, 2005},
  publisher = {ACM}
}

@article{SinPog96,
  author  = {P.~Sinha and T.~Poggio},
  title   = {The role of learning in 3{D} form perception},
  journal = {Nature},
  year    = {1996},
  volume  = {In Press}
}

@inproceedings{SinPro95,
  author    = {Moninder Singh and Gregory M. Provan},
  title     = {A comparison of inductive algorithms for selective and
               non-selective {B}ayesian classifiers},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {497--505},
  publisher = {Morgan Kaufmann}
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@inproceedings{SinPro96,
  author    = {Moninder Singh and Gregory M. Provan},
  title     = {Efficient learning of selective {B}ayesian network
               classifiers},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1996},
  pages     = {453--461},
  publisher = {Morgan Kaufmann}
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@inproceedings{SinRos08,
  author    = {Vikas Sindhwani and David S. Rosenberg},
  title     = {An {RKHS} for Multi-View Learning and Manifold
               Co-Regularization},
  booktitle = icml,
  year      = {2008}
}

@article{SinSut96,
  author    = {S.P. Singh and R. S. Sutton},
  title     = {Reinforcement Learning with Replacing Eligibility Traces},
  journal   = {Machine Learning},
  year      = {1996},
  volume    = {22},
  pages     = {123--158},
  publisher = {Kluwer Academic Publishers, Boston}
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@inproceedings{SinVel00,
  author    = {Bryan Singer and Manuela Veloso},
  title     = {Learning to Predict Performance from Formula Modeling and
               Training Data},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {887--894},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{SinWarRajLam03,
  author    = {R. Singh and M. K. Warmuth and B. Raj and P. Lamere},
  title     = {Classification with Free Energy at Raised Temperatures},
  booktitle = {EuroSpeech},
  pages     = {1773--1776},
  year      = 2003,
  address   = {Geneve, Switzerland},
  month     = {September}
}

@article{SinYee94,
  author  = {S. P. Singh and R. C. Yee},
  title   = {Technical note: an upper bound on the loss from
             approximate optimal-value functions},
  journal = {Machine Learning},
  year    = {1994},
  volume  = {16},
  pages   = {227--234},
  number  = {3}
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@article{SinYee94b,
  author    = {S. P. Singh and R. C. Yee},
  title     = {An Upper Bound on the Loss from Approximate Optimal-Value
               Functions},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {16},
  pages     = {227--233},
  publisher = {Kluwer Academic Publishers, Boston}
}

@manual{Sinz06,
  author = {Fabian Sinz},
  title  = {{UniverSVM}: Support Vector Machine with Large Scale CCCP
            Functionality},
  year   = {2006},
  note   = {Software available at
            {\small\url{http://www.kyb.mpg.de/bs/people/fabee/universvm.html}}}
}

@inproceedings{SinZhoShe06,
  author    = {Debjit Sinha and Hai Zhou and Narendra V. Shenoy},
  title     = {Advances in Computation of the Maximum of a Set of Random
               Variables},
  year      = 2006,
  booktitle = {7th International Symposium on Quality Electronic Design}
}

@article{sion1958general,
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@article{SisNumShi00,
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               Shimura},
  title     = {Multistrategy Discovery and Detection of Novice Programmer
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  journal   = {Machine Learning},
  year      = {2000},
  volume    = {38},
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  number    = {1/2},
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@inproceedings{SitStr97,
  author    = {Meera Sitharam and Timothy Straney},
  title     = {Derandomized Learning of {B}oolean Functions},
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  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {100--115},
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@article{Siveretal16,
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  year    = {2016},
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  author    = {T. A. Slaman and R. M. Solovay},
  title     = {When oracles do not help},
  booktitle = {Proc. 4th Annu. Workshop on Comput. Learning Theory},
  year      = {1991},
  pages     = {379--383},
  publisher = {Morgan Kaufmann},
  annote    = {Subsumed by {\it Structure of the Degrees of Inferability}
               in COLT93}
}

@article{Slater51,
  author  = {M.~Slater},
  title   = {A Note on {M}otzkin's Transposition Theorem},
  journal = {Econometrica},
  year    = {1951},
  volume  = {19},
  pages   = {185--186}
}

@article{Sleeman94,
  author    = {D. Sleeman},
  title     = {Introduction to the Abstracts of the Invited Talks
               Presented at {ML92} Conference in Aberdeen, 1--3 July
               1992},
  journal   = {Machine Learning},
  year      = {1994},
  volume    = {14},
  pages     = {303--303},
  publisher = {Kluwer Academic Publishers, Boston}
}

@article{SleHirEllKim90,
  author    = {D. Sleeman and H. Hirsh and I. Ellery and {In-Y}ung Kim},
  title     = {Extending Domain Theories: Two Case Studies in Student
               Modeling},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  pages     = {11--37},
  publisher = {Kluwer Academic Publishers, Boston}
}

@unpublished{Sloan87,
  author = {R. H. Sloan},
  title  = {Some Notes on {C}hernoff Bounds},
  note   = {(Unpublished)},
  year   = {1987}
}

@inproceedings{Sloan88,
  author    = {R. Sloan},
  title     = {Types of noise in data for concept learning},
  booktitle = {Proc. 1st Annu. Workshop on Comput. Learning Theory},
  year      = {1988},
  pages     = {91--96},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann}
}

@phdthesis{Sloan89,
  author = {R. H. Sloan},
  title  = {Computational {L}earning {T}heory: {N}ew {M}odels and
            {A}lgorithms},
  school = {MIT},
  year   = {1989},
  note   = {Issued as MIT/LCS/TR-448.}
}

@inproceedings{Sloan92,
  author    = {R. H. Sloan},
  title     = {Corrigendum to {T}ypes of noise in data for concept
               learning},
  booktitle = {Proc. 5th Annu. Workshop on Comput. Learning Theory},
  year      = {1992},
  pages     = {450},
  publisher = {ACM Press, New York, NY}
}

@article{Sloan95,
  author  = {R. H. Sloan},
  title   = {Four types of noise in data for {PAC} Learning},
  journal = {Information Processing Letters},
  year    = {1995},
  volume  = {54},
  pages   = {157--162},
  comment = {Earlier version appeared in 1st COLT, 1988}
}

@book{SloLi91,
  title     = {Applied Nonlinear Control},
  publisher = {Prentice Hall},
  year      = {1991},
  author    = {J.~J.~Slotine and W.~Li},
  address   = {Englewood Cliffs, NJ}
}

@phdthesis{Slonim02,
  author = {N. Slonim},
  title  = {The Information Bottleneck: Theory and Applications},
  school = {The Hebrew University of Jerusalem},
  year   = {2002}
}

@inproceedings{SloTis00,
  author    = {N. Slonim and N. Tishby},
  title     = {Agglomerative Information Bottleneck},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {2000},
  editor    = {S.~A.~Solla and T.~K.~Leen and K.-R.~M{\"u}ller},
  pages     = {617--623},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{SloTis00b,
  author    = {N. Slonim and N. Tishby},
  title     = {Document Clustering using Word Clusters via the
               Information Bottleneck Method},
  booktitle = {{ACM} {SIGIR}},
  year      = {2000},
  pages     = {208--215}
}

@inproceedings{SloTis01,
  author    = {N. Slonim and N. Tishby},
  title     = {The power of word clusters for text classification},
  booktitle = {23rd European Colloquium on Information Retrieval
               Research},
  year      = {2001}
}

@inproceedings{SloTis99,
  author    = {N. Slonim and N. Tishby},
  title     = {Agglomerative Information Bottleneck},
  booktitle = {Advances in Neural Information Processing Systems 12},
  year      = {1999},
  editor    = {S.~A. Solla and T.~K. Leen and K.-R. MÂÃ¼ller},
  volume    = {12},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{SloTur94,
  author    = {R. H. Sloan and G. Tur{\'a}n},
  title     = {Learning with queries but incomplete information},
  booktitle = {Proc. 7th Annu. ACM Conf. on Comput. Learning Theory},
  year      = {1994},
  pages     = {237--245},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{SloTur97,
  author    = {Robert H. Sloan and Gy{\"o}rgy Tur{\'a}n},
  title     = {Learning from incomplete boundary queries using split
               graphs and hypergraphs},
  booktitle = {Computational Learning Theory: EuroColt '97},
  year      = {1997},
  number    = {1208},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {38--50},
  publisher = {Springer-Verlag}
}

@inproceedings{SloTur99,
  author    = {Robert H. Sloan and Gy{\"o}rgy Tur{\'a}n},
  title     = {On theory revision with queries},
  booktitle = {Proc. 12th Annu. Conf. on Comput. Learning Theory},
  year      = {1999},
  pages     = {41--52},
  publisher = {ACM Press, New York, NY}
}

@inproceedings{SmaKae00,
  author    = {William D. Smart and Leslie Pack Kaelbling},
  title     = {Practical Reinforcement Learning in Continuous Spaces},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {903--910},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@book{SmaMcL94,
  title     = {Hilbert space methods in probability and statistical
               inference},
  publisher = {John Wiley and Sons},
  year      = {1994},
  author    = {C.~G.~Small and D.~L.~McLeish},
  series    = {Probability and Mathematical Statistics},
  address   = {New York}
}

@inproceedings{SmiEis05,
  title     = {Contrastive estimation: training log-linear models on
               unlabeled data},
  author    = {Noah Smith and Jason Eisner},
  booktitle = acl,
  year      = {2005}
}

@inproceedings{SmiEis05_long,
  title        = {Contrastive estimation: Training log-linear models on
                  unlabeled data},
  author       = {Noah Smith and Jason Eisner},
  booktitle    = acl_long,
  pages        = {354--362},
  year         = {2005},
  organization = {Association for Computational Linguistics},
  url          = {http://www.cs.cmu.edu/~nasmith/papers/smith+eisner.acl05.pdf}
}

@article{SmiGas95,
  author  = {Carl H. Smith and William I. Gasarch},
  title   = {Recursion theoretic models of learning: some results and
             intuitions},
  journal = {Annals of Mathematics and Artificial Intelligence},
  year    = {1995},
  volume  = {15},
  pages   = {151--166},
  number  = {2}
}

@article{SmiHoc72,
  author  = {W.B. Smith and R.R. Hocking},
  title   = {Algorithm {AS} 53: Wishart Variate Generator},
  journal = {Applied Statistics},
  volume  = 21,
  number  = 3,
  year    = 1972,
  pages   = {341--345}
}

@article{Smirnov39,
  author  = {N.V. Smirnov},
  title   = {On the estimation of the discrepancy between empirical
             curves of distribution for two independent samples},
  journal = {Bulletin Mathematics},
  year    = {1939},
  volume  = {2},
  pages   = {3-26},
  note    = {University of Moscow}
}

@article{Smirnov44,
  author  = {N.~V.~Smirnov},
  title   = {Approximation of distribution laws of random variables on
             the basis of empirical data},
  journal = {Usp.~Mat.~Nauk},
  year    = {1944},
  volume  = {10},
  pages   = {179--206}
}

@article{Smith68,
  author  = {F.~W.~Smith},
  title   = {Pattern Classifier Design by Linear programming},
  journal = {IEEE Transactions on Computers},
  year    = {1968},
  volume  = {C-17},
  pages   = {367--372}
}

@techreport{Smith77,
  author      = {D. R. Smith},
  title       = {A class of synthesizeable {LISP} programs},
  institution = {Duke University Computer Science Dept., CS-1977-4},
  year        = {1977}
}

@phdthesis{Smith79,
  author = {C. H. Smith},
  title  = {Hierarchies of {I}dentification {C}riteria for
            {M}echanized {I}nductive {I}nference},
  school = {SUNY Buffalo},
  year   = {1979}
}

@techreport{Smith79b,
  author      = {C. H. Smith},
  title       = {An inductive inference bibliography},
  institution = {Purdue University Computer Science Dept., CSD TR 323},
  year        = {1979}
}

@article{Smith82,
  author  = {C. Smith},
  title   = {The Power of Pluralism for Automatic Program Synthesis},
  journal = {J. ACM},
  year    = {1982},
  volume  = {29},
  pages   = {1144--1165},
  annote  = {Earlier version in FOCS81}
}

@incollection{Smith82b,
  author    = {D. R. Smith},
  title     = {A survey of the synthesis of {LISP} programs from
               examples},
  booktitle = {Automatic Program Construction Techniques},
  publisher = {MacMillan Co.},
  year      = {1982}
}

@article{Smith90,
  author  = {Bruce D. Smith},
  title   = {Cone-beam tomography: recent advances and a tutorial
             review},
  journal = {Optical Engineering},
  volume  = {29},
  number  = {5},
  pages   = {524--534},
  year    = 1990
}

@inproceedings{Smith94,
  author    = {C. H. Smith},
  title     = {Three Decades of Team Learning},
  booktitle = alt-ws,
  year      = {1994},
  volume    = {872},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {211--228},
  publisher = {Springer-Verlag}
}

@inproceedings{SmiVel86,
  author    = {C. Smith and M. Velauthapillai},
  title     = {On the Inference of Programs Approximately Computing the
               Desired Function},
  booktitle = {Proc. 1st International Workshop on Analogical and
               Inductive Inference},
  year      = {1986},
  volume    = {265},
  series    = {Lecture Notes in Computer Science},
  pages     = {164--176},
  publisher = {Springer-Verlag}
}

@article{SmiVel90,
  author  = {Carl Smith and Mahendra Velauthapillai},
  title   = {On the inference of approximate programs},
  journal = {Theoret. Comput. Sci.},
  year    = {1990},
  volume  = {77},
  note    = {Earlier version in U. of MD. TR 1427, in 1985}
}

@article{SmiWieZeu97,
  author  = {C. H. Smith and R. Wiehagen and T. Zeugmann},
  title   = {Classifying Predicates and Languages},
  journal = {International Journal of Foundations of Computer Science},
  year    = {1997},
  volume  = {8},
  pages   = {15--41}
}

@inproceedings{SmoBar01,
  author    = {A. J. Smola and P.~L. Bartlett},
  title     = {Sparse Greedy {G}aussian Process Regression},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {619--625},
  publisher = {{MIT} Press}
}

@book{SmoBarSchSch00,
  title     = {Advances in Large Margin Classifiers},
  publisher = {{MIT} Press},
  year      = {2000},
  author    = {A. J. Smola and P.~L. Bartlett and B. Sch{\"o}lkopf and D.
               Schuurmans},
  address   = {Cambridge, MA}
}

@inproceedings{SmoCan04,
  author    = {A. J. Smola and S. Canu},
  title     = {Gaussian Processes for Heteroscedastic Data},
  booktitle = {Neural Information Processing Systems},
  year      = {2004},
  note      = {submitted}
}

@inproceedings{SmoEliSchWil00,
  author    = {A. J. Smola and A. Elisseeff and B. {Sch\"olkopf} and
               R.~C. Williamson},
  title     = {Entropy Numbers for Convex Combinations and {MLP}s},
  booktitle = {Advances in Large Margin Classifiers},
  year      = {2000},
  editor    = {A. J. Smola and P.~L.~Bartlett and B.~Sch{\"o}lkopf and
               D.~Schuurmans},
  pages     = {369--387},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@techreport{SmoFriSch98,
  author      = {A. J. Smola and T. Frie{\ss} and B. Sch{\"o}lkopf},
  title       = {Semiparametric Support Vector and Linear Programming
                 Machines},
  institution = {Royal Holloway College},
  year        = {1998},
  type        = {{NeuroCOLT} {Technical} {Report}},
  number      = {NC-TR-98-021},
  address     = {University of London, UK}
}

@inproceedings{SmoFriSch99,
  author    = {A. J. Smola and T. Frie{\ss} and B. {Sch\"olkopf}},
  title     = {Semiparametric support vector and linear programming
               machines},
  booktitle = {Advances in Neural Information Processing Systems 11},
  year      = {1999},
  editor    = {M.~S.~Kearns and S.~A.~Solla and D.~A.~Cohn},
  pages     = {585--591},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{SmoGreBor06,
  author    = {A. J. Smola and A. Gretton and K. Borgwardt},
  title     = {Maximum Mean Discrepancy},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2006},
  note      = {submitted}
}

@inproceedings{SmoGreSonSch07,
  author    = {A. J. Smola and A. Gretton and L. Song and B.
               {Sch\"olkopf}},
  title     = {A {H}ilbert Space Embedding for Distributions},
  booktitle = alt,
  year      = {2007},
  series    = {LNAI},
  publisher = {Springer-Verlag},
  pages     = {13--31},
  volume    = {4754}
}

@inproceedings{SmoGreSonSch07b,
  author    = {A.J. Smola and A. Gretton and L. Song and B.
               Sch{\"o}lkopf},
  title     = {A Hilbert Space Embedding for Distributions},
  booktitle = {Algorithmic Learning Theory},
  year      = {2007},
  editor    = {E.\ Takimoto},
  series    = {Lecture Notes on Computer Science},
  publisher = {Springer}
}

@inproceedings{SmoHof03,
  author    = {A. J. Smola and T. Hofmann},
  title     = {Exponential Families in Feature Space},
  booktitle = nips,
  year      = {2003},
  note      = {submitted}
}

@inproceedings{SmoKon03,
  author    = {A. J. Smola and R. Kondor},
  title     = {Kernels and Regularization on Graphs},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2003},
  editor    = {B. Sch{\"o}lkopf and M. K. Warmuth},
  series    = {Lecture Notes in Comput. Sci.},
  address   = {Heidelberg, Germany},
  pages     = {144-158},
  publisher = {Springer-Verlag}
}

@techreport{Smola01,
  author      = {A. J. Smola},
  title       = {An Analysis of Leave-One-Out Estimates for Support Vector
                 Machines},
  institution = {Australian National University},
  year        = {2001},
  note        = {in preparation}
}

@article{Smola19,
  author  = {Alex Smola},
  journal = {NeurIPS Workshop on Sets \& Partitions},
  title   = {Sets and symmetries},
  year    = {2019}
}

@mastersthesis{Smola96,
  author = {A. J. Smola},
  title  = {Regression Estimation with Support Vector Learning
            Machines},
  school = {Technische {Universit\"at} {M\"unchen}},
  year   = {1996},
  type   = {Diplomarbeit}
}

@phdthesis{Smola98,
  author = {A. J. Smola},
  title  = {Learning with Kernels},
  school = {Technische Universit\"at Berlin},
  year   = {1998},
  note   = {GMD Research Series No.~25}
}

@incollection{Smola99,
  author    = {A. J. Smola},
  title     = {Lernen mit {K}ernen},
  booktitle = {Ausgezeichnete Informatikdissertationen},
  publisher = {Teubner, Stuttgart},
  year      = {1999},
  editor    = {G.~Hotz et al.},
  pages     = {184--195}
}

@techreport{SmoMacKow02,
  author      = {A. J. Smola and J. Macnicol and A. Kowalczyk},
  title       = {Scalable Kernel Algorithms},
  institution = {Australian National University},
  year        = {2002},
  note        = {In preparation}
}

@article{SmoManSch01,
  author  = {A. J. Smola and O. Mangasarian and B. Sch\"olkopf},
  title   = {Sparse kernel feature analysis},
  journal = {Neural Computation},
  year    = {2001},
  note    = {Submitted.}
}

@techreport{SmoManSch99,
  author      = {A. J. Smola and O. Mangasarian and B. Sch\"olkopf},
  title       = {Sparse kernel feature analysis},
  institution = {Data Mining Institute, University of Madison, Wisconsin},
  year        = {1999},
  number      = {99-04},
  note        = {ftp://ftp.cs.wisc.edu/dmi/tech-reports/99-04.ps; {\em
                 Neural Computation}.~Submitted}
}

@techreport{SmoMikSch98,
  author      = {A. J. Smola and S. Mika and B. Sch\"olkopf},
  title       = {Quantization Functionals and Regularized Principal
                 Manifolds},
  institution = {Neurocolt2},
  year        = {1998},
  number      = {TR 98-028}
}

@article{SmoMikSchWil01,
  author  = {A. J. Smola and S. Mika and B. {Sch\"olkopf} and R.~C.
             Williamson},
  title   = {Regularized principal manifolds},
  journal = {Journal of Machine Learning},
  year    = {2001},
  volume  = {1},
  pages   = {179--209},
  url     = {http://www.jmlr.org}
}

@inproceedings{SmoMurSchMul98,
  author    = {A. J. Smola and N. Murata and B. {Sch\"olkopf} and K.-R.
               {M\"uller}},
  title     = {Asymptotically Optimal Choice of $\varepsilon$-Loss for
               Support Vector Machines},
  booktitle = {the International Conference on Artificial Neural
               Networks},
  year      = {1998},
  editor    = {L. Niklasson and M. {Bod\'en} and T. Ziemke},
  series    = {Perspectives in Neural Computing},
  pages     = {105--110},
  address   = {Berlin},
  publisher = {Springer}
}

@inproceedings{SmoNar10,
  author    = {A.J. Smola and S. Narayanamurthy},
  title     = {An Architecture for Parallel Topic Models},
  booktitle = {Very Large Databases (VLDB)},
  year      = 2010
}

@inproceedings{SmoOvaWil01,
  author    = {A. J. Smola and Z.~L. {\'Ov\'ari} and R.~C. Williamson},
  title     = {Regularization with dot-product kernels},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {308-314},
  publisher = {{MIT} Press}
}

@inproceedings{SmoSch00,
  author    = {A. J. Smola and B. Sch\"olkopf},
  title     = {Sparse Greedy Matrix Approximation for Machine Learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  editor    = {P. Langley},
  pages     = {911--918},
  address   = {San Francisco},
  publisher = {Morgan Kaufmann Publishers}
}

@article{SmoSch01,
  author  = {A. J. Smola and B. {Sch\"olkopf}},
  title   = {A Tutorial on Support Vector Regression},
  journal = {Statistics and Computing},
  year    = {2001},
  note    = {To Appear}
}

@inproceedings{SmoSch03,
  author    = {A. J. Smola and B. Sch{\"o}lkopf},
  title     = {Bayesian Kernel Methods},
  booktitle = {Machine Learning, the Summer School, Australian National
               University},
  year      = {2003},
  editor    = {S. Mendelson and A. J. Smola},
  pages     = {65--117},
  publisher = {Springer}
}

@article{SmoSch03b,
  author  = {A. J. Smola and B. {Sch\"olkopf}},
  title   = {A Tutorial on Support Vector Regression},
  journal = {Statistics and Computing},
  year    = {2003},
  note    = {in press}
}

@techreport{SmoSch97,
  author      = {A. J. Smola and B. {Sch\"olkopf}},
  title       = {On a Kernel-based Method for Pattern Recognition,
                 Regression, Approximation and Operator Inversion},
  institution = {GMD},
  year        = {1997},
  number      = {1064},
  comment     = {Published partially in {\em Algorithmica}}
}

@techreport{SmoSch97b,
  author      = {A. J. Smola and Bernhard Sch{\"o}lkopf},
  title       = {On a Kernel-based Method for Pattern Recognition,
                 Regression, Approximation, and Operator Inversion},
  institution = {GMD Technical Report No.~1064},
  year        = {1997},
  note        = {also appeared in Algorithmica}
}

@article{SmoSch98,
  author  = {A. J. Smola and B. Sch{\"o}lkopf},
  title   = {On a Kernel-based Method for Pattern Recognition,
             Regression, Approximation and Operator Inversion},
  journal = {Algorithmica},
  year    = {1998},
  volume  = {22},
  pages   = {211--231}
}

@inproceedings{SmoSch98b,
  author    = {A. J. Smola and B. {Sch\"olkopf}},
  title     = {From regularization operators to support vector kernels},
  booktitle = {Advances in Neural Information Processing Systems 10},
  year      = {1998},
  editor    = {M.~I.~Jordan and M.~J.~Kearns and S.~A.~Solla},
  pages     = {343--349},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@techreport{SmoSch98c,
  author      = {A. J. Smola and B. Sch{\"o}lkopf},
  title       = {A Tutorial on Support Vector Regression},
  institution = {Royal Holloway College},
  year        = {1998},
  type        = {{NeuroCOLT} {Technical} {Report}},
  number      = {NC-TR-98-030},
  address     = {University of London, UK}
}

@techreport{SmoSch98d,
  author      = {A. J. Smola and Bernhard Sch{\"o}lkopf},
  title       = {A Tutorial on Support Vector Regression},
  institution = {Royal Holloway, University of London},
  year        = {1998},
  note        = {NC-TR-1998-030}
}

@inproceedings{SmoSchMul98,
  author    = {A. J. Smola and B. {Sch\"olkopf} and K.-R. {M\"uller}},
  title     = {General cost functions for Support Vector Regression},
  booktitle = {Proc.\ of the Ninth Australian Conf.\ on Neural Networks},
  year      = {1998},
  editor    = {T.~Downs and M.~Frean and M.~Gallagher},
  pages     = {79--83},
  address   = {Brisbane, Australia},
  publisher = {University of Queensland}
}

@article{SmoSchMul98b,
  author  = {A. J. Smola and B. Sch{\"o}lkopf and K.-R. M{\"u}ller},
  title   = {The Connection between Regularization Operators and
             Support Vector Kernels},
  journal = {Neural Networks},
  year    = {1998},
  volume  = {11},
  pages   = {637--649},
  number  = {5}
}

@inproceedings{SmoSchMul98c,
  author    = {A. J. Smola and B. {Sch\"olkopf} and K.-R. {M\"uller}},
  title     = {Convex Cost Functions for Support Vector Regression},
  booktitle = {the International Conference on Artificial Neural
               Networks},
  year      = {1998},
  editor    = {L.~Niklasson and M.~{Bod\'en} and T.~Ziemke},
  series    = {Perspectives in Neural Computing},
  pages     = {99--104},
  address   = {Berlin},
  publisher = {Springer}
}

@inproceedings{SmoSchRat99,
  author    = {A. J. Smola and B. Sch\"olkopf and G. R\"atsch},
  title     = {Linear programs for automatic accuracy control in
               regression},
  booktitle = {Ninth International Conference on Artificial Neural
               Networks},
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  trtype      = {Jenaer Schriften zur Mathematik und Informatik}
}

@article{Steinwart01b,
  author  = {I. Steinwart},
  title   = {On the Influence of the Kernel on the Consistency of
             Support Vector Machines},
  journal = {J. Mach. Learn. Res.},
  year    = {2001},
  volume  = {2},
  pages   = {67-93}
}

@article{Steinwart02,
  author  = {I. Steinwart},
  title   = {Support Vector Machines are universally consistent},
  journal = {J. Complexity},
  year    = {2002},
  volume  = {18},
  pages   = {768-791}
}

@book{steinwart2008support,
  title     = {Support vector machines},
  author    = {Steinwart, Ingo and Christmann, Andreas},
  year      = {2008},
  publisher = {Springer Science \& Business Media}
}

@techreport{Steinwart99,
  author      = {I.~Steinwart},
  title       = {Some estimates for the entropy numbers of convex hulls
                 with finitely many extreme points},
  institution = {University of Jena},
  year        = {1999}
}

@inproceedings{Steinwart_ElicitationIdentification_2014,
  address   = {Barcelona, Spain},
  series    = {Proceedings of {Machine} {Learning} {Research}},
  title     = {Elicitation and {Identification} of {Properties}},
  volume    = {35},
  abstract  = {Properties of distributions are real-valued functionals
               such as the mean, quantile or conditional value at risk. A
               property is elicitable if there exists a scoring function
               such that minimization of the associated risks recovers the
               property. We extend existing results to characterize the
               elicitability of properties in a general setting. We
               further relate elicitability to identifiability (a notion
               introduced by Osband) and provide a general formula
               describing all scoring functions for an elicitable
               property. Finally, we draw some connections to the theory
               of coherent risk measures.},
  booktitle = {Proceedings of {The} 27th {Conference} on {Learning}
               {Theory}},
  publisher = {PMLR},
  author    = {Steinwart, Ingo and Pasin, Chloé and Williamson, Robert
               C. and Zhang, Siyu},
  editor    = {Balcan, Maria Florina and Feldman, Vitaly and Szepesvári,
               Csaba},
  month     = may,
  year      = {2014},
  pages     = {482--526},
  file      = {Steinwart et al - Elicitation and Identification of
               Properties
               (2014).pdf:/Users/zcranko/Documents/Library/Conference
               Paper/Steinwart et al - Elicitation and Identification of
               Properties (2014).pdf:application/pdf}
}

@book{Steinwart_SupportVector_2008,
  address   = {New York, NY, USA},
  edition   = {1},
  series    = {Information science and statistics},
  title     = {Support vector machines},
  isbn      = {978-0-387-77241-7 978-0-387-77242-4},
  language  = {en},
  publisher = {Springer},
  author    = {Steinwart, Ingo and Christmann, Andreas},
  year      = {2008},
  file      = {Steinwart, Christmann - Support vector machines
               (2008).pdf:/Users/zcranko/Documents/Library/Book/Steinwart,
               Christmann - Support vector machines
               (2008).pdf:application/pdf}
}

@article{Stengel96,
  title   = {Efficient Computation of Behavior Strategies},
  year    = {1996},
  author  = {Bernhard von Stengel},
  journal = {Games and Economic Behavior},
  volume  = 14,
  number  = 2,
  pages   = {220--246}
}

@inproceedings{Stenstrom07,
  author    = {P. Stenstr{\umlaut}om},
  booktitle = {Applied and Compututational Mathematics},
  vol       = {6},
  pages     = {253-257},
  year      = {2007},
  title     = {The Paradigm Shift to Multi-Cores: Opportunities and
               Challenges}
}

@inproceedings{Stephan95,
  author    = {Frank Stephan},
  title     = {Learning via queries and oracles},
  booktitle = {Proc. 8th Annu. Conf. on Comput. Learning Theory},
  year      = {1995},
  pages     = {162--169},
  publisher = {ACM Press, New York, NY}
}

@article{Stephan97,
  author  = {Frank Stephan},
  title   = {Noisy inference and oracles},
  journal = {Theoret. Comput. Sci.},
  year    = {1997},
  volume  = {185},
  pages   = {129--157},
  number  = {1},
  note    = {Special issue for ALT '95}
}

@inbook{SteSch95,
  chapter   = {7},
  pages     = {115--130},
  title     = {Recurrent neural networks with time-dependent inputs and
               outputs},
  publisher = {{MIT} Press},
  year      = {1995},
  author    = {Volkmar Sterzing and Bernd Sch{\"u}rmann},
  volume    = {III: Selecting Good Models},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@inproceedings{SteSchBla07,
  author    = {F. Steinke and B. Sch\"olkopf and V. Blanz},
  title     = {Learning Dense 3D Correspondence},
  year      = {2007},
  publisher = {MIT Press},
  pages     = {1313-1320},
  month     = {09},
  journal   = {Advances in Neural Information Processing Systems 19: the
               2006 Conference},
  editor    = {B. Sch\"olkopf and J. Platt and T. Hofmann},
  address   = {Cambridge, MA},
  booktitle = {Twentieth Annual Conference on Neural Information
               Processing Systems},
  location  = {Vancouver, BC}
}

@inproceedings{SteTer00,
  author    = {Frank Stephan and Sebastiaan A. Terwijn},
  title     = {Counting Extensional Differences in {BC}-Learning},
  booktitle = {Grammatical Inference: {A}lgorithms and {A}pplications,
               5th International Colloquium, ICGI 2000, Lisbon, Portugal,
               September 11--13, 2000 ; Proceedings},
  year      = {2000},
  volume    = {1891},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {256--269},
  publisher = {Springer, Berlin}
}

@article{SteTer99,
  author  = {F. Stephan and S. A. Terwijn},
  title   = {The complexity of universal text-learners},
  journal = {Inform. Comput.},
  year    = {1999},
  volume  = {154},
  pages   = {149--166},
  number  = {2}
}

@inproceedings{SteVen98,
  author    = {F. Stephan and Y. Ventsov},
  title     = {Learning Algebraic Structures from Text Using Semantical
               Knowledge},
  booktitle = alt,
  year      = {1998},
  volume    = {1501},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {321--335},
  publisher = {Springer-Verlag}
}

@article{SteWanCooAyeetal05,
  author  = {J. Stec and J. Wang and K. Coombes and M. Ayers and S.
             Hoersch and D. L. Gold and J. S. Ross and K. R. Hess and S.
             Tirrell and G. Linette and G. N. Hortobagyi and W. Fraser
             Symmans and L. Pusztai},
  title   = {Comparison of the predictive accuracy of DNA array-based
             multigene classifiers across cDNA arrays and Affymetrix
             GeneChips},
  journal = {J Mol Diagn},
  year    = {2005},
  volume  = {7},
  pages   = {357-367},
  number  = {3},
  month   = {Aug}
}

@article{Stewart00,
  author  = {G.~W. Stewart},
  title   = {Decompositional Approach to Matrix Computation},
  journal = {Computing in Science and Engineering},
  year    = {2000},
  volume  = {2},
  pages   = {50--59},
  number  = {1},
  month   = {February}
}

@article{Stewart76,
  author  = {J.~Stewart},
  title   = {Positive definite functions and generalizations, an
             historical survey.},
  journal = {Rocky Mountain J.~Math.},
  year    = {1976},
  volume  = {6},
  pages   = {409--434}
}

@book{SteWei71,
  title     = {Introduction to Fourier analysis on Euclidean spaces},
  publisher = {Princeton University Press},
  year      = {1971},
  author    = {E.~M.~Stein and G.~Weiss},
  series    = {Princeton mathematical series, 32},
  address   = {Princeton, NJ}
}

@article{SteYuk89,
  author  = {G.~Stengle and J.~E.~Yukich},
  title   = {Some new {V}apnik-{C}hervonenkis Classes},
  journal = {The Annals of Statistics},
  year    = {1989},
  volume  = {17},
  pages   = {1441--1446},
  number  = {4}
}

@inproceedings{SteZeu00,
  author    = {Frank Stephan and T. Zeugmann},
  title     = {Average-Case Complexity of Learning Polynomials},
  booktitle = {Proc. 13th Annu. Conference on Comput. Learning Theory},
  year      = {2000},
  pages     = {59--68},
  publisher = {Morgan Kaufmann, San Francisco}
}

@techreport{SteZeu99,
  author      = {F. Stephan and T. Zeugmann},
  title       = {Learning Classes of Approximations to Non-Recursive
                 Functions},
  institution = {Department of Informatics, Kyushu University},
  year        = {1999},
  number      = {DOI-TR-166},
  month       = jul
}

@inproceedings{SteZeu99b,
  author    = {Frank Stephan and T. Zeugmann},
  title     = {On the Uniform Learnability of Approximations to
               Non-Recursive Functions},
  booktitle = alt,
  year      = {1999},
  volume    = {1720},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {276--290},
  publisher = {Springer-Verlag}
}

@techreport{StiGamVapVovetal97,
  author      = {M.~Stitson and A.~Gammerman and V.~Vapnik and V.~Vovk and
                 C.~Watkins and J.~Weston},
  title       = {Support vector regression with {ANOVA} decomposition
                 kernels},
  institution = {Royal Holloway, University of London},
  year        = {1997},
  number      = {CSD-TR-97-22}
}

@inproceedings{StiGamVapVovetal99,
  author    = {M.\ Stitson and A.~Gammerman and V.~Vapnik and V.~Vovk and
               C.~Watkins and J.~Weston},
  title     = {Support Vector Regression with {ANOVA} Decomposition
               Kernels},
  booktitle = {Advances in Kernel Methods\,---\,Support Vector Learning},
  year      = {1999},
  editor    = {B.~Sch{\"o}lkopf and C.~J.~C.~Burges and A. J. Smola},
  pages     = {285--292},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@phdthesis{Still00,
  author = {S.~Still},
  title  = {Controlling four legged locomotion-bio-inspired technology
            applied to robotics},
  school = {ETH {Z\"urich}},
  year   = {2000}
}

@inproceedings{StiSch01,
  author    = {S.~Still and B.~{Sch\"olkopf}},
  title     = {Four-legged Walking Gait Control Using a Neuromorphic Chip
               Interfaced to a Support Vector Learning Algorithm},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {741--747},
  publisher = {{MIT} Press}
}

@techreport{StiWes96,
  author      = {Mark O.~Stitson and Jason A.~E.~Weston},
  title       = {Implementational Issues of Support Vector Machines},
  institution = {Royal Holloway, University of London},
  year        = {1996},
  note        = {CSD-TR-96-18}
}

@techreport{StiWesGamVovetal96,
  author      = {M.~O.~Stitson and J.~A.~E.~Weston and A.~Gammerman and
                 V.~Vovk and V.~Vapnik},
  title       = {Theory of Support Vector Machines},
  institution = {Royal Holloway, University of London},
  year        = {1996},
  type        = {Technical Report},
  number      = {CSD-TR-96--17},
  month       = {December}
}

@inproceedings{StiWhi89,
  author    = {M.~Stinchcombe and H.~White},
  title     = {Universal approximation using feedforward networks with
               non-sigmoid hidden layer activation functions},
  booktitle = {the International Joint Conference on Neural Networks},
  year      = {1989},
  pages     = {I-607--I-611},
  address   = {Washington, DC},
  month     = jun,
  publisher = {IEEE TAB Neural Network Committee}
}

@book{StoBul93,
  title     = {Introduction to Numerical Analysis},
  publisher = {Springer},
  year      = {1993},
  author    = {J. Stoer and R. Bulirsch},
  address   = {New York},
  edition   = {second},
  kwds      = {book, na}
}

@book{StoGol06,
  title     = {Mathematics for Physics: A Guided Tour for Graduate
               Students},
  author    = {Michael Stone and Paul Goldbart},
  publisher = {Cambridge University Press},
  year      = {2009}
}

@techreport{StoHanKooTru95,
  author      = {Charles J. Stone and Mark Hansen and Charles Kooperberg
                 and Young K. Truong},
  title       = {Polynomial Splines and Their Tensor Products in Extended
                 Linear Modeling},
  institution = {University of California at Berkeley},
  year        = {1995},
  address     = {Berkeley, California},
  month       = jan,
  pages       = {66}
}

@techreport{StoHar79,
  author      = {S. J. Stolfo and M. C. Harrison},
  title       = {Automatic discovery of heuristics for nondeterministic
                 programs},
  institution = {New York University Computer Science Dept., No. 7},
  year        = {1979}
}

@inproceedings{StoHau94,
  author    = {G. D. Stormo and D. Haussler},
  title     = {Optimally Parsing a Sequence into Different Classes Based
               on Multiple Types of Information},
  booktitle = {Proc. of Second Int. Conf. on Intelligent Systems for
               Molecular Biology},
  year      = {1994},
  pages     = {369--375},
  address   = {Menlo Park, CA},
  month     = aug,
  publisher = {AAAI/MIT Press}
}

@article{StoKraAstGra04,
  author  = {H. St{\"o}gbauer and A. Kraskov and S. Astakhov and P.
             Grassberger},
  title   = {Least Dependent Component Analysis Based on Mutual
             Information},
  journal = {Phys. Rev. E},
  volume  = {70},
  number  = {6},
  pages   = {066123},
  year    = {2004}
}

@phdthesis{Stolfo79,
  author = {S. J. Stolfo},
  title  = {Automatic {D}iscovery {H}euristics for {N}ondeterministic
            {P}rograms {F}rom {S}ample {E}xecution {T}races},
  school = {New York University},
  year   = {1979}
}

@article{Stolovitzky03,
  author  = {G. Stolovitzky},
  title   = {Gene selection in microarray data: the elephant, the blind
             men and our algorithms},
  journal = {Current Opinion in Structural Biology},
  volume  = {13},
  number  = {3},
  pages   = {370--376},
  month   = {Jun},
  year    = {2003}
}

@inproceedings{Stone00,
  author    = {Peter Stone},
  title     = {{TPOT-RL} Applied to Network Routing},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  pages     = {935--942},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

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  author  = {M.~H. Stone},
  title   = {Applications of the theory of {Boolean} rings to general
             topology},
  journal = {AMS Transactions},
  year    = {1937},
  volume  = {41},
  pages   = {375--481}
}

@article{Stone48,
  author  = {M.~H. Stone},
  title   = {The generalized {Weierstrass} approximation theorem},
  journal = {Mathematics Magazine},
  year    = {1948},
  volume  = {21},
  pages   = {{167--183, 237--254}}
}

@article{Stone74,
  author  = {M. Stone},
  title   = {Cross-validatory choice and assessment of statistical
             predictors(with discussion)},
  journal = {Journal of the Royal Statistical Society},
  year    = {1974},
  volume  = {B36},
  pages   = {111--147}
}

@article{Stone80,
  author  = {C.~J. Stone},
  title   = {Optimal rates of convergence for nonparametric
             estimators},
  journal = {Annals of Statistics},
  year    = {1980},
  volume  = {8},
  pages   = {1348--1360},
  number  = {6}
}

@article{Stone82,
  author  = {C.~J. Stone},
  title   = {Optimal Global Rates of Convergence for nonparametric
             regression},
  journal = {Annals of Statistics},
  year    = {1982},
  volume  = {10},
  pages   = {1040--1053}
}

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  author  = {C.~J. Stone},
  title   = {Additive regression and other nonparametric models},
  journal = {Annals of Statistics},
  year    = {1985},
  volume  = {13},
  pages   = {689--705}
}

@article{Stone86,
  author  = {C.~J. Stone},
  title   = {The dimensionality reduction principle for generalized
             additive models},
  journal = {Annals of Statistics},
  year    = {1986},
  volume  = {14},
  pages   = {590--606},
  number  = {2}
}

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  author  = {D. Stoneking},
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  journal = {IEEE Spectrum},
  year    = {1999},
  volume  = {36},
  pages   = {70--76},
  number  = {6},
  month   = {June}
}

@inproceedings{StoOmo94,
  author    = {A. Stolcke and S. Omohundro},
  title     = {Inducing probabilistic grammars by {B}yasian model
               merging},
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               Inference - ICGI '94},
  year      = {1994},
  volume    = {862},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {106--118},
  publisher = {Springer-Verlag}
}

@inproceedings{StoRopEis11,
  title     = {Empirical Risk Minimization of Graphical Model Parameters
               Given Approximate Inference, Decoding, and Model
               Structure},
  author    = {Veselin Stoyanov and Alexander Ropson and Jason Eisner},
  booktitle = aistats,
  year      = {2011}
}

@inproceedings{StoVel00,
  author    = {Peter Stone and Manuela M. Veloso},
  title     = {Layered Learning},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
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  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {369--381},
  publisher = {Springer, Berlin}
}

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}

@book{Strang98,
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  year      = {1998},
  author    = {G. Strang},
  month     = {August}
}

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  author    = {W. Nick Street},
  title     = {A neural network model for prognostic prediction},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {540--546},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

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  author    = {Malcolm Strens},
  title     = {A {B}ayesian Framework for Reinforcement Learning},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
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  publisher = {Morgan Kaufmann, San Francisco, CA}
}

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  author    = {G. Strang and G. Fix},
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  year      = {1973},
  editor    = {G. Geymonat},
  pages     = {793--840}
}

@inproceedings{StrGol08,
  title     = {An Online Algorithm for Maximizing Submodular Functions},
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  publisher = {{MIT} Press},
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  booktitle = {NIPS},
  editor    = {D. Koller and D. Schuurmans and Y. Bengio and L. Bottou},
  pages     = {1577--1584},
  url       = {http://books.nips.cc/papers/files/nips21/NIPS2008_0729.pdf}
}

@article{StrIke90,
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  volume  = {85},
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  month   = mar,
  year    = {1990}
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@incollection{Stringa93,
  author    = {L. Stringa},
  title     = {Eyes Detection for Face Recognition},
  booktitle = {Applied Artificial Intelligence},
  publisher = {Hemisphere Publishing Corp.},
  year      = {in press, 1993}
}

@article{StrKobvanBia98,
  author  = {S. Strong and P. Koberle and {R. De R.} {van Steveninck}
             and W. Bialek},
  title   = {Entropy and Information in Neural Spike Trains},
  journal = {Physical Review Letters},
  year    = {1998},
  volume  = {80},
  number  = {1},
  pages   = {197--200}
}

@techreport{StrMan95,
  author      = {W.~N. Street and O.~L. Mangasarian},
  title       = {Improved Generalization via Tolerant Training},
  institution = {University of Wisconsin, Madison},
  year        = {1995},
  type        = {Technical Report},
  number      = {MP-TR-95-11}
}

@inproceedings{StrManWol95,
  author    = {W. Nick Street and O. L. Mangasarian and W. H. Wolberg},
  title     = {An inductive learning approach to prognostic prediction},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1995},
  pages     = {522--530},
  publisher = {Morgan Kaufmann}
}

@book{StrNgu96,
  title     = {Wavelets and Filter Banks},
  publisher = {Wellesley-Cambridge Press},
  year      = {1996},
  author    = {G. Strang and T. Nguyen}
}

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  author  = {M. Stricker and M. Swain},
  title   = {The Capacity of Color Histogram Indexing},
  journal = {IEEE},
  year    = {1994},
  pages   = {704--708}
}

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  author    = {Joshua M. Stuart and Elizabeth Bradley},
  title     = {Learning the grammar of dance},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1998},
  pages     = {547--555},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{StuHeiSch19,
  title   = {Disentangling adversarial robustness and generalization},
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  year    = {2019}
}

@article{Sturm99,
  author  = {J.F. Sturm},
  title   = {Using SeDuMi 1.02, a MATLAB toolbox for optimization over
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  year    = {1999},
  volume  = {11--12},
  pages   = {625--653},
  note    = {Special issue on Interior Point Methods (CD supplement
             with software)}
}

@article{Sturm99b,
  author  = {J. F. Sturm},
  title   = {Using {S}e{D}u{M}i 1.02, a {MATLAB} toolbox for
             optimization over symmetric cones},
  journal = {Optimization Methods and Software},
  year    = {1999},
  volume  = {11/12},
  pages   = {625--653},
  number  = {1--4}
}

@inproceedings{SubBil09,
  author    = {A. Subramanya and J. A. Bilmes},
  title     = {Entropic Graph Regularization in Non-Parametric
               Semi-Supervised Classification},
  booktitle = {NIPS},
  year      = {2009},
  address   = {Vancouver, Canada},
  month     = {December}
}

@inproceedings{SubPetPer10,
  author    = {Amarnag Subramanya and Slav Petrov and Fernando Pereira},
  title     = {Efficient Graph-Based Semi-Supervised Learning of
               Structured Tagging Models},
  booktitle = {the 2010 Conference on Empirical Methods in Natural
               Language Processing},
  month     = {October},
  year      = {2010},
  address   = {Cambridge, MA},
  publisher = {Association for Computational Linguistics},
  pages     = {167--176},
  url       = {http://www.aclweb.org/anthology/D10-1017}
}

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  title     = {Shifting Vocabulary Bias in Speedup Learning},
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  number    = {1/2},
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@article{Takada88,
  author  = {Yuji Takada},
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             Control Sets},
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@inproceedings{Takada94,
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@incollection{Takada95,
  author    = {Yuji Takada},
  title     = {Learning Formal Languages Based on Control Sets},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {316--339}
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@article{Takashi95,
  author    = {Yokomori Takashi},
  title     = {On Polynomial-Time Learnability in the Limit of Strictly
               Deterministic Automata},
  journal   = {Machine Learning},
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  volume    = {19},
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  publisher = {Kluwer Academic Publishers, Boston}
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@incollection{Takens81,
  author    = {F. Takens},
  title     = {Detecting Strange Attractors in Fluid Turbulence},
  booktitle = {Dynamical Systems and Turbulence},
  publisher = {Springer},
  year      = {1981},
  editor    = {D. Rand and L.~S. Young},
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  address   = {Berlin}
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  year      = {1993},
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  series    = {Lecture Notes in Artificial Intelligence},
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@inproceedings{TakFur04,
  author    = {I. Takeuchi and T. Furuhashi},
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  year      = {2004}
}

@inproceedings{TakHirMar97,
  author    = {E. Takimoto and Ken'ichi Hirai and Akira Maruoka},
  title     = {A Simple Algorithm for Predicting Nearly as Well as the
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               Decision Tree},
  booktitle = alt-ws,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {385--400},
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@article{TakLeSeaSmo06,
  author    = {I. Takeuchi and Q.~V. Le and T. Sears and A.~J. Smola},
  title     = {Nonparametric Quantile Estimation},
  journal   = jmlr,
  year      = 2006,
  publisher = {MIT Press},
  volume    = 7
}

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               {O}ccam Algorithms},
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  year      = {1992},
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  author    = {E. Takimoto and A. Maruoka},
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  year      = {1993},
  pages     = {377--383},
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@inproceedings{TakMar93b,
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               One-Sided Error},
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  year      = {1993},
  volume    = {744},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {265--278},
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@inproceedings{TakMar98,
  author    = {Eiji Takimoto and Akira Maruoka},
  title     = {On the Boosting Algorithm for Multiclass Functions Based
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  year      = {1998},
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  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {256--267},
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  author  = {Eiji Takimoto and Akira Maruoka and Volodya Vovk},
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  note    = {Special issue for ALT '97}
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             Yoshifumi Sakai},
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  journal = {Theoret. Comput. Sci.},
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  year      = 2007
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               consistency in generalizing a binary classiﬁcation method
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               multiclass methods and provide a necessary and sufﬁcient
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               literature.},
  language  = {en},
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@inproceedings{Ting96,
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@techreport{TipBis97,
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  institution = {Neural Computing Research Group, Aston University},
  year        = {1997},
  note        = {NCRG/97/003; also appeared in Neural Computation}
}

@article{TipLaw05,
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  volume  = 69,
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@inproceedings{Valiant95b,
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@techreport{VanSha97,
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@article{VapBot93,
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@inproceedings{VapCha00,
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@article{VapChe64,
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@article{VapChe68,
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@article{VapChe71,
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@book{VapChe74,
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               1979)}
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@article{VapChe74b,
  author  = {V.~N. Vapnik and A.~Y. Chervonenkis},
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@book{VapChe74c,
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@article{VapChe91,
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@article{VapLevLeC94,
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  year    = {1994},
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@article{VapMarSte92,
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}

@article{vapnik1971uniform,
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  author    = {V. Vapnik},
  address   = {Moscow},
  note      = {(English translation: Springer, New York, 1982)}
}

@book{Vapnik82,
  title     = {Estimation of Dependences Based on Empirical Data},
  publisher = {Springer},
  year      = {1982},
  author    = {V. Vapnik},
  address   = {Berlin}
}

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  title     = {Principles of risk minimization for learning theory},
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  year      = {1992},
  editor    = {J.~E. Moody and S.~J. Hanson and R. Lippmann},
  pages     = {831--838},
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}

@article{Vapnik93,
  author  = {V.~N. Vapnik},
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  pages   = {838--844}
}

@book{Vapnik95,
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  author    = {V. Vapnik},
  address   = {New York}
}

@incollection{Vapnik95b,
  author    = {V. Vapnik},
  title     = {Inductive principles of statistics and learning theory},
  booktitle = {Mathematical Perspectives on Neural Networks},
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  year      = {1995},
  editor    = {P. Smolensky and M.~C. Mozer and D.~E. Rumelhart},
  address   = {Mahwah, NJ}
}

@incollection{Vapnik96,
  author    = {V. Vapnik},
  title     = {Structure of Statistical Learning Theory},
  booktitle = {Computational and Probabalistic Reasoning},
  publisher = {John Wiley and Sons},
  year      = {1996},
  editor    = {A. Gammerman},
  chapter   = {1},
  address   = {Chichester}
}

@book{Vapnik98,
  title     = {Statistical Learning Theory},
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  author    = {V. Vapnik},
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@inproceedings{Vapnik99,
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  year      = {1999},
  editor    = {B. Sch{\"o}lkopf and C.~J.~C. Burges and A. J. Smola},
  pages     = {25--42},
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}

@book{Vapnik99b,
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  year      = {1999},
  author    = {Vladimir Vapnik},
  edition   = {second}
}

@book{Vapnik_NatureStatistical_2000,
  address   = {New York, NY, USA},
  title     = {The nature of statistical learning theory},
  isbn      = {978-1-4757-3264-1 978-1-4419-3160-3},
  abstract  = {The aim of this book is to discuss the fundamental ideas
               which lie behind the statistical theory of learning and
               generalization. It considers learning as a general problem
               of function estimation based on empirical data. Omitting
               proofs and technical details, the author concentrates on
               discussing the main results of learning theory and their
               connections to fundamental problems in statistics. These
               include: * the setting of learning problems based on the
               model of minimizing the risk functional from empirical data
               * a comprehensive analysis of the empirical risk
               minimization principle including necessary and sufficient
               conditions for its consistency * non-asymptotic bounds for
               the risk achieved using the empirical risk minimization
               principle * principles for controlling the generalization
               ability of learning machines using small sample sizes based
               on these bounds * the Support Vector methods that control
               the generalization ability when estimating function using
               small sample size. The second edition of the book contains
               three new chapters devoted to further development of the
               learning theory and SVM techniques. These include: * the
               theory of direct method of learning based on solving
               multidimensional integral equations for density,
               conditional probability, and conditional density estimation
               * a new inductive principle of learning. Written in a
               readable and concise style, the book is intended for
               statisticians, mathematicians, physicists, and computer
               scientists. Vladimir N. Vapnik is Technology Leader AT \& T
               Labs-Research and Professor of London University. He is one
               of the founders of.},
  language  = {English},
  publisher = {Springer},
  author    = {Vapnik, Vladimir N},
  year      = {2000},
  note      = {OCLC: 864225872},
  file      = {Vapnik - The nature of statistical learning theory
               (2000).pdf:/Users/zcranko/Documents/Library/Book/Vapnik -
               The nature of statistical learning theory
               (2000).pdf:application/pdf}
}

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}

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  year    = {1978},
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              with terms outside of A has a limit inside A. Moreover, a
              metric space is compact if and only if every sequence has a
              converging subsequence. However, in a general topological
              space these equivalences may fail. Unfortunately this fact
              is sometimes overlooked in introductory courses on general
              topology, leaving many students with misconceptions, e.g.
              that compactness would always be equal to sequence
              compactness. The aim of this article is to show how
              sequences might fail to characterize topological properties
              such as openness, continuity and compactness correctly.
              Moreover, I will define nets and show how they succeed
              where sequences fail.},
  journal  = {arXiv:1006.4472 [math]},
  author   = {Vermeeren, Stijn},
  month    = jun,
  year     = {2010},
  note     = {arXiv: 1006.4472},
  keywords = {⛔ No DOI found},
  file     = {arXiv.org
              Snapshot:/Users/zcranko/Documents/Zotero/storage/KWKCZMR5/1006.html:text/html;Vermeeren
              - Sequences and nets in topology
              (2010).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Vermeeren - Sequences and nets in topology
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}

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  volume  = {7},
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@incollection{Vidakovic_GMinimaxParadigm_2000,
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  volume     = {152},
  isbn       = {978-0-387-98866-5 978-1-4612-1306-2},
  shorttitle = {Γ-minimax},
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  publisher  = {Springer},
  author     = {Vidakovic, Brani},
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                K. and Olkin, I. and Wermuth, N. and Zeger, S. and Insua,
                David Ríos and Ruggeri, Fabrizio},
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  doi        = {10.1007/978-1-4612-1306-2_13},
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  year        = {2005},
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  optabstract = {},
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@article{VidThoHigCasetal05,
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@article{VidThoHigCasetal05b,
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  note    = {to appear},
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  author    = {S.~V.~N. Vishwanathan and K. M. Borgwardt and O. Guttman
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  volume    = {69},
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@article{VisBorKonSch08,
  author  = {S.~V.~N. Vishwanathan and Karsten Borgwardt and Nicol N.
             Schraudolph and Imre Risi Kondor},
  title   = {On Graph Kernels},
  journal = {J. Mach. Learn. Res.},
  year    = {2008},
  note    = {submitted},
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@techreport{VisBorSch06,
  author      = {S.~V.~N. Vishwanathan and Karsten Borgwardt and Nicol N.
                 Schraudolph},
  title       = {Fast Computation of Graph Kernels},
  institution = {NICTA},
  year        = {2006}
}

@inproceedings{VisBorSch07,
  crossref = {VisBorSch07b}
}

@inproceedings{VisBorSch07b,
  author    = {S.~V.~N. Vishwanathan and Karsten Borgwardt and Nicol N.
               Schraudolph},
  title     = {Fast Computation of Graph Kernels},
  booktitle = {Advances in Neural Information Processing Systems 19},
  year      = {2007},
  editor    = {B. {Sch\"olkopf} and J. Platt and T. Hofmann},
  address   = {Cambridge MA},
  publisher = {{MIT} Press}
}

@article{VisChe08,
  author    = {S.~V.~N. Vishwanathan and Li Cheng},
  title     = {Implicit Online Learning with Kernels},
  journal   = jmlr,
  year      = 2008,
  b2h_note  = {Submitted (September 2008)},
  b2h_type  = {Journal Papers},
  b2h_topic = {kernels}
}

@phdthesis{Vishwanathan02,
  author  = {S.~V.~N. Vishwanathan},
  title   = {Kernel Methods: Fast Algorithms and Real Life
             Applications},
  school  = {Indian Institute of Science},
  year    = {2002},
  address = {Bangalore, India},
  month   = {November},
  note    = {\url{http://www.stat.purdue.edu/~vishy/papers/Vishwanathan02.pdf}}
}

@article{VisMur00,
  author  = {S.~V.~N. Vishwanathan and M. N. Murty},
  title   = {Kohonen's {SOM} with Cache},
  journal = {Pattern Recognition},
  year    = {2000},
  volume  = {33},
  pages   = {1927--1929},
  number  = {11},
  month   = {November},
  url     = {http://www.stat.purdue.edu/~vishy/papers/VisMur00.pdf}
}

@inproceedings{VisMur01,
  author    = {S.~V.~N. Vishwanathan and M. N. Murty},
  title     = {Use of {MPSVM} for Data Set Reduction},
  booktitle = {Hybrid Information Systems},
  year      = {2001},
  editor    = {A. Abraham and M. Koeppen},
  address   = {Heidelberg},
  publisher = {Physica Verlag},
  url       = {http://www.stat.purdue.edu/~vishy/papers/VisMur01.pdf}
}

@incollection{VisMur02,
  author    = {S.~V.~N. Vishwanathan and M. N. Murty},
  title     = {Use of {MPSVM} for Data Set Reduction},
  booktitle = {Recent Advances in Intelligent Paradigms and
               Applications},
  publisher = {Springer Verlag},
  year      = {2002},
  editor    = {A. Abraham and L. Jain and J. Kacprzyk},
  volume    = {113},
  series    = {Studies in Fuzziness and Soft Computing},
  chapter   = {16},
  address   = {Berlin},
  month     = {November},
  url       = {http://www.stat.purdue.edu/~vishy/papers/VisMur02.pdf}
}

@inproceedings{VisMur02b,
  author    = {S.~V.~N. Vishwanathan and M. N. Murty},
  title     = {Jigsawing: A Method to Generate Virtual Examples in {OCR}
               Data},
  booktitle = {Hybrid Intelligent Systems},
  year      = {2002},
  url       = {http://www.stat.purdue.edu/~vishy/papers/VisMur02a.pdf}
}

@inproceedings{VisMur02c,
  author    = {S.~V.~N. Vishwanathan and M. N. Murty},
  title     = {{SSVM}: A Simple {SVM} Algorithm},
  booktitle = {International Joint Conference on Neural Networks
               (IJCNN)},
  year      = {2002},
  editor    = {C. Lee Giles},
  publisher = {IEEE Press},
  url       = {http://www.stat.purdue.edu/~vishy/papers/VisMur02b.pdf}
}

@inproceedings{VisMur02d,
  author    = {S.~V.~N. Vishwanathan and M. N. Murty},
  title     = {Geometric {SVM}: A fast and intuitive {SVM} algorithm},
  booktitle = {the International Conference on Pattern Recognition
               (ICPR)},
  year      = {2002},
  url       = {http://www.stat.purdue.edu/~vishy/papers/VisMur02c.pdf}
}

@techreport{VisMur02e,
  author      = {P. Viswanath and M.~N. Murty},
  title       = {An efficient incremental mining algorithm for compact
                 realization of prototypes},
  institution = {CSA Department, Indian Institute of Science},
  year        = {2002},
  number      = {IISC-CSA-2002-2},
  address     = {Bangalore, India},
  month       = {January}
}

@article{VisSchKonBor10,
  author  = {S.~V.~N. Vishwanathan and Nicol N. Schraudolph and Imre
             Risi Kondor and Karsten M. Borgwardt},
  title   = {Graph Kernels},
  journal = jmlr,
  volume  = {11},
  number  = {40},
  pages   = {1201--1242},
  year    = 2010
}

@inproceedings{VisSchSchMur06,
  author    = {S.~V.~N. Vishwanathan and N.N. Schraudolph and M. Schmidt
               and K. Murphy},
  title     = {Accelerated Training Conditional Random Fields with
               Stochastic Gradient Methods},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2006},
  isbn      = {1-59593-383-2},
  pages     = {969--976},
  location  = {Pittsburgh, Pennsylvania},
  publisher = {ACM Press},
  address   = {New York, NY, USA}
}

@article{VisSchSmo06,
  author  = {S.~V.~N. Vishwanathan and Nicol N. Schraudolph and A. J.
             Smola},
  title   = {Step Size Adaptation in Reproducing Kernel {H}ilbert
             Space},
  journal = {J. Mach. Learn. Res.},
  year    = {2006},
  volume  = {7},
  pages   = {1107--1133},
  month   = {June},
  url     = {http://www.stat.purdue.edu/~vishy/papers/VisSchSmo06.pdf}
}

@incollection{VisSmo03,
  author    = {S.~V.~N. Vishwanathan and A. J. Smola},
  title     = {Fast Kernels for String and Tree Matching},
  booktitle = {Advances in Neural Information Processing Systems 15},
  publisher = {{MIT} Press},
  year      = {2003},
  editor    = {S. Becker and S. Thrun and K. Obermayer},
  pages     = {569--576},
  address   = {Cambridge, MA}
}

@inproceedings{VisSmo04,
  author    = {S.~V.~N. Vishwanathan and A. J. Smola},
  title     = {Fast Kernels for String and Tree Matching},
  booktitle = {Kernel Methods in Computational Biology},
  year      = {2004},
  editor    = {B. Sch\"olkopf and K. Tsuda and J. P. Vert},
  address   = {Cambridge, MA},
  pages     = {113--130},
  publisher = {{MIT} Press}
}

@inproceedings{VisSmo05,
  author    = {S.~V.~N. Vishwanathan and A. J. Smola},
  title     = {{B}inet-{C}auchy Kernels},
  booktitle = {Advances in Neural Information Processing Systems 17},
  year      = {2005},
  editor    = {L. K. Saul and Y. Weiss and L. Bottou},
  pages     = {1441--1448},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{VisSmoMur03,
  author    = {S.~V.~N. Vishwanathan and A. J. Smola and M. N. Murty},
  title     = {Simple{SVM}},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2003},
  editor    = {Tom Fawcett and Nina Mishra},
  address   = {Washington DC},
  publisher = {AAAI press}
}

@article{VisSmoVid07,
  author    = {S.~V.~N. Vishwanathan and A. J. Smola and R. Vidal},
  title     = {{Binet}-{Cauchy} Kernels on Dynamical Systems and its
               Application to the Analysis of Dynamic Scenes},
  journal   = {International Journal of Computer Vision},
  publisher = {Springer-Verlag},
  address   = {Netherlands},
  volume    = {73},
  number    = {1},
  pages     = {95--119},
  year      = {2007}
}

@inproceedings{VisYuTeo08,
  author    = {S.~V.~N. Vishwanathan and Jin Yu and Choon Hui Teo},
  title     = {Efficient Line Search for Generalized Hinge Losses},
  booktitle = nips21,
  year      = 2008,
  editor    = nips21e,
  publisher = mitpr,
  note      = {submitted}
}

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@phdthesis{Wiehagen78b,
  author = {R. Wiehagen},
  title  = {Zur Theorie der Algorithmischen Erkennung},
  school = {Humboldt-Universitat, Berlin},
  year   = {1978}
}

@inproceedings{Wiehagen86,
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  year      = {1986},
  volume    = {265},
  series    = {Lecture Notes in Computer Science},
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}

@article{Wiehagen86b,
  author  = {R. Wiehagen},
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  year    = {1986},
  volume  = {22},
  pages   = {305--323}
}

@inproceedings{Wiehagen86c,
  author    = {R. Wiehagen},
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               Specification and Synthesis of Software Systems '85},
  year      = {1986},
  volume    = {215},
  series    = {Lecture Notes in Computer Science},
  pages     = {231--239}
}

@inproceedings{Wiehagen90,
  author    = {R. Wiehagen},
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  year      = {1990},
  volume    = {543},
  series    = {Lecture Notes in Computer Science},
  pages     = {184--207},
  publisher = {Springer-Verlag}
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@inproceedings{Wiehagen93,
  author    = {R. Wiehagen},
  title     = {From Inductive Inference to Algorithmic Learning Theory},
  booktitle = alt-ws,
  year      = {1993},
  volume    = {743},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {13--24},
  publisher = {Springer-Verlag}
}

@article{WieJun77,
  author  = {R. Wiehagen and H. Jung},
  title   = {Rekursionstheoretische {C}harakterisierung von erkennbaren
             {K}lassen rekursiver {F}unktionen},
  journal = {Elektronische Informationsverarbeitung und Kybernetik},
  year    = {1977},
  volume  = {13},
  pages   = {385--397}
}

@article{WieKuhSim14,
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@article{WieLie76,
  author  = {R. Wiehagen and W. Liepe},
  title   = {Charakteristche {E}igenschaften von erkennbaren {K}lassen
             rekursiver {F}unktionen},
  journal = {Elektronische Informationsverarbeitung und Kybernetik},
  year    = {1976},
  volume  = {12},
  pages   = {421--438}
}

@article{WieLie76b,
  author  = {R. Wiehagen and W. Liepe},
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  journal = {Elektronische Informationsverarbeitung und Kybernetik},
  year    = {1976},
  volume  = {12},
  pages   = {421--438}
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@article{Wiener47b,
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@inproceedings{WieSmi92,
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@inproceedings{WieSmiZeu94,
  author    = {R. Wiehagen and C. H. Smith and T. Zeugmann},
  title     = {Classification of Predicates and Languages},
  booktitle = {Computational Learning Theory: EuroColt '93},
  year      = {1994},
  volume    = {New Series Number 53},
  series    = {The Institute of Mathematics and its Applications
               Conference Series},
  pages     = {171--181},
  address   = {Oxford},
  publisher = {Oxford University Press},
  isbn      = {0 19 853492 2}
}

@incollection{WieSmiZeu95,
  author    = {R. Wiehagen and C. H. Smith and T. Zeugmann},
  title     = {Classifying recursive predicates and languages},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {174--189},
  note      = {Was GOSLER-Report FB Mathematick und Informatik, TH
               Lepzig, 21/93 in 1993}
}

@inproceedings{WieZeu92,
  author    = {R. Wiehagen and T. Zeugmann},
  title     = {Too Much Information Can be too Much for Efficient
               Learning},
  booktitle = {Proc. 3rd Internat. Workshop on Analogical and Inductive
               Inference},
  year      = {1992},
  volume    = {642},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {72--86},
  publisher = {Springer-Verlag}
}

@article{WieZeu94,
  author  = {R. Wiehagen and T. Zeugmann},
  title   = {Ignoring data may be the only way to learn efficiently},
  journal = {J. of Experimental and Theoret. Artif. Intell.},
  year    = {1994},
  volume  = {6},
  pages   = {131--144},
  number  = {1},
  note    = {Special issue for AII '92}
}

@incollection{WieZeu95,
  author    = {R. Wiehagen and T. Zeugmann},
  title     = {Learning and Consistency},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {1--24},
  comment   = {An earlier version appeared as GOSLER-Report 28/94, FB
               Mathematik und Informatik, TH Lepzig, 1994.}
}

@inproceedings{Wiklicky94,
  author    = {H. Wiklicky},
  title     = {The Neural Network Loading Problem is Undecidable},
  booktitle = {Computational Learning Theory: EuroColt '93},
  year      = {1994},
  volume    = {New Series Number 53},
  series    = {The Institute of Mathematics and its Applications
               Conference Series},
  pages     = {183--192},
  address   = {Oxford},
  publisher = {Oxford University Press},
  isbn      = {0 19 853492 2}
}

@inproceedings{WilBar91,
  author    = {R.~C. Williamson and P.~L. Bartlett},
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               Learning Model},
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  year      = {1991},
  pages     = {24--30},
  address   = {San Mateo, CA},
  publisher = {Morgan Kaufmann Publishers}
}

@techreport{WilBar97,
  author      = {C.~K.~I. Williams and D. Barber},
  title       = {Bayesian Classification with {Gaussian} Processes},
  institution = {Neural Computing Research Group},
  year        = {1997},
  number      = {NCRG/97/015},
  address     = {Aston University, UK},
  month       = dec,
  note        = {submitted to {IEEE PAMI}}
}

@article{WilBar98,
  author  = {C.K.I. Williams and D. Barber},
  title   = {Bayesian Classification with {G}aussian Processes},
  journal = {IEEE Transactions on Pattern Analysis and Machine
             Intelligence PAMI},
  volume  = {20},
  number  = {12},
  pages   = {1342--1351},
  year    = {1998}
}

@techreport{WilGeiJanGroetal92,
  author      = {R.~A. Wilkinson and J. Geist and S. Janet and P.~J.
                 Grother and C.~J.~C. Burges and R. Creecy and B. Hammond
                 and J.~J. Hull and N.~J. Larsen and T.~P. Vogl and C.~L.
                 Wilson},
  title       = {The First Census Optical Character Recognition System
                 Conference},
  institution = {National Institute of Standards and Technology (NIST)},
  year        = {1992},
  number      = {NISTIR 4912},
  address     = {Gaithersburg}
}

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  volume  = {15},
  number  = {3},
  pages   = {333--335}
}

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  author  = {J. Wilkinson},
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  journal = {Journal of the ACM},
  volume  = {8},
  issue   = {3},
  pages   = {281--330},
  year    = {1961}
}

@book{Wilkinson65,
  title     = {The Algebraic Eigenvalue Problem},
  publisher = {Clarendon Press},
  year      = {1965},
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  address   = {Oxford}
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  year     = {1986},
  volume   = {22},
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              Federation of Automatic Control}
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@article{Willems86b,
  author   = {Willems, J.~C.},
  title    = {From time series to linear system. {I}{I}. {E}xact
              modelling},
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  year     = {1986},
  volume   = {22},
  pages    = {675--694},
  number   = {6},
  fjournal = {Automatica. A Journal of IFAC, the International
              Federation of Automatic Control}
}

@article{Willems87,
  author   = {Willems, J.~C.},
  title    = {From time series to linear system. {I}{I}{I}.
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  journal  = {Automatica J. IFAC},
  year     = {1987},
  volume   = {23},
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  fjournal = {Automatica. A Journal of IFAC, the International
              Federation of Automatic Control}
}

@misc{Williams00,
  author = {C.~K.~I. Williams},
  year   = {2000},
  note   = {Personal Communication}
}

@inproceedings{Williams01,
  author    = {C.~K.~I. Williams},
  title     = {On a connection between kernel {PCA} and metric
               multidimensional scaling},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {675--681},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@article{Williams92,
  author    = {Ronald J. Williams},
  title     = {Simple Statistical Gradient-Following Algorithms for
               Connectionist Reinforcement Learning},
  journal   = {Machine Learning},
  year      = {1992},
  volume    = {8},
  pages     = {229--256},
  publisher = {Kluwer Academic Publishers, Boston}
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@inproceedings{Williams97,
  author    = {C.~K.~I. Williams},
  title     = {Computation with Infinite Networks},
  booktitle = {Advances in Neural Information Processing Systems 9},
  year      = {1997},
  editor    = {M.~C. Mozer and M.~I. Jordan and T. Petsche},
  pages     = {295--301},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@incollection{Williams98,
  author    = {C.~K.~I. Williams},
  title     = {Prediction with {G}aussian Processes: From Linear
               Regression to Linear Prediction and Beyond},
  booktitle = {Learning and Inference in Graphical Models},
  publisher = {Kluwer Academic},
  year      = {1998},
  editor    = {M.~I. Jordan},
  pages     = {599--621}
}

@incollection{Williams99,
  author    = {C.~K.~I. Williams},
  title     = {Prediction with {G}aussian Processes: From Linear
               Regression to Linear Prediction and Beyond},
  booktitle = {Learning and Inference in Graphical Models},
  publisher = {{MIT} Press},
  year      = {1999},
  editor    = {M.~I. Jordan},
  pages     = {599--621}
}

@article{Williamson13,
  author = {Robert Williamson},
  title  = {Big data and big business: it's what you do with it that
            matters},
  year   = {2013},
  month  = {Nov},
  url    = {https://theconversation.com/big-data-and-big-business-its-what-you-do-with-it-that-matters-20543}
}

@inproceedings{Williamson98,
  author       = {R.~C. Williamson},
  title        = {Some Results in Statistical Learning Theory with Relevance
                  to Nonlinear System Identification},
  booktitle    = {Nonlinear Control Systems Design Symposium 1998
                  {(NOLCOS98)}},
  year         = {1998},
  volume       = {2},
  pages        = {443--448},
  organization = {{IFAC}},
  publisher    = {Elsevier Science Publishers}
}

@article{Williamson_CompositeMulticlass_2016,
  title    = {Composite multiclass losses},
  volume   = {17},
  number   = {222},
  journal  = {Journal of Machine Learning Research},
  author   = {Williamson, Robert C. and Vernet, Elodie and Reid, Mark
              D.},
  year     = {2016},
  keywords = {⛔ No DOI found, \#nosource},
  pages    = {1--52},
  file     = {Williamson et al - Composite multiclass losses
              (2016).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Williamson et al - Composite multiclass losses
              (2016).pdf:application/pdf}
}

@inproceedings{Williamson_GeometryLosses_2014,
  address   = {Barcelona, Spain},
  series    = {Proceedings of {Machine} {Learning} {Research}},
  title     = {The geometry of losses},
  volume    = {35},
  abstract  = {Loss functions are central to machine learning because
               they are the means by which the quality of a prediction is
               evaluated. Any loss that is not proper, or can not be
               transformed to be proper via a link function is
               inadmissible. All admissible losses for n-class problems
               can be obtained in terms of a convex body in
               {\textbackslash}mathbbR{\textasciicircum}n. We show this
               explicitly and show how some existing results simplify when
               viewed from this perspective. This allows the development
               of a rich algebra of losses induced by binary operations on
               convex bodies (that return a convex body). Furthermore it
               allows us to define an “inverse loss” which provides a
               universal “substitution function” for the Aggregating
               Algorithm. In doing so we show a formal connection between
               proper losses and norms.},
  booktitle = {Proceedings of the 27th conference on learning theory},
  publisher = {PMLR},
  author    = {Williamson, Robert C.},
  editor    = {{Maria Florina Balcan} and {Vitaly Feldman} and {Csaba
               Szepesvári}},
  month     = may,
  year      = {2014},
  keywords  = {\#nosource},
  pages     = {1078--1108}
}

@article{WilMar00,
  author    = {D. Randall Wilson and Tony R. Martinez},
  title     = {Reduction Techniques for Instance-Based Learning
               Algorithms},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {38},
  pages     = {257--286},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{WilMar97,
  author    = {D. Randall Wilson and Tony R. Martinez},
  title     = {Instance pruning techniques},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1997},
  pages     = {403--411},
  publisher = {Morgan Kaufmann}
}

@article{WilMcN94,
  author  = {M.~A. Wilson and B.~L. McNaughton},
  title   = {Reactivation of hippocampal ensemble memories during
             sleep},
  journal = {Science},
  year    = {1994},
  volume  = {265},
  pages   = {676--679}
}

@inproceedings{WilRas96,
  author    = {C. Williams and C. Rasmussen},
  title     = {Gaussian Processes for Regression},
  booktitle = {Advances in Neural Information Processing Systems 8},
  year      = {1996},
  editor    = {D.~S. Touretzky and M.~C. Mozer and M.~E. Hasselmo},
  volume    = {8},
  pages     = {514-520},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@unpublished{WilSchSmo00,
  author = {R.~C. Williamson and B. Sch{\"{o}}lkopf and A. J. Smola},
  title  = {A {M}aximum {M}argin {M}iscellany},
  note   = {Typescript},
  year   = {2000}
}

@inproceedings{WilSee00,
  author    = {C.~K.~I. Williams and M. Seeger},
  title     = {The Effect of the Input Density Distribution on
               Kernel-based Classifiers},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2000},
  editor    = {P. Langley},
  pages     = {1159--1166},
  address   = {San Francisco, California},
  publisher = {Morgan Kaufmann Publishers}
}

@inproceedings{WilSee00b,
  author    = {C. K. I. Williams and M. Seeger},
  title     = {Using the {Nystr\"{o}m} Method to Speed Up Kernel
               Machines},
  booktitle = nips,
  year      = {2000}
}

@inproceedings{WilSee01,
  author    = {Christoper K.~I. Williams and Matthias Seeger},
  title     = {Using the {N}ystrom method to Speed up Kernel Machines},
  booktitle = {Advances in Neural Information Processing Systems 13},
  year      = {2001},
  editor    = {T.~K. Leen and T.~G. Dietterich and V. Tresp},
  pages     = {682--688},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{WilSmoSch00,
  author    = {R.~C. Williamson and A. J. Smola and B. Sch{\"o}lkopf},
  title     = {Entropy numbers of linear function classes},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2000},
  editor    = {N. Cesa-Bianchi and S. Goldman},
  pages     = {309--319},
  address   = {San Francisco},
  publisher = {Morgan Kaufmann Publishers}
}

@article{WilSmoSch01,
  author  = {R.~C. Williamson and A. J. Smola and B.~{Sch\"olkopf}},
  title   = {Generalization bounds for regularization networks and
             support vector machines via entropy numbers of compact
             operators},
  journal = {IEEE Transactions on Information Theory},
  year    = {2001},
  volume  = {47},
  pages   = {2516--2532},
  number  = {6}
}

@techreport{WilSmoSch98,
  author      = {R.~C. Williamson and A. J. Smola and B. Sch{\"o}lkopf},
  title       = {Generalization Performance of Regularization Networks and
                 Support Vector Machines via Entropy Numbers of Compact
                 Operators},
  institution = {NeuroCOLT},
  year        = {1998},
  number      = {19},
  address     = {http://www.neurocolt.com},
  note        = {Accepted for publication in IEEE Transactions on
                 Information Theory}
}

@inproceedings{WilSmoSch99,
  author    = {R.~C. Williamson and A. J. Smola and B.~{Sch\"olkopf}},
  title     = {Entropy numbers, operators and support vector kernels},
  booktitle = {Advances in Kernel Methods\,---\,Support Vector Learning},
  year      = {1999},
  editor    = {B. Sch{\"o}lkopf and C.~J.~C. Burges and A. J. Smola},
  pages     = {127--144},
  address   = {Cambridge, MA},
  publisher = {{MIT} Press}
}

@inproceedings{WilSmoSch99b,
  author    = {R.~C. Williamson and A. J. Smola and B.~{Sch\"olkopf}},
  title     = {Entropy numbers, operators and support vector kernels},
  booktitle = {Computational Learning Theory: 4th European Conference},
  year      = {1999},
  volume    = {1572},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {285--299},
  publisher = {Springer},
  editor    = {P. Fischer and H.~U. Simon}
}

@inproceedings{Wilson85,
  author    = {S. W. Wilson},
  title     = {Knowledge Growth in an Artificial Animal},
  booktitle = {Proc. of an International Conference on Genetic Algorithms
               and their Applications},
  year      = {1985},
  pages     = {16--23},
  month     = jul,
  comment   = {Empirical results for a `critter' wandering around in the
               woods trying to find food, using a genetic algorithm a la
               Holland to learn general situation/action rules.}
}

@article{Wilson87,
  author    = {Stewart W. Wilson},
  title     = {Classifier Systems and the Animat Problem},
  journal   = {Machine Learning},
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  title     = {{VC} dimension of an integrate-and-fire neuron model},
  booktitle = {Proc. 9th Annu. Conf. on Comput. Learning Theory},
  year      = {1996},
  pages     = {10--18},
  publisher = {ACM Press, New York, NY}
}

@inbook{ZadPea97,
  chapter   = {13},
  pages     = {211--225},
  title     = {The discriminative power of a dynamical model neuron},
  publisher = mitpr,
  year      = {1997},
  author    = {Anthony M. Zador and Barak A. Pearlmutter},
  volume    = {IV: Making Learning Systems Practical},
  booktitle = {Computational Learning Theory and Natural Learning
               Systems}
}

@inproceedings{Zadrozny04,
  author    = {B. Zadrozny},
  title     = {Learning and evaluating classifiers under sample selection
               bias},
  booktitle = icml,
  year      = {2004}
}

@article{Zaffaroni_ConvexCoradiant_2008,
  title    = {Convex coradiant sets with a continuous concave cogauge},
  volume   = {15},
  issn     = {0944-6532},
  number   = {2},
  journal  = {Journal of Convex Analysis},
  author   = {Zaffaroni, Alberto},
  year     = {2008},
  note     = {tex.fjournal: tex.mrclass: 52A07 (46A55 46B20)
              tex.mrnumber: 2422993 tex.mrreviewer: Wolfgang Lusky},
  keywords = {⛔ No DOI found},
  pages    = {325--343},
  file     = {Zaffaroni - Convex coradiant sets with a continuous
              concave cogauge
              (2008).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Zaffaroni - Convex coradiant sets with a continuous
              concave cogauge (2008).pdf:application/pdf}
}

@article{Zaffaroni_ConvexRadiant_2013,
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              gauge},
  volume   = {20},
  issn     = {0944-6532},
  number   = {2},
  journal  = {Journal of Convex Analysis},
  author   = {Zaffaroni, Alberto},
  year     = {2013},
  note     = {tex.fjournal: Journal of Convex Analysis tex.mrclass:
              52A07 (46A55) tex.mrnumber: 3098468 tex.mrreviewer:
              Wolfgang Lusky},
  keywords = {⛔ No DOI found},
  pages    = {307--328},
  file     = {Zaffaroni - Convex radiant costarshaped sets and the least
              sublinear gauge
              (2013).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Zaffaroni - Convex radiant costarshaped sets and
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@inproceedings{ZafHut02,
  author    = {M. Zaffalon and M. Hutter},
  title     = {Robust Feature Selection using Distributions of Mutual
               Information},
  year      = {2002},
  pages     = {577--584},
  booktitle = {the 18th International Conference on Uncertainty in
               Artificial Intelligence (UAI-2002)},
  editor    = {A. Darwiche and N. Friedman},
  publisher = {Morgan Kaufmann},
  address   = {San Francisco, CA.}
}

@inproceedings{Zaheeretal17,
  author    = {Manzil Zaheer and Satwik Kottur and Siamak Ravanbakhsh and
               Barnabas Poczos and Ruslan Salakhutdinov and Alexander
               Smola},
  title     = {Deep Sets},
  booktitle = nips,
  year      = {2017}
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@article{Zaki01,
  author    = {Mohammed J. Zaki},
  title     = {{SPADE}: {A}n Efficient Algorithm for Mining Frequent
               Sequences},
  journal   = {Machine Learning},
  year      = {2001},
  volume    = {42},
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}

@book{Zalinescu02,
  title     = {Convex Analysis in General Vector Spaces},
  publisher = {World Scientific},
  year      = {2002},
  author    = {Constantin Z{\u a}linescu}
}

@article{Zalinescu_AbstractControl_1980,
  title   = {On an abstract control problem},
  volume  = {2},
  issn    = {0163-0563},
  doi     = {10.1080/01630568008816074},
  number  = {6},
  journal = {Numerical Functional Analysis and Optimization},
  author  = {Zălinescu, Constantin},
  year    = {1980},
  note    = {tex.fjournal: Numerical Functional Analysis and
             Optimization tex.mrclass: 49A51 (90C25) tex.mrnumber:
             605758 tex.mrreviewer: Jean-Paul Penot},
  pages   = {531--542},
  file    = {Zălinescu - On an abstract control problem
             (1980).pdf:/Users/zcranko/Documents/Library/Journal
             Article/Zălinescu - On an abstract control problem
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@book{Zalinescu_ConvexAnalysis_2002,
  address   = {River Edge, NJ, USA},
  title     = {Convex analysis in general vector spaces},
  isbn      = {978-981-238-067-8},
  language  = {eng},
  publisher = {World Scientific},
  author    = {Zălinescu, Constantin},
  year      = {2002},
  note      = {OCLC: 845511462},
  file      = {Zălinescu - Convex analysis in general vector spaces
               (2002).pdf:/Users/zcranko/Documents/Library/Book/Zălinescu
               - Convex analysis in general vector spaces
               (2002).pdf:application/pdf}
}

@article{Zalinescu_DifferentiabilitySupport_2013,
  title    = {On the differentiability of the support function},
  volume   = {57},
  issn     = {1573-2916},
  doi      = {10.1007/s10898-012-9918-z},
  abstract = {The support function is strongly related to the cost
              function in economics. Because the differentiability of the
              cost function is an important property, being related to
              the celebrated Shephard’s lemma, our propose is to study
              the differentiability of the support function of a nonempty
              closed and convex subset of a finite dimensional normed
              space. So we provide characterizations of the
              differentiability of the support function on three subsets
              of its domain.},
  number   = {3},
  journal  = {Journal of Global Optimization},
  author   = {Zălinescu, Constantin},
  month    = nov,
  year     = {2013},
  pages    = {719--731},
  file     = {Zălinescu - On the differentiability of the support
              function
              (2013).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Zălinescu - On the differentiability of the
              support function (2013).pdf:application/pdf}
}

@article{Zalinescu_DualityResults_2009,
  title   = {Duality results involving functions associated to nonempty
             subsets of locally convex spaces},
  volume  = {103},
  doi     = {10.1007/bf03191905},
  journal = {Revista de la Real Academia de Ciencias Exactas, Físicas
             y Naturales. Serie A, Matemáticas},
  author  = {Zălinescu, Constantin},
  month   = sep,
  year    = {2009},
  pages   = {219--234},
  file    = {Zălinescu - Duality results involving functions
             associated to nonempty subsets of locally
             (2009).pdf:/Users/zcranko/Documents/Library/Journal
             Article/Zălinescu - Duality results involving functions
             associated to nonempty subsets of locally
             (2009).pdf:application/pdf}
}

@article{Zalinescu_RecessionCones_1993,
  title    = {Recession cones and asymptotically compact sets},
  volume   = {77},
  issn     = {1573-2878},
  doi      = {10.1007/bf00940787},
  abstract = {The present note is concerned with the study of the
              relations between the notions of asymptotic cones
              introduced by Dedieu and that of recession cones introduced
              by Luc. Conditions under which these notions coincide are
              given, as well as the fact that the compactness condition
              used by Luc is related (more restrictively) to asymptotic
              compactness. As an application of these notions, a result
              on proper efficiency in the sense of Lampe, established by
              Luc in finite dimensions, is extended to the
              infinite-dimensional case.},
  number   = {1},
  journal  = {Journal of Optimization Theory and Applications},
  author   = {Zălinescu, Constantin},
  month    = apr,
  year     = {1993},
  pages    = {209--220},
  file     = {Zălinescu - Recession cones and asymptotically compact
              sets (1993).pdf:/Users/zcranko/Documents/Library/Journal
              Article/Zălinescu - Recession cones and asymptotically
              compact sets (1993).pdf:application/pdf}
}

@article{Zalinescu_RelationsConvexity_2016,
  title   = {Relations between the convexity of a set and the
             differentiability of its support function},
  volume  = {65},
  issn    = {0233-1934},
  doi     = {10.1080/02331934.2015.1044528},
  number  = {3},
  journal = {Optimization},
  author  = {Zălinescu, Constantin},
  month   = mar,
  year    = {2016},
  pages   = {651--670},
  file    = {Zălinescu - Relations between the convexity of a set and
             the differentiability of its
             (2016).pdf:/Users/zcranko/Documents/Library/Journal
             Article/Zălinescu - Relations between the convexity of a
             set and the differentiability of its
             (2016).pdf:application/pdf}
}

@incollection{Zalinescu_StabilityClass_1989,
  address   = {Boston, MA, USA},
  title     = {Stability for a class of nonlinear optimization problems
               and applications},
  isbn      = {978-1-4757-6019-4},
  abstract  = {The aim of this chapter is to give a unified approach to
               some problems in nonlinear optimization using asymptotic
               cones, recession functions and asymptotically compact sets.
               Thus we establish a stability result for a class of
               nonconvex programming problems which turns out to be
               equivalent to Dedieu’s criterion for the closedeness of
               the image of a closed set by a multifunction. Also we
               obtain a formula for the recession function of the marginal
               function for the first time. This formula seems to be
               important and new also in the finite dimensional case. The
               convex version of the stability result is used to reobtain
               formulae for the conjugates, ϵ-subdifferentials and
               recession functions of some convex functions, results which
               are comparable with those of McLinden. It is also shown
               that in some cases one can perturbe the objective function
               of a family of convex problems such that the resulting
               problems have optimal solutions; the behaviour of the
               values of these perturbed problems and their solutions is
               also investigated. Another result establishes the
               relationship between conically compact sets introduced by
               Isac and Théra and asymptotic cones.},
  booktitle = {Nonsmooth {Optimization} and {Related} {Topics}},
  publisher = {Springer},
  author    = {Zălinescu, Constantin},
  editor    = {Clarke, Francis H. and Dem’yanov, V. F. and Giannessi,
               F.},
  year      = {1989},
  doi       = {10.1007/978-1-4757-6019-4_26},
  pages     = {437--458},
  file      = {Zălinescu - Stability for a class of nonlinear
               optimization problems and applications
               (1989).pdf:/Users/zcranko/Documents/Library/Book
               Section/Zălinescu - Stability for a class of nonlinear
               optimization problems and applications
               (1989).pdf:application/pdf}
}

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  year    = {2006},
  month   = {July}
}

@article{ZanSerZanBenetal06,
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             and E. Parrado-Hernandez},
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  volume  = {7}
}

@misc{ZarHer01,
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  author  = {T. Zaslavsky},
  title   = {Signed graphs},
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@book{Zemanian87,
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@article{Zeugmann83b,
  author  = {T. Zeugmann},
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@phdthesis{Zeugmann83c,
  author = {T. Zeugmann},
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@article{Zeugmann84,
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@inproceedings{Zeugmann86,
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  year      = {1986},
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}

@article{Zeugmann88,
  author  = {T. Zeugmann},
  title   = {On the Power of Recursive Optimizers},
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  year    = {1988},
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@inproceedings{Zeugmann90,
  author    = {T. Zeugmann},
  title     = {Inductive Inference of Optimal Programs: {A} Survey and
               Open Problems},
  booktitle = {Nonmonotonic and Inductive Logic, 1st International
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  year      = {1990},
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}

@techreport{Zeugmann93,
  author      = {T. Zeugmann},
  title       = {{\it Algorithmisches {L}ernen von {F}unktionen und
                 {S}prachen}},
  institution = {Technische Hochschule Darmstadt, Fachbereich Informatik},
  year        = {1993},
  number      = {Habilitationsschrift}
}

@inproceedings{Zeugmann94,
  author    = {T. Zeugmann},
  title     = {Average-Case Analysis of Pattern Language Learning
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  booktitle = {Proc. 4th Internat. Workshop on Analogical and Inductive
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               Learning Theory},
  year      = {1994},
  volume    = {872},
  series    = {Lecture Notes in Artificial Intelligence},
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  publisher = {Springer-Verlag}
}

@article{Zeugmann97,
  author  = {T. Zeugmann},
  title   = {Foreword},
  journal = {Theoret. Comput. Sci.},
  year    = {1997},
  volume  = {185},
  pages   = {1--1},
  number  = {1},
  note    = {Special issue for ALT '95}
}

@article{Zeugmann98,
  author  = {T. Zeugmann},
  title   = {Lange and {W}iehagen's Pattern Language Learning
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  year    = {1998},
  volume  = {23},
  pages   = {117--145},
  note    = {Special issue for AII '94 and ALT '94, first appeared as
             RIFIS-TR-CS-111, 1995}
}

@incollection{ZeuLan95,
  author    = {T. Zeugmann and S. Lange},
  title     = {A Guided Tour Across the Boundaries of Learning Recursive
               Languages},
  booktitle = {Algorithmic Learning for Knowledge-Based Systems},
  publisher = {Springer-Verlag},
  year      = {1995},
  volume    = {961},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {190--258},
  comment   = {An earlier version appeared as GOSLER-Report 25/94, FB
               Mathematik und Informatik, TH Lepzig, 1994.}
}

@article{ZeuLanKap95,
  author  = {T. Zeugmann and S. Lange and S. Kapur},
  title   = {Characterizations of monotonic and dual monotonic language
             learning},
  journal = {Inform. Comput.},
  year    = {1995},
  volume  = {120},
  pages   = {155--173},
  number  = {2},
  comment = {A preliminary version appeared as Technical Report IRCS
             92-24, Institute for Research in Cognitive Science, Univ.
             Pennsylvania, Philadelphia, 1992.}
}

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  month     = {June}
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@techreport{ZhaAnd05,
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  year        = {2005},
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@inbook{ZhaAtk94,
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@inproceedings{ZhaBenHaretal17,
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  year      = {2017}
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@article{ZhaCisDauLop18,
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@inproceedings{ZhaCuiNeuChe18,
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@inproceedings{ZhaDinFu17,
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               learnable in polynomial time},
  author    = {Zhang, Yuchen and Lee, Jason D and Jordan, Michael I},
  booktitle = icml,
  year      = {2016}
}

@inproceedings{ZhaLeeTeh13,
  abstract  = {Incorporating invariance information is important for many
               learning problems. To exploit invariances, most existing
               methods resort to approximations that either lead to
               expensive optimization problems such as semi-definite
               programming, or rely on separation oracles to retain
               tractability. Some methods further limit the space of
               functions and settle for non-convex models. In this paper,
               we propose a framework for learning in reproducing kernel
               Hilbert spaces (RKHS) using local invariances that
               explicitly characterize the behavior of the target function
               around data instances. These invariances are compactly
               encoded as linear functionals whose value are penalized by
               some loss function. Based on a representer theo-rem that we
               establish, our formulation can be efficiently optimized via
               a convex program. For the representer theorem to hold, the
               linear functionals are required to be bounded in the RKHS,
               and we show that this is true for a variety of com-monly
               used RKHS and invariances. Experiments on learning with
               unlabeled data and transform invariances show that the
               proposed method yields better or similar results compared
               with the state of the art.},
  author    = {Zhang, X and Lee, W S and Teh, Yee Whye},
  booktitle = nips,
  title     = {{Learning with Invariances via Linear Functionals on
               Reproducing Kernel {H}ilbert Space}},
  year      = {2013}
}

@inproceedings{ZhaLeeTeh2013,
  title     = {Learning with invariance via linear functionals on
               reproducing kernel hilbert space},
  author    = {Zhang, Xinhua and Lee, Wee Sun and Teh, Yee Whye},
  booktitle = nips,
  year      = {2013}
}

@inproceedings{ZhaLiaWai17,
  title     = {Convexified Convolutional Neural Networks},
  booktitle = icml,
  author    = {Yuchen Zhang and Percy Liang and Martin Wainwright},
  year      = {2017}
}

@article{ZhaLiuGotGor10,
  author    = {Lei Zhang and Ligang Liu and Craig Gotsman and Steven J.
               Gortler},
  title     = {An as-rigid-as-possible approach to sensor network
               localization},
  journal   = {ACM Transactions on Sensor Networks},
  volume    = 6,
  number    = 4,
  year      = 2010,
  pages     = {1--21},
  publisher = {ACM},
  address   = {New York, NY, USA}
}

@article{ZhaMic95,
  author    = {Jianping Zhang and Ryszard S. Michalski},
  title     = {An Integration of Rule Induction and Exemplar-Based
               Learning for Graded Concepts},
  journal   = {Machine Learning},
  year      = {1995},
  volume    = {21},
  pages     = {235--267},
  number    = {3},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Zhang00,
  author    = {Wei Zhang},
  title     = {Some Improvements on Event-Sequence Temporal Region
               Methods},
  booktitle = {Machine Learning: ECML 2000, 11th European Conference on
               Machine Learning, Barcelona, Catalonia, Spain, May 31 -
               June 2, 2000, Proceedings},
  year      = {2000},
  volume    = {1810},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {446--458},
  publisher = {Springer, Berlin}
}

@inproceedings{Zhang01,
  author    = {T. Zhang},
  title     = {Leave-one-out Cross Validation Bound for Kernel Methods
               with Applications in Learning},
  booktitle = {Proc.\ Annual Conf.\ Computational Learning Theory},
  year      = {2001},
  editor    = {D. Hembold and R.~C. Williamson},
  note      = {submitted}
}

@inproceedings{Zhang01b,
  author    = {T. Zhang},
  title     = {Some Sparse Approximation Bounds for Regression Problems},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2001},
  pages     = {624-631},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{Zhang03,
  author  = {Tong Zhang},
  title   = {Sequential Greedy Approximation for Certain Convex
             Optimization Problems},
  journal = {IEEE Transactions on Information Theory},
  year    = {2003},
  volume  = {49},
  pages   = {682--691},
  number  = {3}
}

@inproceedings{Zhang04,
  author    = {T. Zhang},
  title     = {Solving large scale linear prediction problems using
               stochastic gradient descent algorithms},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {2004},
  publisher = {ACM Press}
}

@article{Zhang04b,
  author  = {T. Zhang},
  title   = {Statistical behavior and consistency of classification
             methods based on convex risk minimization},
  journal = {Ann. Statist.},
  year    = {2004},
  volume  = {32},
  pages   = {56-85},
  number  = {1}
}

@unpublished{Zhang08,
  title  = {Very Gentle Note on the Construction of {D}irichlet
            Process},
  author = {Xinhua Zhang},
  note   = {The Australian National University},
  year   = {2008}
}

@techreport{Zhang10,
  author      = {Xinhua Zhang},
  title       = {{BFGS} for Composite Convex Objectives},
  institution = {University of Alberta},
  year        = {2010},
  url         = {http://webdocs.cs.ualberta.ca/~xinhua2/pubDoc/Zhang10.pdf}
}

@unpublished{Zhang99,
  author = {T. Zhang},
  title  = {Analysis of Regularised Linear Functions for
            Classification Problems},
  note   = {IBM Research Report RC-21572},
  year   = {1999}
}

@inproceedings{Zhang99b,
  author    = {X. Zhang},
  title     = {Using Class-Center Vectors to Build Support Vector
               Machines},
  booktitle = {{NNSP}'99},
  year      = {1999}
}

@inproceedings{Zhangetal07,
  title     = {Binary Matrix Factorization with Applications},
  author    = {Zhongyuan Zhang and Tao Li and Chris Ding and Xiangsun
               Zhang},
  booktitle = icdm,
  year      = {2007}
}

@inproceedings{zhangetal08,
  author    = {D. Zhang and F. Wang and C. Zhang and T. Li},
  title     = {Multi-View Local Learning},
  booktitle = aaai,
  year      = {2008},
  pages     = {752--757}
}

@inproceedings{Zhangetal11,
  author    = {Xinhua Zhang and Yaoliang Yu and Martha White and Ruitong
               Huang and Dale Schuurmans},
  title     = {Convex Sparse Coding, Subspace Learning, and
               Semi-Supervised Extensions},
  booktitle = aaai,
  year      = {2011}
}

@inproceedings{zhangetal11a,
  author    = {Zhang, Dan and He, Jingrui and Liu, Yan and Si, Luo and
               Lawrence, Richard},
  title     = {Multi-view transfer learning with a large margin
               approach},
  booktitle = kdd,
  year      = {2011},
  pages     = {1208--1216}
}

@inproceedings{Zhangetal12,
  author    = {Xinhua Zhang and Yaoliang Yu and Dale Schuurmans},
  title     = {Accelerated Training for Matrix-Norm Regularization: A
               Boosting Approach},
  booktitle = nips,
  year      = {2012}
}

@inproceedings{Zhangetal13a,
  author    = {Xinhua Zhang and Yaoliang Yu and Dale Schuurmans},
  title     = {Polar Operators for Structured Sparse Estimation},
  booktitle = nips,
  year      = {2013}
}

@inproceedings{Zhangetal18,
  title     = {Efficient Neural Network Robustness Certification with
               General Activation Functions},
  author    = {Huan Zhang and Tsui-Wei Weng and Pin-Yu Chen and Cho-Jui
               Hsieh and Luca Daniel},
  booktitle = nips,
  year      = {2018}
}

@inproceedings{Zhangetal19,
  author    = {Hongyang Zhang and Yaodong Yu and Jiantao Jiao and Eric P.
               Xing and Laurent El Ghaoui and Michael I. Jordan},
  title     = {Theoretically Principled Trade-off between Robustness and
               Accuracy},
  booktitle = icml,
  year      = {2019}
}

@article{zhangzhang11,
  author  = {J. Zhang and C. Zhang},
  title   = {Multitask {Bregman} clustering},
  journal = {Neurocomputing},
  year    = {2011},
  volume  = {74},
  pages   = {1720--1734},
  number  = {10}
}

@inproceedings{ZhaNum97,
  author    = {Xiaolong Zhang and Masayuki Numao},
  title     = {Learning and Revising Theories in Noisy Domains},
  booktitle = alt-ws,
  year      = {1997},
  volume    = {1316},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {339--351},
  publisher = {Springer-Verlag}
}

@inproceedings{ZhaOle00,
  author    = {Tong Zhang and Frank J. Oles},
  title     = {A Probability Analysis on the Value of Unlabeled Data for
               Classification Problems},
  booktitle = icml,
  year      = {2000},
  pages     = {1191--1198},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@article{ZhaOle01,
  author  = {Tong Zhang and Frank J. Oles},
  title   = {Text Categorization Based on Regularized Linear
             Classification Methods},
  journal = {Information Retrieval},
  year    = {2001},
  volume  = {4},
  pages   = {5--31}
}

@inproceedings{ZhaPenXia13,
  title     = {Heterogeneous Metric Learning with Joint Graph
               Regularization for Cross-Media Retrieval},
  author    = {Zhai, Xiaohua and Peng, Yuxin and Xiao, Jianguo},
  booktitle = aaai,
  year      = {2013}
}

@article{ZhaRekBer06,
  author  = {W. Zhang and R. Rekaya and K. Bertrand},
  title   = {A Method for Predicting Disease Subtypes in Presence of
             Misclassification Among Training Samples Using Gene
             Expression: Application to Human Breast Cancer},
  journal = {Bioinformatics},
  volume  = {22},
  number  = {3},
  pages   = {317-325},
  month   = {Feb},
  year    = {2006}
}

@article{ZhaRekBer06b,
  author  = {W. Zhang and R. Rekaya and K. Bertrand},
  title   = {A Method for Predicting Disease Subtypes in Presence of
             Misclassification Among Training Samples Using Gene
             Expression: Application to Human Breast Cancer},
  journal = {Bioinformatics},
  volume  = {22},
  number  = {3},
  pages   = {317-325},
  month   = {Feb},
  year    = {2006}
}

@article{ZhaRocYu09,
  author  = {Peng Zhao and Guilherme Rocha and Bin Yu},
  title   = {The composite absolute penalties family for grouped and
             hierarchical variable selection},
  journal = {Annals of Statistics},
  year    = {2009},
  volume  = {37},
  pages   = {3468--3497},
  number  = {6A}
}

@inproceedings{ZhaSahVis10a,
  author    = {Xinhua Zhang and Ankan Saha and S.~V.~N. Vishwanathan},
  title     = {Lower Bounds on Rate of Convergence of Cutting Plane
               Methods},
  booktitle = nips,
  year      = {2011}
}

@techreport{ZhaSahVis10b,
  author = {Xinhua Zhang and Ankan Saha and S.~V.~N. Vishwanathan},
  title  = {Faster Rates for Training Max-Margin Markov Networks},
  year   = {2010},
  type   = {Technical report},
  number = {arXiv:1003.1354},
  url    = {http://arxiv.org/abs/1003.1354}
}

@techreport{ZhaSahVis10c,
  author = {Xinhua Zhang and Ankan Saha and S.~V.~N. Vishwanathan},
  title  = {Regularized Risk Minimization by {N}esterov's Accelerated
            Gradient Methods: Algorithmic Extensions and Empirical
            Studies},
  year   = {2010},
  type   = {Technical report},
  number = {arXiv:1011.0472},
  url    = {http://arxiv.org/abs/1011.0472}
}

@techreport{ZhaSahVis10d,
  author = {Xinhua Zhang and Ankan Saha and S.~V.~N. Vishwanathan},
  title  = {Lower Bounds on Rate of Convergence of Cutting Plane
            Methods (long version)},
  year   = {2010},
  type   = {Technical report},
  note   = {
            \href{http://www.stat.purdue.edu/~vishy/papers/ZhaSahVis10_long.pdf}{http://www.stat.purdue.edu/$\sim$vishy/papers/ZhaSahVis10\_long.pdf}
            }
}

@inproceedings{ZhaSahVis11,
  author    = {Xinhua Zhang and Ankan Saha and S.~V.~N.~Vishwanathan},
  title     = {Smoothing Multivariate Performance Measures},
  booktitle = uai,
  year      = 2011
}

@article{ZhaSahVis12,
  author  = {Xinhua Zhang and Ankan Saha and S.~V.~N. Vishwanathan},
  title   = {Smoothing Multivariate Performance Measures},
  journal = jmlr,
  volume  = 13,
  pages   = {3589--3646},
  month   = {Dec},
  year    = 2012
}

@article{ZhaSahVis14,
  author  = {Xinhua Zhang and Ankan Saha and S.~V.~N. Vishwanathan},
  title   = {Accelerated Training of Max-Margin {M}arkov Networks with
             Kernels},
  journal = {Journal of Theoretical Computer Science (TCS)},
  volume  = 519,
  pages   = {88--102},
  month   = {Jan},
  year    = 2014
}

@techreport{ZhaSch11,
  author      = {Xinhua Zhang and Dale Schuurmans},
  title       = {Deep Boosting},
  institution = {University of Alberta},
  year        = {2010},
  url         = {http://webdocs.cs.ualberta.ca/~xinhua2/pubDoc/deepBoost.pdf}
}

@inproceedings{ZhaSonGreSmo09,
  author    = {Xinhua Zhang and Le Song and Arthur Gretton and Alex
               Smola},
  title     = {Kernel Measures of Independence for Non-iid Data},
  booktitle = nips,
  year      = {2009}
}

@inproceedings{ZhaTsaKwo07,
  author    = {Kai Zhang and Ivor Tsang and James Kwok},
  title     = {Maximum margin clustering made practical},
  booktitle = icml,
  year      = 2007,
  isbn      = {978-1-59593-793-3},
  pages     = {1119--1126},
  location  = {Corvalis, Oregon},
  publisher = {ACM},
  address   = {New York, NY, USA}
}

@inproceedings{ZhaTsaKwo08,
  title     = {Improved Nystr{\:o}m Low-Rank Approximation and Error
               Analysis},
  author    = {Kai Zhang and Ivor W. Tsang and James T. Kwok},
  booktitle = icml,
  year      = {2008}
}

@inproceedings{ZhaWan07,
  author    = {Zhenyue Zhang and Jing Wang},
  title     = {{MLLE}: Modified Locally Linear Embedding Using Multiple
               Weights},
  booktitle = nips,
  pages     = {1593--1600},
  year      = {2007}
}

@techreport{ZhaWarFen04,
  author      = {B. Zhang and J. Ward and Q. Feng},
  title       = {A Simultaneous Parametric Maximum-Flow Algorithm for
                 Finding the Complete Chain of Solutions},
  institution = {Hewlett Packard Laboratories},
  number      = {{HPL}-2004-189},
  year        = {2004},
  url         = {http://www.hpl.hp.com/techreports/2004/HPL-2004-189.html}
}

@techreport{ZhaWarFen05,
  author      = {B. Zhang and J. Ward and A. Feng},
  title       = {A Simultaneous Maximum Flow Algorithm for the Selection
                 Model},
  institution = {Hewlett Packard Laboratories},
  number      = {{HPL}-2005-91},
  year        = {2005},
  url         = {http://www.hpl.hp.com/techreports/2005/HPL-2005-91.html}
}

@techreport{ZhaWarFen05b,
  author      = {B. Zhang and J. Ward and Q. Feng},
  title       = {Simultaneous Parametric Maximum Flow Algorithm with Vertex
                 Balancing},
  institution = {Hewlett Packard Laboratories},
  number      = {{HPL}-2005-121},
  year        = {2005},
  url         = {http://www.hpl.hp.com/techreports/2005/HPL-2005-121.html}
}

@inproceedings{ZhaXia15,
  author    = {Yuchen Zhang and Lin Xiao},
  title     = {Stochastic Primal-Dual Coordinate Method for Regularized
               Empirical Risk Minimization},
  booktitle = icml,
  year      = {2015}
}

@article{ZhaXuZha09,
  title   = {Reproducing Kernel {B}anach Spaces for Machine Learning},
  author  = {Haizhang Zhang and Yuesheng Xu and Jun Zhang},
  journal = jmlr,
  year    = {2009},
  volume  = {10},
  pages   = {2741--2775}
}

@inproceedings{ZhaYanGha04,
  author    = {Jian Zhang and Yiming Yang and Zoubin Ghahramani},
  title     = {A Probabilistic Model for Online Document Clustering with
               Application to Novelty Detection},
  year      = {2004},
  booktitle = {{Neural Information Processing Systems}}
}

@techreport{ZhaYu10,
  author      = {Xinhua Zhang and Yao-Liang Yu},
  title       = {{$O(1/\epsilon)$} Lower Bounds for Convex Optimization
                 with Stochastic Oracles},
  institution = {University of Alberta},
  year        = {2010},
  url         = {http://webdocs.cs.ualberta.ca/~xinhua2/papers/YuZha10.pdf}
}

@inproceedings{ZhaYuSch12,
  author    = {X. Zhang and Y. Yu and D. Schuurmans},
  title     = {Accelerated Training for Matrix-norm Regularization: A
               Boosting Approach},
  booktitle = nips,
  year      = 2012
}

@inproceedings{ZhaYuWhietal11,
  author    = {Xinhua Zhang and Yao-Liang Yu and Martha White and Ruitong
               Huang and Dale Schuurmans},
  title     = {Convex Sparse Coding, Subspace Learning, and
               Semi-Supervised Extensions},
  booktitle = aaai,
  year      = {2011}
}

@article{ZhaZha11,
  title   = {Frames, Riesz bases, and sampling expansions in Banach
             spaces via semi-inner products},
  journal = {Applied and Computational Harmonic Analysis},
  volume  = {31},
  number  = {1},
  pages   = {1--25},
  year    = {2011},
  author  = {Haizhang Zhang and Jun Zhang}
}

@article{Zheng00,
  author    = {Zijian Zheng},
  title     = {Constructing {X}-of-{N} Attributes for Decision Tree
               Learning},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {40},
  pages     = {35--75},
  number    = {1},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{ZheNgJor01,
  author    = {A. Zheng and A. Ng and M.~I. Jordan },
  title     = {Stable Eigenvector Algorithms for Link Analysis},
  booktitle = {the 24th Annual International {ACM} {SIGIR} Conference on
               Research and Development in Information Retrieval},
  year      = {2001},
  editor    = {W. Croft and D. Harper and D. Kraft and J. Zobel},
  pages     = {258--266},
  address   = {New York},
  publisher = {ACM Press}
}

@article{ZheWeb00,
  author    = {Zijian Zheng and Geoffrey I. Webb},
  title     = {Lazy Learning of {B}ayesian Rules},
  journal   = {Machine Learning},
  year      = {2000},
  volume    = {41},
  pages     = {53--84},
  number    = {1},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{ZheWebTin99,
  author    = {Zijian Zheng and Geoffrey I. Webb and Kai Ming Ting},
  title     = {Lazy {B}ayesian rules: a lazy semi-naive {B}ayesian
               learning technique competitive to boosting decision trees},
  booktitle = {Proc.\ Intl.\ Conf.\ Machine Learning},
  year      = {1999},
  pages     = {493--502},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{ZheZhaSun08,
  author    = {Zhaohui Zheng and Hongyuan Zha and Gordon Sun},
  title     = {Query-Level Learning to Rank Using Isotonic Regression},
  booktitle = {Learning to Rank for Information Retrieval},
  year      = {2008}
}

@incollection{ZheZhaZhaChaetal08,
  author    = {Zhaohui Zheng and Hongyuan Zha and Tong Zhang and Olivier
               Chapelle and Keke Chen and Gordon Sun},
  title     = {A General Boosting Method and its Application to Learning
               Ranking Functions for Web Search},
  booktitle = {Advances in Neural Information Processing Systems 20},
  publisher = mitpr,
  year      = {2008},
  editor    = {J.C. Platt and D. Koller and Y. Singer and S. Roweis},
  pages     = {1697--1704},
  address   = {Cambridge, MA}
}

@inproceedings{ZhoBro99,
  author    = {Yuanhui Zhou and Carla Brodley},
  title     = {A Hybrid Lazy-Eager Approach to Reducing the Computation
               and Memory Requirements of Local Parametric Learning
               Algorithms},
  booktitle = icml,
  year      = {1999},
  pages     = {503--512},
  publisher = {Morgan Kaufmann, San Francisco, CA}
}

@inproceedings{ZhoDiDuetal18,
  title     = {SC2Net: Sparse LSTMs for Sparse Coding},
  author    = {Joey Tianyi Zhou and Kai Di and Jiawei Du and Xi Peng and
               Hao Yang and Sinno Jialin Pan and Ivor Wai-Hung Tsang and
               Yong Liu and Zheng Qin and Rick Siow Mong Goh},
  booktitle = aaai,
  year      = {2018}
}

@inproceedings{ZhoHuaSch05,
  author    = {D. Zhou and J. Huang and B.~{Sch\"olkopf}},
  title     = {Learning from Labeled and Unlabeled Data on a Directed
               Graph},
  booktitle = icml,
  year      = {2005}
}

@inproceedings{ZhoJinHoi10,
  author    = {Yang Zhou and Rong Jin and Steven Chu¨CHong Hoi},
  title     = {Exclusive Lasso for Multi-task Feature Selection},
  booktitle = aistats,
  year      = {2010}
}

@inproceedings{ZhoNieRouVasetal10,
  author    = {Y. Zhou and L. Nie and O. Rouhani-Kalleh and F. Vasile and
               S. Gaffney},
  title     = {Resolving Surface Forms to Wikipedia Topics},
  booktitle = {the 23rd International Conference on Computational
               Linguistics {COLING}},
  year      = {2010},
  month     = {August},
  pages     = {1335--1343}
}

@inproceedings{ZhoOhs93,
  author    = {N. Zhong and S. Ohsuga},
  title     = {A Decomposition Based Induction Model for Discovering
               Concept Clusters from Databases},
  booktitle = alt-ws,
  year      = {1993},
  volume    = {744},
  series    = {Lecture Notes in Artificial Intelligence},
  pages     = {384--397},
  publisher = {Springer-Verlag}
}

@inproceedings{ZhoSch04,
  author    = {D. Zhou and B. {Sch\"olkopf}},
  title     = {Learning from Labeled and unlabeled Data using random
               walks},
  booktitle = {{DAGM}},
  year      = {2004},
  number    = {26}
}

@incollection{ZhoSch06,
  title     = {Discrete Regularization},
  author    = {D. Zhou and B. Sch{\"o}lkopf},
  year      = {2006},
  booktitle = {Semi-Supervised Learning},
  pages     = {221--232},
  publisher = {{MIT} Press}
}

@inproceedings{ZhoTaoWu10,
  author    = {Tianyi Zhou and Dacheng Tao and Xindong Wu},
  title     = {{NESVM}: a Fast Gradient Method for Support Vector
               Machines},
  booktitle = {Proc.\ Intl.\ Conf.\ Data Mining},
  year      = {2010}
}

@article{Zhou02,
  title   = {The covering number in learning theory},
  journal = {Journal of Complexity},
  volume  = {18},
  number  = {3},
  pages   = {739--767},
  year    = {2002},
  author  = {Ding-Xuan Zhou}
}

@article{zhou2010multi,
  title     = {On multi-class cost-sensitive learning},
  author    = {Zhou, Zhi-Hua and Liu, Xu-Ying},
  journal   = {Computational Intelligence},
  volume    = {26},
  number    = {3},
  pages     = {232--257},
  year      = {2010},
  publisher = {Wiley Online Library}
}

@article{Zhou90,
  author    = {Hayong Harry Zhou},
  title     = {{CSM}: {A} Computational Model of Cumulative Learning},
  journal   = {Machine Learning},
  year      = {1990},
  volume    = {5},
  pages     = {383--406},
  publisher = {Kluwer Academic Publishers, Boston}
}

@inproceedings{Zhouetal19,
  author    = {Pan Zhou and Xiaotong Yuan and Huan Xu and Shuicheng Yan
               and Jiashi Feng},
  title     = {Efficient Meta Learning via Minibatch Proximal Update},
  booktitle = nips,
  year      = 2019
}

@techreport{ZhouXiaoZhoDai02,
  author      = {Zhou, Dengyong and Xiao, Baihua and Zhou, Huibin and Dai,
                 Ruwei},
  title       = {Global Geometry of SVM Classifiers},
  institution = {Institute of Automation, Chinese Academy of Sciences},
  year        = {2002},
  number      = {30-5-02}
}

@inproceedings{ZhoZhaSon13,
  title     = {Learning Social Infectivity in Sparse Low-rank Networks
               Using Multi-dimensional Hawkes Processes},
  author    = {K. Zhou and H. Zha and L. Song},
  booktitle = {Artificial Intelligence and Statistics (AISTATS)},
  year      = {2013},
  pdf       = {papers/ZhoZhaSon13.pdf}
}

@inproceedings{ZhoZhaSon13b,
  title     = {Learning Triggering Kernels for Multi-dimensional Hawkes
               Processes},
  author    = {K. Zhou and H. Zha and L. Song},
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#==============
# from 08/09/2020

@inproceedings{MaGanYuZha20,
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@inproceedings{MuaFukDinSch12,
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@inproceedings{IlsTomWel18,
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@inproceedings{TomIlsWel17,
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@inproceedings{Jinetal20a,
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  booktitle = iccv,
  year      = {2015}
}

@article{SanYanPicSum19,
  author  = {Sandfort, Veit and Yan, Ke and Pickhardt, Perry and Summers, Ronald},
  year    = {2019},
  title   = {Data augmentation using generative adversarial networks ({CycleGAN}) to improve generalizability in {CT} segmentation tasks},
  volume  = {9},
  journal = {Scientific Reports}
}

@article{PerWan19,
  title   = {The Effectiveness of Data Augmentation in Image Classification using Deep Learning},
  author  = {Luis Perez and Jason Wang},
  journal = {arXiv preprint arXiv:1712.04621},
  year    = {2019}
}

@inproceedings{AntStoEdw18,
  author    = {Antoniou, Antreas
               and Storkey, Amos
               and Edwards, Harrison},
  editor    = {K{\r{u}}rkov{\'a}, V{\v{e}}ra
               and Manolopoulos, Yannis
               and Hammer, Barbara
               and Iliadis, Lazaros
               and Maglogiannis, Ilias},
  title     = {Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks},
  booktitle = {Artificial Neural Networks and Machine Learning -- ICANN},
  year      = {2018}
}

@unpublished{SynDigits,
  title = {Synthetic Digits},
  note  = {\url{https://www.kaggle.com/prasunroy/synthetic-digits}}
}

@article{ComZhaWanGor20,
  title   = {Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift},
  author  = {Remi Tachet des Combes and Han Zhao and Yu-Xiang Wang and Geoff Gordon},
  journal = {arXiv preprint arXiv:2003.04475},
  year    = {2020}
}


@article{SunDouYanWanetal20,
  title   = {Adversarial Attack and Defense on Graph Data: A Survey},
  author  = {Lichao Sun and Yingtong Dou and Carl Yang and Ji Wang and Philip S. Yu and Lifang He and Bo Li},
  journal = {arXiv:1812.10528},
  year    = {2020}
}

@inproceedings{ZugGun20,
  title     = {Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations},
  author    = {Z{\"u}gner, Daniel and G{\"u}nnemann, Stephan},
  booktitle = kdd,
  year      = {2020}
}

@inproceedings{BojKliGun20,
  title     = {Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More},
  author    = {Aleksandar Bojchevski and Johannes Klicpera and Stephan G{\"u}nnemann},
  booktitle = icml,
  year      = {2020}
}

###############
#  Added after April 16, 2021

@article{AusItaMarUmb91,
  title   = {Incremental algorithms for minimal length paths},
  journal = {Journal of Algorithms},
  volume  = {12},
  number  = {4},
  pages   = {615-638},
  year    = {1991},
  author  = {Giorgio Ausiello and Giuseppe F Italiano and Alberto {Marchetti Spaccamela} and Umberto Nanni}
}

@article{HadRenWol92,
  title   = {A New Lower Bound Via Projection for the Quadratic Assignment Problem},
  journal = {Mathematics of Operations Research},
  volume  = {17},
  number  = {3},
  pages   = {727--739},
  year    = {1992},
  author  = {S. W. Hadley and F. Rendl and H. Wolkowicz}
}

@article{RenWol92,
  author  = {Rendl, Franz and Wolkowicz, Henry},
  title   = {Applications of Parametric Programming and Eigenvalue Maximization to the Quadratic Assignment Problem},
  year    = {1992},
  volume  = {53},
  number  = {1--3},
  journal = {Mathematical Programming: Series A and B},
  pages   = {63--78}
}

@inproceedings{JinShiPerZha20,
  title     = {Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks },
  author    = {Hongwei Jin and Zhan Shi and Ashish Peruri and Xinhua Zhang},
  booktitle = nips,
  year      = {2020}
}

@inproceedings{VayChaFlaTavetal19,
  title     = {Optimal Transport for structured data with application on graphs},
  author    = {Titouan Vayer and Laetitia Chapel and Remi Flamary and Romain Tavenard and Nicolas Courty},
  booktitle = icml,
  year      = {2019}
}

@inproceedings{WonSchKol19,
  title     = {Wasserstein Adversarial Examples via Projected Sinkhorn Iterations},
  author    = {Eric Wong and Frank Schmidt and Zico Kolter},
  booktitle = icml,
  year      = {2019}
}

@inproceedings{XuLuoZhaCar19,
  title     = {{Gromov-Wasserstein} Learning for Graph Matching and Node Embedding},
  author    = {Hongteng Xu and Dixin Luo and Hongyuan Zha and Lawrence Carin},
  booktitle = icml,
  year      = {2019}
}

@article{Memoli11,
  title   = {{Gromov-Wasserstein} Distances and the Metric Approach to Object Matching},
  journal = {Foundations of Computational Mathematics},
  volume  = {11},
  pages   = {417--487},
  year    = {2011},
  author  = {Facundo Memoli}
}

@inproceedings{XuLuoCar19,
  title     = {Scalable {Gromov-Wasserstein} Learning for Graph Partitioning and Matching},
  author    = {Hongteng Xu and Dixin Luo and Lawrence Carin},
  booktitle = nips,
  year      = {2019}
}

@inproceedings{AlvJaa18,
  title     = {{Gromov-Wasserstein} Alignment of Word Embedding Spaces},
  author    = {David Alvarez-Melis and Tommi Jaakkola},
  booktitle = emnlp,
  year      = {2018}
}

@inproceedings{VayFlaTavChaetal19,
  title     = {Sliced {Gromov-Wasserstein}},
  author    = {Titouan Vayer and Remi Flamary and Romain Tavenard and Laetitia Chapel and Nicolas Courty},
  booktitle = nips,
  year      = {2019}
}

@article{MemNee21,
  title   = {Distance distributions and inverse problems for metric measure spaces (former title: {Gromov-Monge} quasi-metrics and distance distributions)},
  author  = {Facundo Memoli and Tom Needham},
  journal = {arXiv:1810.09646v3},
  year    = {2021}
}

@inproceedings{PeyCutSol16,
  title     = {{Gromov-Wasserstein} Averaging of Kernel and Distance Matrices},
  author    = {Peyre, Gabriel and Cuturi, Marco and Solomon, Justin},
  booktitle = icml,
  year      = {2016}
}

@article{WenYin13,
  title   = {A feasible method for optimization with orthogonality constraints},
  author  = {Zaiwen Wen and Wotao Yin},
  journal = {Mathematical Programming},
  volume  = {142},
  pages   = {397--434},
  year    = {2013}
}

@article{AraMoh18,
  title   = {Optimization problems with orthogonal matrix constraints},
  journal = {Numerical Algebra, Control \& Optimization},
  volume  = {8},
  number  = {4},
  pages   = {413--440},
  year    = {2018},
  author  = {K. T. Arasu and Manil T. Mohan}
}

@article{BroBruLeCSzletal17,
  author  = {Bronstein, Michael M. and Bruna, Joan and LeCun, Yann and Szlam, Arthur and Vandergheynst, Pierre},
  journal = {IEEE Signal Processing Magazine},
  title   = {Geometric Deep Learning: Going beyond Euclidean data},
  year    = {2017},
  volume  = {34},
  number  = {4},
  pages   = {18--42}
}

@inproceedings{DefBreVan16,
  author    = {Defferrard, Micha\"{e}l and Bresson, Xavier and Vandergheynst, Pierre},
  booktitle = nips,
  title     = {Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering},
  year      = {2016}
}

@inproceedings{YanLiWuMinetal18,
  title     = {Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation},
  author    = {Yuguang Yan and Wen Li and Hanrui Wu and Huaqing Min and Mingkui Tan and Qingyao Wu},
  booktitle = ijcai,
  year      = {2018}
}

@article{EzuSolKimBen17,
  author  = {Ezuz, Danielle and Solomon, Justin and Kim, Vladimir G. and Ben-Chen, Mirela},
  title   = {GWCNN: A Metric Alignment Layer for Deep Shape Analysis},
  year    = {2017},
  volume  = {36},
  number  = {5},
  journal = {Comput. Graph. Forum},
  pages   = {49--57}
}

@book{AbsMahSep09,
  author    = {P.-A. Absil and R. Mahony and Rodolphe Sepulchre},
  title     = {Optimization Algorithms on Matrix Manifolds},
  year      = {2009},
  publisher = {Princeton University Press}
}

@inproceedings{LeeYuaChaJaa19,
  title     = {Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers},
  author    = {Guang-He Lee and Yang Yuan and Shiyu Chang and Tommi Jaakkola},
  booktitle = nips,
  year      = {2019}
}

@article{ChoMem19,
  author  = {Chowdhury, Samir and Mémoli, Facundo},
  title   = {The {Gromov–Wasserstein} distance between networks and stable network invariants},
  journal = {Information and Inference: A Journal of the IMA},
  volume  = {8},
  number  = {4},
  pages   = {757-787},
  year    = {2019}
}

@article{AnsBri01,
  author  = {Kurt M. Anstreicher and Nathan W. Brixius},
  title   = {A new bound for the quadratic assignment problem based on convex quadratic programming},
  journal = {Mathematical Programming},
  volume  = {89},
  pages   = {341--357},
  year    = {2001}
}

@inproceedings{CohSej19,
  title     = {On the Gromov-Wasserstein Distance and Coupled Deep Generative Models},
  author    = {Samuel Cohen and Dino Sejdinovic},
  booktitle = {NeurIPS 2019 Workshop on Optimal Transport \& Machine Learning},
  year      = {2019}
}

@article{TUDataset,
  author = {TUDataset},
  title  = {TUDataset},
  note   = {\url{https://chrsmrrs.github.io/datasets/docs/home/}}
}


% new in AISTATS22
@article{chang2010training,
  title   = {Training and testing low-degree polynomial data mappings via linear SVM.},
  author  = {Chang, Yin-Wen and Hsieh, Cho-Jui and Chang, Kai-Wei and Ringgaard, Michael and Lin, Chih-Jen},
  journal = {Journal of Machine Learning Research},
  volume  = {11},
  number  = {4},
  year    = {2010}
}

@inproceedings{fan2019graph,
  title     = {Graph neural networks for social recommendation},
  author    = {Fan, Wenqi and Ma, Yao and Li, Qing and He, Yuan and Zhao, Eric and Tang, Jiliang and Yin, Dawei},
  booktitle = {The World Wide Web Conference},
  pages     = {417--426},
  year      = {2019}
}

@article{wu2020graph,
  title   = {Graph neural networks in recommender systems: a survey},
  author  = {Wu, Shiwen and Sun, Fei and Zhang, Wentao and Cui, Bin},
  journal = {arXiv preprint arXiv:2011.02260},
  year    = {2020}
}

@inproceedings{li2020flowscope,
  title     = {Flowscope: Spotting money laundering based on graphs},
  author    = {Li, Xiangfeng and Liu, Shenghua and Li, Zifeng and Han, Xiaotian and Shi, Chuan and Hooi, Bryan and Huang, He and Cheng, Xueqi},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume    = {34},
  number    = {04},
  pages     = {4731--4738},
  year      = {2020}
}

@article{coley2019graph,
  title     = {A graph-convolutional neural network model for the prediction of chemical reactivity},
  author    = {Coley, Connor W and Jin, Wengong and Rogers, Luke and Jamison, Timothy F and Jaakkola, Tommi S and Green, William H and Barzilay, Regina and Jensen, Klavs F},
  journal   = {Chemical science},
  volume    = {10},
  number    = {2},
  pages     = {370--377},
  year      = {2019},
  publisher = {Royal Society of Chemistry}
}

@inproceedings{abusnaina2019adversarial,
  title        = {Adversarial learning attacks on graph-based IoT malware detection systems},
  author       = {Abusnaina, Ahmed and Khormali, Aminollah and Alasmary, Hisham and Park, Jeman and Anwar, Afsah and Mohaisen, Aziz},
  booktitle    = {ICDCS},
  pages        = {1296--1305},
  year         = {2019},
  organization = {IEEE}
}

@article{gold1996graduated,
  title     = {A graduated assignment algorithm for graph matching},
  author    = {Gold, Steven and Rangarajan, Anand},
  journal   = {IEEE Transactions on pattern analysis and machine intelligence},
  volume    = {18},
  number    = {4},
  pages     = {377--388},
  year      = {1996},
  publisher = {IEEE}
}

@article{flamary2021pot,
  author  = {R{\'e}mi Flamary and Nicolas Courty and Alexandre Gramfort and Mokhtar Z. Alaya and Aur{\'e}lie Boisbunon and Stanislas Chambon and Laetitia Chapel and Adrien Corenflos and Kilian Fatras and Nemo Fournier and L{\'e}o Gautheron and Nathalie T.H. Gayraud and Hicham Janati and Alain Rakotomamonjy and Ievgen Redko and Antoine Rolet and Antony Schutz and Vivien Seguy and Danica J. Sutherland and Romain Tavenard and Alexander Tong and Titouan Vayer},
  title   = {POT: Python Optimal Transport},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  number  = {78},
  pages   = {1-8},
  url     = {http://jmlr.org/papers/v22/20-451.html}
}

@article{Birkhoff46,
  title   = {Three observations on linear algebra},
  author  = {Birkhoff, Garrett},
  journal = {Univ. Nac. Tacuman, Rev. Ser. A},
  volume  = {5},
  pages   = {147--151},
  year    = {1946}
}

@inproceedings{alman2021refined,
  title        = {A refined laser method and faster matrix multiplication},
  author       = {Alman, Josh and Williams, Virginia Vassilevska},
  booktitle    = {Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA)},
  pages        = {522--539},
  year         = {2021},
  organization = {SIAM}
}

@article{siglidis2020grakel,
  title   = {GraKeL: A Graph Kernel Library in Python},
  author  = {Siglidis, Giannis and Nikolentzos, Giannis and Limnios, Stratis and Giatsidis, Christos and Skianis, Konstantinos and Vazirgiannis, Michalis},
  journal = {J. Mach. Learn. Res.},
  volume  = {21},
  number  = {54},
  pages   = {1--5},
  year    = {2020}
}

@article{Umeyama88,
  title   = {An eigendecomposition approach to weighted graph matching problems},
  author  = {Shinji Umeyama},
  journal = pami,
  volume  = {10},
  number  = {5},
  pages   = {695--703},
  year    = {1988}
}

@article{LozMed13,
title = {On an exact method for the constrained shortest path problem},
journal = {Computers \& Operations Research},
volume = {40},
number = {1},
pages = {378-384},
year = {2013},
author = {Leonardo Lozano and Andrés L. Medaglia},
}

@article{SunOvsGui09,
author = {Sun, Jian and Ovsjanikov, Maks and Guibas, Leonidas},
title = {A Concise and Provably Informative Multi-Scale Signature Based on Heat Diffusion},
journal = {Computer Graphics Forum},
volume = {28},
number = {5},
pages = {1383--1392},
year = {2009}
}

@INPROCEEDINGS{AubSchCre11,  
author={Aubry, Mathieu and Schlickewei, Ulrich and Cremers, Daniel},  
booktitle={2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)},   
title={The wave kernel signature: A quantum mechanical approach to shape analysis},   
year={2011},  
pages={1626-1633},
}


@InProceedings{KorNorLeeHin19,
  title =    {Similarity of Neural Network Representations Revisited},
  author =       {Kornblith, Simon and Norouzi, Mohammad and Lee, Honglak and Hinton, Geoffrey},
  booktitle =    icml,
  pages =    {3519--3529},
  year =   {2019},
}
