@inproceedings{ref:rubens2007influence,
  title={Influence-based collaborative active learning},
  author={Rubens, Neil and Sugiyama, Masashi},
  booktitle={Proceedings of the 2007 ACM Conference on Recommender Systems},
  pages={145--148},
  year={2007}
}

@article{ref:houlsby2012collaborative,
  title={Collaborative gaussian processes for preference learning},
  author={Houlsby, Neil and Huszar, Ferenc and Ghahramani, Zoubin and Hern{\'a}ndez-lobato, Jose},
  journal={Advances in Neural Information Processing Systems},
  volume={25},
  year={2012}
}

@inproceedings{ref:harpale2008personalized,
  title={Personalized active learning for collaborative filtering},
  author={Harpale, Abhay S and Yang, Yiming},
  booktitle={Proceedings of the 31st annual international ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={91--98},
  year={2008}
}

@inproceedings{ref:houlsby2014cold,
  title={Cold-start active learning with robust ordinal matrix factorization},
  author={Houlsby, Neil and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel and Ghahramani, Zoubin},
  booktitle={International Conference on Machine Learning},
  pages={766--774},
  year={2014},
  organization={PMLR}
}

@inproceedings{ref:adam2021possibilistic,
  title={Possibilistic preference elicitation by minimax regret},
  author={Adam, Lo{\"\i}c and Destercke, S{\'e}bastien},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={718--727},
  year={2021},
  organization={PMLR}
}

@article{ref:zhu2019addressing,
  title={Addressing the item cold-start problem by attribute-driven active learning},
  author={Zhu, Yu and Lin, Jinghao and He, Shibi and Wang, Beidou and Guan, Ziyu and Liu, Haifeng and Cai, Deng},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={32},
  number={4},
  pages={631--644},
  year={2019},
  publisher={IEEE}
}

@inproceedings{ref:iyengar2001evaluating,
  title={Evaluating multiple attribute items using queries},
  author={Iyengar, Vijay S and Lee, Jon and Campbell, Murray},
  booktitle={Proceedings of the 3rd ACM Conference on Electronic Commerce},
  pages={144--153},
  year={2001}
}

@article{ref:toubia2004polyhedral,
  title={Polyhedral methods for adaptive choice-based conjoint analysis},
  author={Toubia, Olivier and Hauser, John R and Simester, Duncan I},
  journal={Journal of Marketing Research},
  volume={41},
  number={1},
  pages={116--131},
  year={2004},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}

@inproceedings{ref:canal2019active,
  title={Active embedding search via noisy paired comparisons},
  author={Canal, Gregory and Massimino, Andy and Davenport, Mark and Rozell, Christopher},
  booktitle={International Conference on Machine Learning},
  pages={902--911},
  year={2019},
  organization={PMLR}
}

@inproceedings{ref:boutilier2002active,
  title={Active Collaborative Filtering},
  author={Craig Boutilier and Richard S. Zemel and Benjamin M Marlin},
  booktitle={Conference on Uncertainty in Artificial Intelligence},
  year={2002},
}

@inproceedings{ref:rashid2002getting,
  title={Getting to know you: learning new user preferences in recommender systems},
  author={Rashid, Al Mamunur and Albert, Istvan and Cosley, Dan and Lam, Shyong K and McNee, Sean M and Konstan, Joseph A and Riedl, John},
  booktitle={Proceedings of the 7th international conference on Intelligent user interfaces},
  pages={127--134},
  year={2002}
}

@inproceedings{ref:jin2012bayesian,
  author       = {Rong Jin and
                  Luo Si},
  editor       = {David Maxwell Chickering and
                  Joseph Y. Halpern},
  title        = {A Bayesian Approach toward Active Learning for Collaborative Filtering},
  booktitle    = {{UAI} '04, Proceedings of the 20th Conference in Uncertainty in Artificial
                  Intelligence, Banff, Canada, July 7-11, 2004},
  pages        = {278--285},
  publisher    = {{AUAI} Press},
  year         = {2004},
}

@inproceedings{ref:cho2011friendship,
  title={Friendship and mobility: {U}ser movement in location-based social networks},
  author={Cho, Eunjoon and Myers, Seth A and Leskovec, Jure},
  booktitle={Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={1082--1090},
  year={2011}
}

@inproceedings{ref:ni2019justifying,
  title={Justifying recommendations using distantly-labeled reviews and fine-grained aspects},
  author={Ni, Jianmo and Li, Jiacheng and McAuley, Julian},
  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  pages={188--197},
  year={2019}
}

@inproceedings{ref:he2016vbpr,
  title={VBPR: visual bayesian personalized ranking from implicit feedback},
  author={He, Ruining and McAuley, Julian},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={30},
  number={1},
  year={2016}
}

@inproceedings{ref:truong2021bilateral,
  title={Bilateral variational autoencoder for collaborative filtering},
  author={Truong, Quoc-Tuan and Salah, Aghiles and Lauw, Hady W},
  booktitle={Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
  pages={292--300},
  year={2021}
}

@inproceedings{ref:liang2016modeling,
  title={Modeling user exposure in recommendation},
  author={Liang, Dawen and Charlin, Laurent and McInerney, James and Blei, David M},
  booktitle={Proceedings of the 25th International Conference on World Wide Web},
  pages={951--961},
  year={2016}
}

@inproceedings{ref:sepliarskaia2018preference,
  title={Preference elicitation as an optimization problem},
  author={Sepliarskaia, Anna and Kiseleva, Julia and Radlinski, Filip and de Rijke, Maarten},
  booktitle={Proceedings of the 12th ACM Conference on Recommender Systems},
  pages={172--180},
  year={2018}
}

@inproceedings{ref:zhao2013interactive,
  title={Interactive collaborative filtering},
  author={Zhao, Xiaoxue and Zhang, Weinan and Wang, Jun},
  booktitle={Proceedings of the 22nd ACM International Conference on Information \& Knowledge Management},
  pages={1411--1420},
  year={2013}
}

@inproceedings{ref:golbandi2011adaptive,
author = {Golbandi, Nadav and Koren, Yehuda and Lempel, Ronny},
title = {Adaptive Bootstrapping of Recommender Systems Using Decision Trees},
year = {2011},
isbn = {9781450304931},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1935826.1935910},
doi = {10.1145/1935826.1935910},
booktitle = {Proceedings of the Fourth ACM International Conference on Web Search and Data Mining},
pages = {595–604},
numpages = {10},
keywords = {user cold start, collaborative filtering, decision tree, new user, recommender systems},
location = {Hong Kong, China},
series = {WSDM '11}
}

@inproceedings{ref:zhou2011functional,
author = {Zhou, Ke and Yang, Shuang-Hong and Zha, Hongyuan},
title = {Functional Matrix Factorizations for Cold-Start Recommendation},
year = {2011},
isbn = {9781450307574},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2009916.2009961},
doi = {10.1145/2009916.2009961},
pages = {315–324},
numpages = {10},
location = {Beijing, China},
series = {SIGIR '11}
}

@inproceedings{ref:sedhain2017low-rank,
author = {Sedhain, Suvash and Menon, Aditya Krishna and Sanner, Scott and Xie, Lexing and Braziunas, Darius},
title = {Low-Rank Linear Cold-Start Recommendation from Social Data},
year = {2017},
publisher = {AAAI Press},
booktitle = {Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence},
pages = {1502–1508},
numpages = {7},
location = {San Francisco, California, USA},
series = {AAAI'17}
}

@inproceedings{ref:guo2010gaussian,
 author = {Guo, Shengbo and Sanner, Scott and Bonilla, Edwin V},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {J. Lafferty and C. Williams and J. Shawe-Taylor and R. Zemel and A. Culotta},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Gaussian Process Preference Elicitation},
 url = {https://proceedings.neurips.cc/paper_files/paper/2010/file/46922a0880a8f11f8f69cbb52b1396be-Paper.pdf},
 volume = {23},
 year = {2010}
}


@inproceedings{ref:liu2011wisdom,
author = {Liu, Nathan N. and Meng, Xiangrui and Liu, Chao and Yang, Qiang},
title = {Wisdom of the Better Few: Cold Start Recommendation via Representative Based Rating Elicitation},
year = {2011},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the Fifth ACM Conference on Recommender Systems},
pages = {37–44},
numpages = {8},
keywords = {collaborative filtering, recommender systems, cold start},
location = {Chicago, Illinois, USA},
series = {RecSys '11}
}


@inproceedings{ref:dilina2022fast,
author = {Rajapakse, Dilina Chandika and Leith, Douglas},
title = {Fast and Accurate User Cold-Start Learning Using Monte Carlo Tree Search},
year = {2022},
isbn = {9781450392785},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3523227.3546786},
doi = {10.1145/3523227.3546786},
booktitle = {Proceedings of the 16th ACM Conference on Recommender Systems},
pages = {350–359},
numpages = {10},
keywords = {User cold start, Monte Carlo Tree Search},
location = {Seattle, WA, USA},
series = {RecSys '22}
}

@inproceedings{
ref:kong2022provably,
title={Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning},
author={Dingwen Kong and Lin Yang},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=B2rqx0w63U}
}

@article{ref:rippl2016limit,
	title={Limit laws of the empirical {W}asserstein distance: {G}aussian distributions},
	author={Rippl, T. and Munk, A. and Sturm, A.},
	journal={Journal of Multivariate Analysis},
	volume={151},
	pages={90--109},
	year={2016},
}


@book{ref:murphy2012machine,
  title={Machine Learning: A Probabilistic Perspective},
  author={K.P.~Murphy},
  year={2012},
  publisher={MIT Press}
}
@book{ref:oppenheim2015signals,
  title={Signals, Systems and Inference},
  author={A.V.~Oppenheim and G.C.~Verghese},
  year={2015},
  publisher={Pearson}
}
@book{ref:kay1993fundamentals,
  title={Fundamentals of Statistical Signal Processing: Estimation Theory},
  author={S.M.~Kay},
   year={1993},
  publisher={Prentice Hall}
}
@book{ref:cover2012elements,
  title={Elements of Information Theory},
  author={T.M.~Cover and J.A.~Thomas},
  year={2012},
  publisher={John Wiley \& Sons}
}
@book{ref:mackay2003information,
  title={Information Theory, Inference and Learning Algorithms},
  author={D.J.C.~MacKay},
  year={2003},
  publisher={Cambridge University Press}
}
@book{ref:hamilton1994time,
  title={Time Series Analysis},
  author={J.D.~Hamilton},
  year={1994},
  publisher={Princeton University Press}
}
@book{ref:wooldridge2010econometric,
  title={Econometric Analysis of Cross Section and Panel Data},
  author={J.M.~Wooldridge},
  year={2010},
  publisher={MIT Press}
}
@book{ref:chatfield2016analysis,
  title={The Analysis of Time Series: {A}n Introduction},
  author={C.~Chatfield},
  year={2016},
  publisher={CRC Press}
}
@book{ref:stock2015introduction,
  title={Introduction to Econometrics},
  author={J.H. Stock and M.W.~Watson},
  publisher={Prentice Hall},
  year={2015}
}
@book{ref:ogata2009modern,
  title={Modern Control Engineering},
  author={K.~Ogata},
  year={2009},
  publisher={Pearson}
}
@book{ref:golnaraghi2017automatic,
  title={Automatic Control Systems},
  author={F.~Golnaraghi and B.C.~Kuo},
  year={2017},
  publisher={McGraw-Hill Education}
}

@article{ref:chen2018drocc,
	title={Data-Driven Chance Constrained Programs over {W}asserstein Balls},
	author = {Chen, Z. and Kuhn, D. and Wiesemann, W.},
	journal={arXiv preprint arXiv:1809.00210},
	year={2018}
}

@article{ref:chen2018adaptive,
  title={Adaptive Robust Optimization with Scenario-wise Ambiguity Sets},
  author={Z.~Chen and M.~Sim and P.~Xiong},
  journal={Available from Optimization Online},
  year={2018}
}
@article{ref:bertsimas2018data,
  title={Data-driven Robust Optimization},
  author={D.~Bertsimas and V.~Gupta and N.~Kallus},
  journal={Mathematical Programming},
  volume={167},
  number={2},
  pages={235--292},
  year={2018},
  publisher={Springer}
}
@article{ref:bertsimas2018robust,
  title={Robust Sample Average Approximation},
  author={D.~Bertsimas and V.~Gupta and N.~Kallus},
  journal={Mathematical Programming},
  volume={171},
  number={1-2},
  pages={217--282},
  year={2018},
  publisher={Springer}
}

@article{ref:xie2018drocc,
	title={On Distributionally Robust Chance Constrained Program with {W}asserstein Distance},
	author = {Xie, W.},
	journal={arXiv preprint arXiv:1806.07418},
	year={2018}
}

@book{ref:villani2008optimal,
	title={Optimal Transport: Old and New},
	author={C.~Villani},
	year={2008},
	publisher={Springer Science \& Business Media}
}

@book{ref:rockafellar1997convex,
  title={Convex Analysis},
  author={R.T.~Rockafellar},
  year={1997},
  publisher={Princeton University Press}
}

@article{ref:blanchet2019inverse,
	title={Optimal Uncertainty Size in Distributionally Robust Inverse Covariance Estimation},
	author = {Blanchet, J. and Si, N.},
	journal={arXiv preprint arXiv:1901.07693},
	year={2019}
}

@article{ref:sturt2018data-driven,
	title={A Data-Driven Approach for Multi-Stage Linear Optimization},
	author={D. Bertsimas and S. Shtern and B. Sturt},
	journal={Available from Optimization Online},
	year={2018}
}

@article{ref:zhen2019distributionally,
  title={Distributionally robust nonlinear optimization},
  author={J.~Zhen and D.~Kuhn and W.~Wiesemann},
  journal={Working paper},
  year={2019}
}

@article{ref:zymler2013wcvar,
author = {Zymler, S. and Kuhn, D. and Rustem, B.},
title = {Worst-Case Value at Risk of Nonlinear Portfolios},
journal = {Management Science},
volume = {59},
number = {1},
pages = {172--188},
year = {2013}
}

@article{ref:el-ghaoui2003wcvar,
author = {El Ghaoui, L. and Oks, M. and Oustry, F.},
title = {Worst-Case Value-At-Risk and Robust Portfolio Optimization: A Conic Programming Approach},
journal = {Operations Research},
volume = {51},
number = {4},
pages = {543--556},
year = {2003}
}

@article{ref:natarajan2010utility,
  title={Tractable robust expected utility and risk models for portfolio optimisation},
  author={K. Natarajan and M. Sim and J. Uichanco},
  journal={Mathematical Finance},
  volume={20},
  number={4},
  pages={695--731},
 year={2010}
}

@article{ref:ben2015deriving,
  title={Deriving robust counterparts of nonlinear uncertain inequalities},
  author={A.~Ben-Tal and D.~Den Hertog and J.~Vial},
  journal={Mathematical Programming},
  volume={149},
  number={1-2},
  pages={265--299},
  year={2015},
  publisher={Springer}
}

@article{ref:kantorovich1958space,
	title={On a space of totally additive functions},
	author={L.V.~Kantorovich and G.~Rubinshtein},
	journal={Vestnik Leningrad University},
	volume={13},
	number={7},
	pages={52--59},
	year={1958}
}

@article{ref:esfahani2018data,
	title={Data-driven distributionally robust optimization using the {W}asserstein metric: Performance guarantees and tractable reformulations},
	author={P.~{Mohajerin Esfahani} and D.~Kuhn},
	journal={Mathematical Programming},
	volume={171},
	number={1-2},
	pages={115--166},
	year={2018},
	publisher={Springer}
}

@article{ref:blanchet2016quantifying,
	title={Quantifying distributional model risk via optimal transport},
	author={J.~Blanchet and K.~Murthy},
	journal={To appear in Mathematics of Operations Research},
	year={2017}
}

@article{ref:gao2016distributionally,
	title={Distributionally robust stochastic optimization with {W}asserstein distance},
	author={R.~Gao and A.~J.~Kleywegt},
	journal={arXiv preprint arXiv:1604.02199},
	year={2016}
}

@article{ref:wozabal2012framework,
	title={A framework for optimization under ambiguity},
	author={D.~Wozabal},
	journal={Annals of Operations Research},
	volume={193},
	number={1},
	pages={21--47},
	year={2012},
	publisher={Springer}
}

@article{ref:owhadi2017extreme,
	title={Extreme points of a ball about a measure with finite support},
	author={H.~Owhadi and C.~Scovel},
	journal={Communications in Mathematical Sciences},
	volume={15},
	number={1},
	pages={77--96},
	year={2017},
	publisher={International Press of Boston}
}

@article{ref:pflug2007ambiguity,
	title={Ambiguity in portfolio selection},
	author={G.~Pflug and D.~Wozabal},
	journal={Quantitative Finance},
	volume={7},
	number={4},
	pages={435--442},
	year={2007},
	publisher={Taylor \& Francis}
}

@article{ref:nguyen2019bridging,
	title={Bridging {B}ayesian and Minimax Mean Square Error Estimation via {W}asserstein Distributionally Robust Optimization},
	author={{Viet Anh} Nguyen and S. Shafieezadeh-Abadeh and D. Kuhn and P. {Mohajerin Esfahani}},
	journal={arXiv preprint arxiv:1910.10583},
	year={2019}
}

@inproceedings{ref:monge1781memoire,
  author = {G.~Monge},
  booktitle= {Histoire de l'Acad{\'e}mie Royale des Sciences de Paris},
  pages = {666-704},
  title = {M{\'e}moire sur la th{\'e}orie des d{\'e}blais et des remblais},
  year = {1781}
}

@article{ref:kantorovich1942translocation,
	author = {L.V.~Kantorovich},
	year = {1942},
	pages = {199-201},
	title = {On the translocation of masses},
	journal = {Doklady Akademii Nauk USSR},
	volume = {37}
}

@article{ref:zhao2018data,
	title = {Data-driven risk-averse stochastic optimization with {W}asserstein metric},
	journal = "Operations Research Letters",
	volume = "46",
	number = "2",
	pages = "262 - 267",
	year = "2018",
	author = "C.~Zhao and Y.~Guan",
}

@article{ref:taskesen2019complexity,
	title =  {On the complexity of computing {W}asserstein distances},
	author = {B.~Ta\c skesen and S.~Shafieezadeh-Abadeh and D.~Kuhn},
	journal = {Working paper},
	year = {2019}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% DRO with Wasserstein: applications
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@inproceedings{ref:shafieezadeh2015distributionally,
	title={Distributionally robust logistic regression},
	author={S.~Shafieezadeh-Abadeh and P.~{Mohajerin Esfahani} and D.~Kuhn},
	booktitle={Advances in Neural Information Processing Systems 28},
	pages={1576--1584},
	year={2015}
}

@article{ref:shafieezadeh2017regularization,
	title={Regularization via mass transportation},
	author={S.~Shafieezadeh-Abadeh and D.~Kuhn and P.~{Mohajerin Esfahani}},
	journal={arXiv preprint arXiv:1710.10016},
	year={2017}
}

@article{ref:nguyen2018distributionally,
	title={Distributionally Robust Inverse Covariance Estimation: The {W}asserstein Shrinkage Estimator},
	author={V.A. Nguyen and D. Kuhn and P. {Mohajerin Esfahani}},
	journal={arXiv preprint arXiv:1805.07194},
	year={2018}
}

@inproceedings{ref:shafieezadeh2018wasserstein,
	title={{W}asserstein Distributionally Robust {K}alman Filtering},
	author={S.~Shafieezadeh-Abadeh and V.A.~Nguyen and D.~Kuhn and P.~{Mohajerin Esfahani}},
	booktitle={Advances in Neural Information Processing Systems 31},
	pages={8483-8492},
	year={2018}
}

@article{ref:esfahani2018inverse,
	title={Data-driven inverse optimization with imperfect information},
	author={P.~{Mohajerin Esfahani} and S.~Shafieezadeh-Abadeh and G.A.~Hanasusanto and D.~Kuhn},
	journal={Mathematical Programming},
	volume={167},
	number={1},
	pages={191--234},
	year={2018},
	publisher={Springer}
}

@article{ref:blanchet2016robust,
	title={Robust {W}asserstein profile inference and applications to machine learning},
	author={J.~Blanchet and Y.~Kang and K.~Murthy},
	journal={arXiv preprint arXiv:1610.05627},
	year={2016}
}

@article{ref:blanchet2016sample,
	title={Sample out-of-sample inference based on {W}asserstein distance},
	author={J.~Blanchet and Y.~Kang},
	journal={arXiv preprint arXiv:1605.01340},
	year={2016}
}

@article{ref:blanchet2017data,
	title={Data-driven Optimal Transport Cost Selection for Distributionally Robust Optimization},
	author={J.~Blanchet and Y.~Kang and F.~Zhang and K.~Murthy},
	journal={arXiv preprint arXiv:1705.07152},
	year={2017}
}

@article{ref:gao2017wasserstein,
	title={{W}asserstein distributional robustness and regularization in statistical learning},
	author={R.~Gao and X.~Chen and A.~J.~Kleywegt},
	journal={arXiv preprint arXiv:1712.06050},
	year={2017}
}

@article{ref:chen2018data,
	title={Data-Driven Chance Constrained Programs over {W}asserstein Balls},
	author={Z.~Chen and D.~Kuhn and W.~Wiesemann},
	journal={arXiv preprint arXiv:1809.00210},
	year={2018}
}

@article{ref:cheng2018distributionally,
	title={Distributionally Robust Optimization with Principal Component Analysis},
	author={J.~Cheng and R.~Li-Yang Chen and H.~N.~Najm and A.~Pinar and C.~Safta and J.~Watson},
	journal={SIAM Journal on Optimization},
	volume={28},
	number={2},
	pages={1817--1841},
	year={2018},
	publisher={SIAM}
}

@article{ref:xie2018distributionally,
	title={On Distributionally Robust Chance Constrained Program with {W}asserstein Distance},
	author={W.~Xie},
	journal={arXiv preprint arXiv:1806.07418},
	year={2018}
}

@inproceedings{ref:gao2018robust,
	title={Robust Hypothesis Testing Using {W}asserstein Uncertainty Sets},
	author={R.~Gao and L.~Xie and Y.~Xie and H.~Xu},
	booktitle={Advances in Neural Information Processing Systems 31},
	pages = {7913--7923},
	year={2018}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% DRO with Wasserstein: performance guarantee
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@inproceedings{ref:lee2017minimax,
	title={Minimax statistical learning with {W}asserstein distances},
	author={J.~Lee and M.~Raginsky},
	booktitle={Advances in Neural Information Processing Systems 31},
	pages = {2692--2701},
	year={2018}
}

@article{ref:fournier2015rate,
	title={On the rate of convergence in {W}asserstein distance of the empirical measure},
	author={N.~Fournier and A.~Guillin},
	journal={Probability Theory and Related Fields},
	volume={162},
	number={3-4},
	pages={707--738},
	year={2015},
	publisher={Springer}
}

@article{ref:weed2017sharp,
	title={Sharp asymptotic and finite-sample rates of convergence of empirical measures in {W}asserstein distance},
	author={J.~Weed and F.~Bach},
	journal={To appear in Bernoulli},
	year={2019}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Wasserstein in ML
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@article{ref:cuturi2014ground,
	title={Ground metric learning},
	author={M.~Cuturi and D.~Avis},
	journal={The Journal of Machine Learning Research},
	volume={15},
	number={1},
	pages={533--564},
	year={2014}
}

@article{ref:rubner2000earth,
	title={The earth mover's distance as a metric for image retrieval},
	author={Y.~Rubner and C.~Tomasi and L.~J.~Guibas},
	journal={International Journal of Computer Vision},
	volume={40},
	number={2},
	pages={99--121},
	year={2000}
}

@article{ref:parikh2014proximal,
	title={Proximal Algorithms},
	author={N.~Parikh and S.~Boyd},
	journal={Foundations and Trends in Optimization},
	volume={1},
	number={3},
	pages={127--239},
	year={2014},
	publisher={Now Publishers}
}

@article{ref:courty2017optimal, 
	author={N.~Courty and R.~Flamary and D.~Tuia and A.~Rakotomamonjy}, 
	journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
	title={Optimal Transport for Domain Adaptation}, 
	year={2017}, 
	volume={39}, 
	number={9}, 
	pages={1853-1865}, 
}


@article{ref:rujeerapaiboon2018scenario,
	author="N.~Rujeerapaiboon and K.~Schindler and D.~Kuhn and W.~Wiesemann",
	title="Scenario reduction revisited: fundamental limits and guarantees",
	journal="To appear in Mathematical Programming",
	year={2018}
	}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Wasserstein computation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@article{ref:givens1984class,
	title={A class of {W}asserstein metrics for probability distributions},
	author={C.R.~Givens and R.M.~Shortt},
	journal={The Michigan Mathematical Journal},
	volume={31},
	number={2},
	pages={231--240},
	year={1984},
	publisher={The University of Michigan}
}

@article{ref:gelbrich1990formula,
	title={On a formula for the ${L}^2$ {W}asserstein metric between measures on {E}uclidean and {H}ilbert spaces},
	author={M.~Gelbrich},
	journal={Mathematische Nachrichten},
	volume={147},
	number={1},
	pages={185--203},
	year={1990},
	publisher={Wiley Online Library}
}

@article{ref:olkin1982distance,
	title={The distance between two random vectors with given dispersion matrices},
	author={I.~Olkin and F.~Pukelsheim},
	journal={Linear Algebra and its Applications},
	volume={48},
	pages={257--263},
	year={1982},
	publisher={Elsevier}
}

@article{ref:dowson1982frechet,
	title={The {F}r{\'e}chet distance between multivariate normal distributions},
	author={D.C.~Dowson and B.V.~Landau},
	journal={Journal of Multivariate Analysis},
	volume={12},
	number={3},
	pages={450--455},
	year={1982},
	publisher={Academic Press}
}

@article{ref:peyre2018computational,
	title={Computational Optimal Transport},
	author={G.~Peyr{\'e} and M.~Cuturi},
	journal={arXiv preprint arXiv:1803.00567},
	year={2018}
}

@book{ref:brualdi2006combinatorial,
	title={Combinatorial Matrix Classes},
	author={R.A.~Brualdi},
	year={2006},
	publisher={Cambridge University Press}
}

@book{ref:bertsekas1998network,
	title={Network Optimization: Continuous and Discrete Models},
	author={D.P.~Bertsekas},
	year={1998},
	publisher={Athena Scientific}
}

@book{ref:bertsimas1997introduction,
	title={Introduction to Linear Optimization},
	author={D.~Bertsimas and J.N.~Tsitsiklis},
	year={1997},
	publisher={Athena Scientific}
}

@article{ref:bertsekas1981new,
	title={A new algorithm for the assignment problem},
	author={D.P.~Bertsekas},
	journal={Mathematical Programming},
	volume={21},
	number={1},
	pages={152--171},
	year={1981},
	publisher={Springer}
}

@article{ref:bertsekas1992auction,
	title={Auction algorithms for network flow problems: A tutorial introduction},
	author={D.P.~Bertsekas},
	journal={Computational Optimization and Applications},
	volume={1},
	number={1},
	pages={7--66},
	year={1992},
	publisher={Springer}
}

@inproceedings{ref:cuturi2013sinkhorn,
	title={Sinkhorn distances: Lightspeed computation of optimal transport},
	author={M.~Cuturi},
	booktitle={Advances in Neural Information Processing Systems 26},
	pages={2292--2300},
	year={2013}
}

@article{ref:essid2018quadratically,
	title={Quadratically regularized optimal transport on graphs},
	author={M.~Essid and J.~Solomon},
	journal={SIAM Journal on Scientific Computing},
	volume={40},
	number={4},
	pages={A1961--A1986},
	year={2018},
	publisher={SIAM}
}

@inproceedings{ref:muzellec2017tsallis,
	title={Tsallis Regularized Optimal Transport and Ecological Inference},
	author={B.~Muzellec and R.~Nock and G.~Patrini and F.~Nielsen},
	booktitle={Association for the Advancement of Artificial Intelligence},
	pages={2387--2393},
	year={2017}
}

@article{ref:benamou2015iterative,
	title={Iterative {B}regman projections for regularized transportation problems},
	author={J.~Benamou and G.~Carlier and M.~Cuturi and L.~Nenna and G.~Peyr{\'e}},
	journal={SIAM Journal on Scientific Computing},
	volume={37},
	number={2},
	pages={A1111--A1138},
	year={2015},
	publisher={SIAM}
}

@article{ref:schmitzer2016sparse,
	title={A sparse multiscale algorithm for dense optimal transport},
	author={B.~Schmitzer},
	journal={Journal of Mathematical Imaging and Vision},
	volume={56},
	number={2},
	pages={238--259},
	year={2016},
	publisher={Springer}
}

@inproceedings{ref:fonarev2016efficient,
  title={Efficient rectangular maximal-volume algorithm for rating elicitation in collaborative filtering},
  author={Fonarev, Alexander and Mikhalev, Alexander and Serdyukov, Pavel and Gusev, Gleb and Oseledets, Ivan},
  booktitle={2016 IEEE 16th International Conference on Data Mining (ICDM)},
  pages={141--150},
  year={2016},
  organization={IEEE}
}

@inproceedings{ref:bae2023lancer,
  title={{LANCER}: {A} {L}ifetime-{A}ware {N}ews {R}ecommender {S}ystem},
  author={Bae, Hong-Kyun and Ahn, Jeewon and Lee, Dongwon and Kim, Sang-Wook},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  pages={4141--4148},
  year={2023}
}

@inproceedings{ref:zhang2022deep,
  title={Deep multi-representational item network for {CTR} prediction},
  author={Zhang, Jihai and Lin, Fangquan and Yang, Cheng and Wang, Wei},
  booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={2277--2281},
  year={2022}
}

@inproceedings{ref:parapar2021diverse,
  title={Diverse user preference elicitation with multi-armed bandits},
  author={Parapar, Javier and Radlinski, Filip},
  booktitle={Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
  pages={130--138},
  year={2021}
}

@article{ref:solomon2015convolutional,
	title={Convolutional {W}asserstein distances: Efficient optimal transportation on geometric domains},
	author={J.~Solomon and F.~De Goes and G.~Peyr{\'e} and M.~Cuturi and A.~Butscher and A.~Nguyen and T.~Du and L.~Guibas},
	journal={ACM Transactions on Graphics},
	volume={34},
	number={4},
	pages={66},
	year={2015},
	publisher={ACM}
}

@article{ref:peyre2017quantum,
	title={Quantum entropic regularization of matrix-valued optimal transport},
	author={G.~Peyr{\'e} and L.~Chizat and F.~Vialard and J.~Solomon},
	journal={European Journal of Applied Mathematics},
	pages={1--24},
	year={2017},
	publisher={Cambridge University Press}
}

@article{ref:peyre2015entropic,
	title={Entropic approximation of {W}asserstein gradient flows},
	author={G.~Peyr{\'e}},
	journal={SIAM Journal on Imaging Sciences},
	volume={8},
	number={4},
	pages={2323--2351},
	year={2015},
	publisher={SIAM}
}

@article{ref:karlsson2017generalized,
	title={Generalized {S}inkhorn iterations for regularizing inverse problems using optimal mass transport},
	author={J.~Karlsson and A.~Ringh},
	journal={SIAM Journal on Imaging Sciences},
	volume={10},
	number={4},
	pages={1935--1962},
	year={2017},
	publisher={SIAM}
}

@article{ref:chizat2018scaling,
	title={Scaling algorithms for unbalanced optimal transport problems},
	author={L.~Chizat and G.~Peyr{\'e} and B.~Schmitzer and F.~Vialard},
	journal={Mathematics of Computation},
	year={2018},
	volume = {87},
	pages = {2563-2609}
}

@inproceedings{ref:canas2012learning,
title = {Learning Probability Measures with respect to Optimal Transport Metrics},
author = {G.~Canas and L.~Rosasco},
booktitle = {Advances in Neural Information Processing Systems 25},
pages = {2492--2500},
year = {2012},
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%	iStability results
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{ref:dupacova1990stability,
	author="J.~Dupa{\v{c}}ov{\'a}",
	title="Stability and sensitivity-analysis for stochastic programming",
	journal="Annals of Operations Research",
	year="1990",
	volume="27",
	number="1",
	pages="115--142",
}

@article{ref:roemisch1991stability,
	author="W.~R{\"o}misch and R.~Schultz",
	title="Stability analysis for stochastic programs",
	journal="Annals of Operations Research",
	year="1991",
	volume="30",
	number="1",
	pages="241--266",
}

@book{ref:pflug2014multistage,
	title={Multistage Stochastic Optimization},
	author={G.~Pflug and A.~Pichler},
	year={2014},
	publisher={Springer}
}


@article{ref:roemisch2007stability,
author = {W.~R{\"o}misch and R.~Wets},
title = {Stability of $\varepsilon$-approximate Solutions to Convex Stochastic Programs},
journal = {SIAM Journal on Optimization},
volume = {18},
number = {3},
pages = {961-979},
year = {2007}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% DRO with moments
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@incollection{ref:scarf1958min,
	author={H.~Scarf},
	title={A min-max solution of an inventory problem},
	booktitle={Studies in the Mathematical Theory of Inventory and Production},
	publisher={Stanford University Press},
	year={1958},	
	pages={201--209},
	editor={Arrow, Kenneth Joseph and Karlin, Samuel and Scarf, Herbert}
}

@article{ref:delage2010distributionally,
	title={Distributionally robust optimization under moment uncertainty with application to data-driven problems},
	author={E.~Delage and Y.~Ye},
	journal={Operations Research},
	volume={58},
	number={3},
	pages={595--612},
	year={2010},
	publisher={INFORMS}
}

@article{ref:delage2010percentile,
  title={Percentile Optimization for {M}arkov Decision Processes with Parameter Uncertainty},
  author={Erick Delage and Shie Mannor},
  journal={Operations Research},
  year={2010},
  volume={58},
  pages={203-213}
}

@article{ref:goh2010distributionally,
	title={Distributionally robust optimization and its tractable approximations},
	author={J.~Goh and M.~Sim},
	journal={Operations Research},
	volume={58},
	number={4},
	pages={902--917},
	year={2010},
	publisher={INFORMS}
}

@article{ref:hanasusanto2015distributionally,
	title={Distributionally robust multi-item newsvendor problems with multimodal demand distributions},
	author={G.A.~Hanasusanto and D.~Kuhn and S.~W.~Wallace and S.~Zymler},
	journal={Mathematical Programming},
	volume={152},
	number={1-2},
	pages={1--32},
	year={2015},
	publisher={Springer}
}

@article{ref:vayanos2012constraint,
	title={A constraint sampling approach for multi-stage robust optimization},
	author={P.~Vayanos and D.~Kuhn and B.~Rustem},
	journal={Automatica},
	volume={48},
	number={3},
	pages={459--471},
	year={2012},
	publisher={Elsevier}
}

@article{ref:wiesemann2014distributionally,
	title={Distributionally robust convex optimization},
	author={W.~Wiesemann and D.~Kuhn and M.~Sim},
	journal={Operations Research},
	volume={62},
	number={6},
	pages={1358--1376},
	year={2014},
	publisher={INFORMS}
}

@incollection{ref:shapiro2001on,
	author={A.~Shapiro},
	title={On Duality Theory of Conic Linear Problems},
	booktitle={Semi-Infinite Programming},
	publisher={Kluwer Academic Publishers},
	year={2001},	
	pages={135--165},
}

@article{ref:ghaoui2003worst,
	title={Worst-case value-at-risk and robust portfolio optimization: {A} conic programming approach},
	author={L.~El Ghaoui and M.~Oks and F.~Oustry},
	journal={Operations Research},
	volume={51},
	number={4},
	pages={543--556},
	year={2003},
	publisher={INFORMS}
}

@article{ref:calafiore2006distributionally,
	title={On distributionally robust chance-constrained linear programs},
	author={G.C.~Calafiore and L.~El Ghaoui},
	journal={Journal of Optimization Theory and Applications},
	volume={130},
	number={1},
	pages={1--22},
	year={2006},
	publisher={Springer}
}

@article{ref:calafiore2009parameter,
title = "Parameter estimation with expected and residual-at-risk criteria",
journal = "Systems \& Control Letters",
volume = "58",
number = "1",
pages = "39--46",
year = "2009",
author = "Giuseppe Calafiore and Ufuk Topcu and Laurent El Ghaoui",
}

@article{ref:chen2010cvar,
	title={From {CV}a{R} to uncertainty set: {I}mplications in joint chance-constrained optimization},
	author={W.~Chen and M.~Sim and J.~Sun and C.~Teo},
	journal={Operations Research},
	volume={58},
	number={2},
	pages={470--485},
	year={2010},
	publisher={INFORMS}
}

@article{ref:cheung2012linear,
	title={Linear matrix inequalities with stochastically dependent perturbations and applications to chance-constrained semidefinite optimization},
	author={S.~Cheung and A.~Man-Cho So and K.~Wang},
	journal={SIAM Journal on Optimization},
	volume={22},
	number={4},
	pages={1394--1430},
	year={2012},
	publisher={SIAM}
}

@article{ref:zymler2013distributionally,
	title={Distributionally robust joint chance constraints with second-order moment information},
	author={S.~Zymler and D.~Kuhn and B.~Rustem},
	journal={Mathematical Programming},
	volume={137},
	number={1-2},
	pages={167--198},
	year={2013},
	publisher={Springer}
}

@article{ref:bertsimas2010models,
	title={Models for minimax stochastic linear optimization problems with risk aversion},
	author={D.~Bertsimas and X.V.~Doan and K.~Natarajan and C.~Teo},
	journal={Mathematics of Operations Research},
	volume={35},
	number={3},
	pages={580--602},
	year={2010},
	publisher={INFORMS}
}

@article{ref:kuhn2011primal,
	title={Primal and dual linear decision rules in stochastic and robust optimization},
	author={D.~Kuhn and W.~Wiesemann and A.~Georghiou},
	journal={Mathematical Programming},
	volume={130},
	number={1},
	pages={177--209},
	year={2011},
	publisher={Springer}
}

@article{ref:hanasusanto2015perspective,
	title={A distributionally robust perspective on uncertainty quantification and chance constrained programming},
	author={G.A.~Hanasusanto and V.~Roitch and D.~Kuhn and W.~Wiesemann},
	journal={Mathematical Programming},
	volume={151},
	number={1},
	pages={35--62},
	year={2015},
	publisher={Springer}
}

@article{ref:rujeerapaiboon2015robust,
	title={Robust growth-optimal portfolios},
	author={N.~Rujeerapaiboon and D.~Kuhn and W.~Wiesemann},
	journal={Management Science},
	volume={62},
	number={7},
	pages={2090--2109},
	year={2015},
	publisher={Informs}
}

@article{ref:nguyen2019distributionally,
	title={Distributionally robust risk measures with structured {W}asserstein ambiguity sets},
	author = {V.A.~Nguyen and D.~Filipovic and D.~Kuhn},
	journal={Working paper},
	year={2019}
}


@article{ref:dantzig1957discrete,
 author = {George B. Dantzig},
 journal = {Operations Research},
 number = {2},
 pages = {266--277},
 title = {Discrete-Variable Extremum Problems},
 volume = {5},
 year = {1957}
}
@book{ref:korte2007,
 author = {Korte, Bernhard and Vygen, Jens},
 title = {Combinatorial Optimization: Theory and Algorithms},
 year = {2007},
 isbn = {3540718435, 9783540718437},
 publisher = {Springer},
} 
@book{ref:hiriart2012fundamentals,
  title={Fundamentals of convex analysis},
  author={Hiriart-Urruty, Jean-Baptiste and Lemar{\'e}chal, Claude},
  year={2012},
  publisher={Springer Science \& Business Media}
}
@Article{ref:isii1962,
author="Isii, Keiiti",
title="On sharpness of tchebycheff-type inequalities",
journal="Annals of the Institute of Statistical Mathematics",
year="1962",
month="Dec",
day="01",
volume="14",
number="1",
pages="185--197",
issn="1572-9052",
doi="10.1007/BF02868641",
url="https://doi.org/10.1007/BF02868641"
}

@book{ref:bertsekas1999nonlinear,
  author = {Bertsekas, D.P.},
  publisher = {Athena Scientific},
  title = {Nonlinear Programming},
  year = 1999
}

@inproceedings{ref:hu2018does,
author = {Weihua Hu and Gang Niu and Issei Sato and Masashi Sugiyama },
title = {Does Distributionally Robust Supervised Learning Give Robust Classifiers?},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
year = {2018}
}

@book{ref:berlinet2004reproducing,
author = {Berlinet, Alain and Thomas-Agnan, Christine},
year = {2004},
title = {Reproducing Kernel Hilbert Space in Probability and Statistics},
publisher = {Kluwer Academic Publishers}
}

@book{ref:bertsekas2009convex,
	AUTHOR = {Bertsekas, Dimitri},
	TITLE = {Convex Optimization Theory},
	PUBLISHER = {Athena Scientific},
	YEAR = {2009},
	PAGES = {x+246},
}

@book{ref:berman1994nonnegative,
author = {Berman, Abraham and Plemmons, Robert J.},
title = {Nonnegative Matrices in the Mathematical Sciences},
publisher = {Society for Industrial and Applied Mathematics},
year = {1994},
}

@phdthesis{ref:bertsekas1971control,
    author = {Bertsekas, Dimitri P.}, 
    year = 1971, 
    title = {Control of Uncertain Systems with a Set-Membership Description of Uncertainty} ,
    school   = {Massachusetts Institute of Technology},
}

@article{ref:chebbi2012means,
title = "Means of {H}ermitian positive-definite matrices based on the log-determinant $\alpha$-divergence function",
journal = "Linear Algebra and its Applications",
volume = "436",
number = "7",
pages = "1872 - 1889",
year = "2012",
author = "Zeineb Chebbi and Maher Moakher",
}

@inproceedings{ref:lahoti2020fairness,
author = {Preethi Lahoti and Alex Beutel and Jilin Chen and Kang Lee, Flavien Prost and Nithum Thain and Xuezhi Wang and Ed Chi},
title = {Fairness without Demographics through Adversarially Reweighted Learning},
booktitle={Advances in Neural Information Processing Systems},
year = {2020}
}

@inproceedings{ref:wang2020robust,
author = {Wang, Serena and Guo, Wenshuo and Narasimhan, Harikrishna and Cotter, Andrew and Gupta, Maya and Jordan, Michael I},
title = {Robust optimization for fairness with noisy protected groups},
booktitle={Advances in Neural Information Processing Systems},
year = {2020}
}

@article{duchi2019distributionally,
  title={Distributionally robust losses against mixture covariate shifts},
  author={Duchi, John C and Hashimoto, Tatsunori and Namkoong, Hongseok},
  journal={Under review},
  year={2019}
}

@article{zhang2020coping,
  title={Coping with Label Shift via Distributionally Robust Optimisation},
  author={Zhang, Jingzhao and Menon, Aditya and Veit, Andreas and Bhojanapalli, Srinadh and Kumar, Sanjiv and Sra, Suvrit},
  journal={arXiv preprint arXiv:2010.12230},
  year={2020}
}

@inproceedings{ref:namkoong2017variance,
title = {Variance-based Regularization with Convex Objectives},
author = {Namkoong, Hongseok and Duchi, John C},
booktitle = {Advances in Neural Information Processing Systems 30},
pages = {2971--2980},
year = {2017}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Nadaraya-Watson estimator
%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@book{tsybakov2008introduction,
  title={Introduction to Nonparametric Estimation},
  author={Tsybakov, Alexandre B},
  year={2008},
  publisher={Springer}
}

@inproceedings{noh2017generative,
  title={Generative local metric learning for kernel regression},
  author={Noh, Yung-Kyun and Sugiyama, Masashi and Kim, Kee-Eung and Park, Frank and Lee, Daniel D},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2452--2462},
  year={2017}
}

@article{liu2020learning,
  title={Learning Data-adaptive Non-parametric Kernels},
  author={Liu, Fanghui and Huang, Xiaolin and Gong, Chen and Yang, Jie and Li, Li},
  journal={Journal of Machine Learning Research},
  volume={21},
  number={208},
  pages={1--39},
  year={2020}
}

@article{hofmann2008kernel,
  title={Kernel methods in machine learning},
  author={Hofmann, Thomas and Sch{\"o}lkopf, Bernhard and Smola, Alexander J},
  journal={The Annals of Statistics},
  pages={1171--1220},
  year={2008}
}

@article{carmeli2010vector,
  title={Vector valued reproducing kernel {H}ilbert spaces and universality},
  author={Carmeli, Claudio and De Vito, Ernesto and Toigo, Alessandro and Umanit{\'a}, Veronica},
  journal={Analysis and Applications},
  volume={8},
  number={01},
  pages={19--61},
  year={2010},
  publisher={World Scientific}
}

@article{micchelli2006universal,
  title={Universal kernels},
  author={Micchelli, Charles A and Xu, Yuesheng and Zhang, Haizhang},
  journal={Journal of Machine Learning Research},
  volume={7},
  number={Dec},
  pages={2651--2667},
  year={2006}
}

@article{sriperumbudur2011universality,
  title={Universality, Characteristic Kernels and {RKHS} Embedding of Measures.},
  author={Sriperumbudur, Bharath K and Fukumizu, Kenji and Lanckriet, Gert RG},
  journal={Journal of Machine Learning Research},
  volume={12},
  number={7},
  year={2011}
}

%%%%%%%%%%
% Geographically weighted regression
%%%%%%%%%%

@article{ref:brunsdon1998geographically,
  title={Geographically weighted regression},
  author={Brunsdon, Chris and Fotheringham, Stewart and Charlton, Martin},
  journal={Journal of the Royal Statistical Society: Series D (The Statistician)},
  volume={47},
  number={3},
  pages={431--443},
  year={1998},
  publisher={Wiley Online Library}
}

@book{ref:silverman1986density,
  title={Density Estimation for Statistics and Data Analysis},
  author={Silverman, Bernard W},
  year={1986},
  publisher={CRC Press}
}


@book{ref:hastie09elements,
  author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
  publisher = {Springer},
  title = {The Elements of Statistical Learning},
  year = 2009
}

@article{ref:muandet2016kernel,
  title={Kernel mean embedding of distributions: A review and beyond},
  author={Muandet, Krikamol and Fukumizu, Kenji and Sriperumbudur, Bharath and Sch{\"o}lkopf, Bernhard},
  journal={arXiv preprint arXiv:1605.09522},
  year={2016}
}

@inproceedings{hashimoto2018fairness,
  title={Fairness without demographics in repeated loss minimization},
  author={Hashimoto, Tatsunori and Srivastava, Megha and Namkoong, Hongseok and Liang, Percy},
  booktitle={International Conference on Machine Learning},
  pages={1929--1938},
  year={2018},
}

%%%%%%%%%%%%%%%
% Adversarial Reweighting
%%%%%%%%%%%%%%%

@inproceedings{DBLP:conf/nips/ShuXY0ZXM19,
  author    = {Jun Shu and
               Qi Xie and
               Lixuan Yi and
               Qian Zhao and
               Sanping Zhou and
               Zongben Xu and
               Deyu Meng},
  title     = {Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting},
  booktitle = {Advances in Neural Information Processing Systems},
  volume={32},
  pages     = {1917--1928},
  year      = {2019},
}

@article{wu2016,
  title={Importance Reweighting Using Adversarial-Collaborative Training},
  author={Yifan Wu and Tianshu Ren and Lidan Mu},
  journal={In NIPS 2016 Workshop},
  year={2016}
}

@inproceedings{NIPS2016_9d268236,
 title = {Equality of Opportunity in Supervised Learning},
 author = {Hardt, Moritz and Price, Eric and Price, Eric and Srebro, Nati},
 booktitle = {Advances in Neural Information Processing Systems},
 volume = {29},
 pages = {3315--3323},
 year = {2016}
}

@inproceedings{NIPS2017_82161242,
title = {From Parity to Preference-based Notions of Fairness in Classification},
 author = {Zafar, Muhammad Bilal and Valera, Isabel and Rodriguez, Manuel and Gummadi, Krishna and Weller, Adrian},
 booktitle = {Advances in Neural Information Processing Systems},
 volume = {30},
 pages = {229--239},
 year = {2017},
}

@InProceedings{pmlr-v28-zemel13,
  title = 	 {Learning Fair Representations},
  author = 	 {Rich Zemel and Yu Wu and Kevin Swersky and Toni Pitassi and Cynthia Dwork},
  booktitle = 	 {Proceedings of the 30th International Conference on Machine Learning},
  volume = 	 {28},
  pages = 	 {325--333},
  year = 	 {2013},
}

@inproceedings{NIPS2014_792c7b5a,
title = {Fairness in Multi-Agent Sequential Decision-Making},
 author = {Zhang, Chongjie and Shah, Julie A},
 booktitle = {Advances in Neural Information Processing Systems},
 volume = {27},
 pages = {2636--2644},
 year = {2014},
}

@InProceedings{pmlr-v48-amodei16,
  title = 	 {Deep Speech 2 : End-to-End Speech Recognition in {E}nglish and {M}andarin},
  author = 	 {Dario Amodei and Sundaram Ananthanarayanan and Rishita Anubhai and Jingliang Bai and Eric Battenberg and Carl Case and Jared Casper and Bryan Catanzaro and Qiang Cheng and Guoliang Chen},
  booktitle = 	 {Proceedings of The 33rd International Conference on Machine Learning},
  volume = 	 {48},
  pages = 	 {173--182},
  year = 	 {2016},
}

@inproceedings{DBLP:conf/acl/RajpurkarJL18,
  title={Know What You Don't Know: Unanswerable Questions for squad},
  author={Pranav Rajpurkar and Robin Jia and Percy Liang},
  booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics},
  pages={784-789},
  year={2018},
 }

@inproceedings{sugiyama2007direct,
  title={Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation.},
  author={Sugiyama, Masashi and Nakajima, Shinichi and Kashima, Hisashi and Von Buenau, Paul and Kawanabe, Motoaki},
  booktitle={Advances in Neural Information Processing Systems},
  volume={7},
  pages={1433--1440},
  year={2007},
}

@article{huang2006correcting,
  title={Correcting sample selection bias by unlabeled data},
  author={Huang, Jiayuan and Gretton, Arthur and Borgwardt, Karsten and Sch{\"o}lkopf, Bernhard and Smola, Alex},
  journal={Advances in neural information processing systems},
  volume={19},
  pages={601--608},
  year={2006},
}

@article{sun2011two,
  title={A two-stage weighting framework for multi-source domain adaptation},
  author={Sun, Qian and Chattopadhyay, Rita and Panchanathan, Sethuraman and Ye, Jieping},
  journal={Advances in neural information processing systems},
  volume={24},
  pages={505--513},
  year={2011},
}

@article{petrovic2020fair,
  title={FAIR: Fair Adversarial Instance Re-weighting},
  author={Petrovi{\'c}, Andrija and Nikoli{\'c}, Mladen and Radovanovi{\'c}, Sandro and Deliba{\v{s}}i{\'c}, Boris and Jovanovi{\'c}, Milo{\v{s}}},
  journal={arXiv preprint arXiv:2011.07495},
  year={2020}
}

@inproceedings{ShuNeurIPS19,
  author    = {Jun Shu and
               Qi Xie and
               Lixuan Yi and
               Qian Zhao and
               Sanping Zhou and
               Zongben Xu and
               Deyu Meng},
  title     = {Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting},
  booktitle = {Advances in Neural Information Processing Systems},
  pages     = {1917--1928},
  year      = {2019},
}

@inproceedings{ren2018learning,
  title={Learning to reweight examples for robust deep learning},
  author={Ren, Mengye and Zeng, Wenyuan and Yang, Bin and Urtasun, Raquel},
  booktitle={International Conference on Machine Learning},
  pages={4334--4343},
  year={2018},
  organization={PMLR}
}

@inproceedings{jiang2018mentornet,
  title={Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels},
  author={Jiang, Lu and Zhou, Zhengyuan and Leung, Thomas and Li, Li-Jia and Fei-Fei, Li},
  booktitle={International Conference on Machine Learning},
  pages={2304--2313},
  year={2018},
  organization={PMLR}
}

@inproceedings{chen2018re,
  title={Re-weighted adversarial adaptation network for unsupervised domain adaptation},
  author={Chen, Qingchao and Liu, Yang and Wang, Zhaowen and Wassell, Ian and Chetty, Kevin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={7976--7985},
  year={2018}
}

@inproceedings{
zhang2021geometryaware,
title={Geometry-aware Instance-reweighted Adversarial Training},
author={Jingfeng Zhang and Jianing Zhu and Gang Niu and Bo Han and Masashi Sugiyama and Mohan Kankanhalli},
booktitle={International Conference on Learning Representations},
year={2021},
}

%%%%%%%%
% NW
%%%%%%%
@article{nadaraya1964estimating,
  title={On estimating regression},
  author={Nadaraya, Elizbar A},
  journal={Theory of Probability \& Its Applications},
  volume={9},
  number={1},
  pages={141--142},
  year={1964},
  publisher={SIAM}
}

@article{watson1964smooth,
  title={Smooth regression analysis},
  author={Watson, Geoffrey S},
  journal={Sankhy{\=a}: The Indian Journal of Statistics, Series A},
  pages={359--372},
  year={1964},
  publisher={JSTOR}
}
%%%%%%%%
% LLR
%%%%%%%%
@article{atkeson1997locally,
  title={Locally weighted learning},
  author={Atkeson, Christopher G and Moore, Andrew W and Schaal, Stefan},
  journal={Lazy Learning},
  pages={11--73},
  year={1997},
  publisher={Springer}
}

@article{ruppert1994multivariate,
  title={Multivariate locally weighted least squares regression},
  author={Ruppert, David and Wand, Matthew P},
  journal={The Annals of Statistics},
  pages={1346--1370},
  year={1994},
  publisher={JSTOR}
}

%%%%%%%%%%
% ML for NW regression
%%%%%%%%%%
@inproceedings{weinberger2007metric,
  title={Metric learning for kernel regression},
  author={Weinberger, Kilian Q and Tesauro, Gerald},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={612--619},
  year={2007},
  organization={PMLR}
}

@article{kpotufe2016gradients,
  title={Gradients weights improve regression and classification},
  author={Kpotufe, Samory and Boularias, Abdeslam and Schultz, Thomas and Kim, Kyoungok},
  journal={The Journal of Machine Learning Research},
  volume={17},
  number={1},
  pages={672--705},
  year={2016},
  publisher={JMLR. org}
}

%%%%%%%%%
% KERNELS
%%%%%%%%%

@book{berg1984harmonic,
  title={Harmonic Analysis on Semigroups: Theory of Positive Definite and Related Functions},
  author={Berg, Christian and Christensen, Jens Peter Reus and Ressel, Paul},
  year={1984},
  publisher={Springer}
}

@book{scholkopf2018learning,
  title={Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond},
  author={Scholkopf, Bernhard and Smola, Alexander J},
  year={2018},
  publisher={Adaptive Computation and Machine Learning series}
}

@book{rasmussen2005GP,
author = {Rasmussen, Carl Edward and Williams, Christopher K. I.},
title = {Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)},
year = {2005},
isbn = {026218253X},
publisher = {The MIT Press}
}

@article{ref:du2018robust,
	title={Robust Nonparametric Regression under {H}uber's $\epsilon$-contamination Model},
	author = {Simon Du and Yining Wang and Sivaraman Balakrishnan and Pradeep Ravikumar and Aarti Singh},
	journal={arXiv preprint arXiv:1805.10406},
	year={2018}
}


@article{ref:liu2017density,
title = {Density Estimation with Contaminated Data: Minimax Rates and Theory of Adaptation}, 
author = {Haoyang Liu and Chao Gao},
year = 2017,
journal={arXiv preprint arXiv:1712.07801v3}
}
%%%%%%%%%%%
% Sugiyama - Gang Niu
%%%%%%%%%%
@article{kwon2019principled,
  title={Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric},
  author={Kwon, Yongchan and Kim, Wonyoung and Sugiyama, Masashi and Paik, Myunghee Cho},
  journal={Machine Learning},
  pages={1--20},
  year={2019},
  publisher={Springer}
}

@article{karasuyama2012multi,
  title={Multi-parametric solution-path algorithm for instance-weighted support vector machines},
  author={Karasuyama, Masayuki and Harada, Naoyuki and Sugiyama, Masashi and Takeuchi, Ichiro},
  journal={Machine learning},
  volume={88},
  number={3},
  pages={297--330},
  year={2012},
  publisher={Springer}
}

@article{ref:bottou1992local,
    author = {Bottou, Léon and Vapnik, Vladimir},
    title = "Local Learning Algorithms",
    journal = {Neural Computation},
    volume = {4},
    number = {6},
    pages = {888-900},
    year = {1992},
    month = {11},
}



@article{ref:kim2012robust,
	title={Robust Kernel Density Estimation},
	author={JooSeuk Kim and Clayton D. Scott},
	journal={The Journal of Machine Learning Research},
	volume={13},
	pages={2529--2565},
	year={2012}
}

@inproceedings{ref:le2021adversarial,
title = {Adversarial Regression with Doubly Non-negative Weighting Matrices},
author = {Tam Le and Truyen Nguyen and Makoto Yamada and Jose Blanchet and Viet Anh Nguyen},
year = {2021},
booktitle=  {Advances in Neural Information Processing Systems 34},
}

@inproceedings{ref:nguyen2019calculating,
	title={Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization},
	author={Nguyen, Viet Anh and Shafieezadeh-Abadeh, Soroosh and Yue, Man-Chung and Kuhn, Daniel and Wiesemann, Wolfram},
	booktitle={Advances in Neural Information Processing Systems 32},
	year={2019}, 
	pages = {13942--13953},
}


@inproceedings{ref:brunel2018learning,
  title={Learning signed determinantal point processes through the principal minor assignment problem},
  author={Brunel, Victor-Emmanuel},
  booktitle={Proceedings of the 32nd International Conference on Neural Information Processing Systems},
  pages={7376--7385},
  year={2018}
}

@article{ref:gartrell2019learning,
  title={Learning nonsymmetric determinantal point processes},
  author={Gartrell, Mike and Brunel, Victor-Emmanuel and Dohmatob, Elvis and Krichene, Syrine},
  journal={arXiv preprint arXiv:1905.12962},
  year={2019}
}

@article{ref:poulson2020high,
  title={High-performance sampling of generic determinantal point processes},
  author={Poulson, Jack},
  journal={Philosophical Transactions of the Royal Society A},
  volume={378},
  number={2166},
  pages={20190059},
  year={2020},
  publisher={The Royal Society Publishing}
}

@article{ref:wang2021graph,
  title={Graph learning based recommender systems: A review},
  author={Wang, Shoujin and Hu, Liang and Wang, Yan and He, Xiangnan and Sheng, Quan Z and Orgun, Mehmet A and Cao, Longbing and Ricci, Francesco and Yu, Philip S},
  journal={arXiv preprint arXiv:2105.06339},
  year={2021}
}

@inproceedings{ref:baluja2008video,
  title={Video suggestion and discovery for youtube: taking random walks through the view graph},
  author={Baluja, Shumeet and Seth, Rohan and Sivakumar, Dharshi and Jing, Yushi and Yagnik, Jay and Kumar, Shankar and Ravichandran, Deepak and Aly, Mohamed},
  booktitle={Proceedings of the 17th international conference on World Wide Web},
  pages={895--904},
  year={2008}
}

@inproceedings{ref:bagci2016context,
  title={Context-aware friend recommendation for location based social networks using random walk},
  author={Bagci, Hakan and Karagoz, Pinar},
  booktitle={Proceedings of the 25th International Conference Companion on World Wide Web},
  pages={531--536},
  year={2016}
}

@inproceedings{ref:jiang2018recommendation,
  title={Recommendation in heterogeneous information networks based on generalized random walk model and {B}ayesian personalized ranking},
  author={Jiang, Zhengshen and Liu, Hongzhi and Fu, Bin and Wu, Zhonghai and Zhang, Tao},
  booktitle={Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining},
  pages={288--296},
  year={2018}
}

@article{ref:shi2018heterogeneous,
  title={Heterogeneous information network embedding for recommendation},
  author={Shi, Chuan and Hu, Binbin and Zhao, Wayne Xin and Philip, S Yu},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={31},
  number={2},
  pages={357--370},
  year={2018},
  publisher={IEEE}
}

@inproceedings{ref:cen2019representation,
  title={Representation learning for attributed multiplex heterogeneous network},
  author={Cen, Yukuo and Zou, Xu and Zhang, Jianwei and Yang, Hongxia and Zhou, Jingren and Tang, Jie},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={1358--1368},
  year={2019}
}

@inproceedings{ref: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}
}

@inproceedings{ref:ying2018graph,
  title={Graph convolutional neural networks for web-scale recommender systems},
  author={Ying, Rex and He, Ruining and Chen, Kaifeng and Eksombatchai, Pong and Hamilton, William L and Leskovec, Jure},
  booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={974--983},
  year={2018}
}

@inproceedings{ref:xu2019gromov,
  title={Gromov-{W}asserstein learning for graph matching and node embedding},
  author={Xu, Hongteng and Luo, Dixin and Zha, Hongyuan and Duke, Lawrence Carin},
  booktitle={International Conference on Machine Learning},
  pages={6932--6941},
  year={2019},
  organization={PMLR}
}

@InProceedings{ref:scetbon2022linear,
  title = 	 {Linear-Time Gromov {W}asserstein Distances using Low Rank Couplings and Costs},
  author =       {Scetbon, Meyer and Peyr{\'e}, Gabriel and Cuturi, Marco},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {19347--19365},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/scetbon22b/scetbon22b.pdf},
  url = 	 {https://proceedings.mlr.press/v162/scetbon22b.html}
}

@inproceedings{ref:gope2017survey,
  title={A survey on solving cold start problem in recommender systems},
  author={Gope, Jyotirmoy and Jain, Sanjay Kumar},
  booktitle={2017 International Conference on Computing, Communication and Automation (ICCCA)},
  pages={133--138},
  year={2017},
  organization={IEEE}
}

@inproceedings{ref:hao2021pre,
  title={Pre-training graph neural networks for cold-start users and items representation},
  author={Hao, Bowen and Zhang, Jing and Yin, Hongzhi and Li, Cuiping and Chen, Hong},
  booktitle={Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
  pages={265--273},
  year={2021}
}

@inproceedings{ref:kempe2003maximizing,
  title={Maximizing the spread of influence through a social network},
  author={Kempe, David and Kleinberg, Jon and Tardos, {\'E}va},
  booktitle={Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={137--146},
  year={2003}
}

@article{ref:song2011automatic,
  title={Automatic tag recommendation algorithms for social recommender systems},
  author={Song, Yang and Zhang, Lu and Giles, C Lee},
  journal={ACM Transactions on the Web (TWEB)},
  volume={5},
  number={1},
  pages={1--31},
  year={2011},
  publisher={ACM New York, NY, USA}
}

@inproceedings{ref:yu2014personalized,
  title={Personalized entity recommendation: A heterogeneous information network approach},
  author={Yu, Xiao and Ren, Xiang and Sun, Yizhou and Gu, Quanquan and Sturt, Bradley and Khandelwal, Urvashi and Norick, Brandon and Han, Jiawei},
  booktitle={Proceedings of the 7th ACM International Conference on Web search and Data Mining},
  pages={283--292},
  year={2014}
}

@inproceedings{ref:he2020lightgcn,
  title={Light{GCN}: Simplifying and powering graph convolution network for recommendation},
  author={He, Xiangnan and Deng, Kuan and Wang, Xiang and Li, Yan and Zhang, Yongdong and Wang, Meng},
  booktitle={Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={639--648},
  year={2020}
}

@inproceedings{ref:wang2019neural,
  title={Neural graph collaborative filtering},
  author={Wang, Xiang and He, Xiangnan and Wang, Meng and Feng, Fuli and Chua, Tat-Seng},
  booktitle={Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval},
  pages={165--174},
  year={2019}
}

@article{ref:wu2022graph,
  title={Graph neural networks in recommender systems: a survey},
  author={Wu, Shiwen and Sun, Fei and Zhang, Wentao and Xie, Xu and Cui, Bin},
  journal={ACM Computing Surveys},
  volume={55},
  number={5},
  pages={1--37},
  year={2022},
  publisher={ACM New York, NY}
}

@inproceedings{ref:wang2019kgat,
  title={Kgat: Knowledge graph attention network for recommendation},
  author={Wang, Xiang and He, Xiangnan and Cao, Yixin and Liu, Meng and Chua, Tat-Seng},
  booktitle={Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
  pages={950--958},
  year={2019}
}

@incollection{ref:furnkranz2010preference,
  title={Preference learning and ranking by pairwise comparison},
  author={F{\"u}rnkranz, Johannes and H{\"u}llermeier, Eyke},
  booktitle={Preference learning},
  pages={65--82},
  year={2010},
  publisher={Springer}
}

@inproceedings{ref:dong2017metapath2vec,
  title={metapath2vec: Scalable representation learning for heterogeneous networks},
  author={Dong, Yuxiao and Chawla, Nitesh V and Swami, Ananthram},
  booktitle={Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining},
  pages={135--144},
  year={2017}
}

@misc{ref:cfgnn2021lucic,
  doi = {10.48550/ARXIV.2102.03322},
  
  url = {https://arxiv.org/abs/2102.03322},
  
  author = {Lucic, Ana and ter Hoeve, Maartje and Tolomei, Gabriele and de Rijke, Maarten and Silvestri, Fabrizio},
  
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{ref:stiennon2020learning,
  title={Learning to summarize with human feedback},
  author={Stiennon, Nisan and Ouyang, Long and Wu, Jeffrey and Ziegler, Daniel and Lowe, Ryan and Voss, Chelsea and Radford, Alec and Amodei, Dario and Christiano, Paul F},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={3008--3021},
  year={2020}
}

@inproceedings{ref:wang2022off,
  title={Off-policy Learning over Heterogeneous Information for Recommendation},
  author={Wang, Xiangmeng and Li, Qian and Yu, Dianer and Xu, Guandong},
  booktitle={Proceedings of the ACM Web Conference 2022},
  pages={2348--2359},
  year={2022}
}

@inproceedings{ref:wan2020reinforcement,
  title={Reinforcement learning based meta-path discovery in large-scale heterogeneous information networks},
  author={Wan, Guojia and Du, Bo and Pan, Shirui and Haffari, Gholameza},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={6094--6101},
  year={2020}
}

@inproceedings{ref:ning2022automatic,
  title={Automatic Meta-Path Discovery for Effective Graph-Based Recommendation},
  author={Ning, Wentao and Cheng, Reynold and Shen, Jiajun and Haldar, Nur Al Hasan and Kao, Ben and Yan, Xiao and Huo, Nan and Lam, Wai Kit and Li, Tian and Tang, Bo},
  booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  pages={1563--1572},
  year={2022}
}

@article{ref:schulman2017proximal,
  title={Proximal policy optimization algorithms},
  author={Schulman, John and Wolski, Filip and Dhariwal, Prafulla and Radford, Alec and Klimov, Oleg},
  journal={arXiv preprint arXiv:1707.06347},
  year={2017}
}

@inproceedings{ref:arthur2007kmeans, 
author = {Arthur, David and Vassilvitskii, Sergei}, 
title = {K-Means++: The Advantages of Careful Seeding}, 
year = {2007}, 
isbn = {9780898716245}, 
publisher = {Society for Industrial and Applied Mathematics}, 
address = {USA}, 
booktitle = {Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms}, 
pages = {1027–1035}, 
numpages = {9}, 
location = {New Orleans, Louisiana}, 
series = {SODA '07} 
}

@inproceedings{ref:kalloori2018eliciting,
  title={Eliciting pairwise preferences in recommender systems},
  author={Kalloori, Saikishore and Ricci, Francesco and Gennari, Rosella},
  booktitle={Proceedings of the 12th ACM Conference on Recommender Systems},
  pages={329--337},
  year={2018}
}

@INPROCEEDINGS{ref:ostrovsky2006effectiveness,
  author={Ostrovsky, Rafail and Rabani, Yuval and Schulman, Leonard J. and Swamy, Chaitanya},
  booktitle={2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06)}, 
  title={The Effectiveness of {L}loyd-Type Methods for the k-Means Problem}, 
  year={2006},
  volume={},
  number={},
  pages={165-176},
  doi={10.1109/FOCS.2006.75}
  }





@article{ref:papadimitriou1981worst,
author = {Papadimitriou, Christos H.},
title = {Worst-Case and Probabilistic Analysis of a Geometric Location Problem},
journal = {SIAM Journal on Computing},
volume = {10},
number = {3},
pages = {542-557},
year = {1981},
}

@inproceedings{ref:amatriain2009wisdom,
  title={The wisdom of the few: a collaborative filtering approach based on expert opinions from the web},
  author={Amatriain, Xavier and Lathia, Neal and Pujol, Josep M and Kwak, Haewoon and Oliver, Nuria},
  booktitle={Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={532--539},
  year={2009}
}


@article{ref:huang2022learning,
author = {Huang, Xiaowen and Sang, Jitao and Yu, Jian and Xu, Changsheng},
title = {Learning to Learn a Cold-Start Sequential Recommender},
year = {2022},
issue_date = {April 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {40},
number = {2},
issn = {1046-8188},
url = {https://doi.org/10.1145/3466753},
doi = {10.1145/3466753},
journal = {ACM Trans. Inf. Syst.},
month = {feb},
articleno = {30},
numpages = {25},
keywords = {graph representation, Cold-start recommendation, sequential recommendation, meta-learning}
}


@article{ref:banovic2018warming,
author = {Banovic, Nikola and Krumm, John},
title = {Warming Up to Cold Start Personalization},
year = {2018},
issue_date = {December 2017},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {1},
number = {4},
url = {https://doi.org/10.1145/3161175},
doi = {10.1145/3161175},
abstract = {Smart agents face abandonment if they are unable to provide value to the users from the very first interaction. Existing smart agents take time to learn about new users before they can offer them personalized services. We present a method for learning personalization information about users quickly and without placing unnecessary hardship on them. Our method enables smart agents to pick which questions to ask the user when they first interact to maximize the agent's overall knowledge about the user. We demonstrate our method on two publically available US census datasets containing 172 user variables from 1,799,394 training and 1,618,489 testing users. The questions selected using our method improve the agent's accuracy when inferring information about future users, including information that they did not ask about. Our work enables smart agents that assist the user with personalized services soon after they start interacting.},
journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
month = {jan},
articleno = {124},
numpages = {13},
keywords = {Cold start, Submodularity, Personalization}
}

@inproceedings{ref:rossetti2016contrasting,
  title={Contrasting offline and online results when evaluating recommendation algorithms},
  author={Rossetti, Marco and Stella, Fabio and Zanker, Markus},
  booktitle={Proceedings of the 10th ACM conference on recommender systems},
  pages={31--34},
  year={2016}
}

@inproceedings{ref:kostric2021soliciting,
  title={Soliciting user preferences in conversational recommender systems via usage-related questions},
  author={Kostric, Ivica and Balog, Krisztian and Radlinski, Filip},
  booktitle={Proceedings of the 15th ACM Conference on Recommender Systems},
  pages={724--729},
  year={2021}
}

@article{ref:batmaz2019review,
  title={A review on deep learning for recommender systems: challenges and remedies},
  author={Batmaz, Zeynep and Yurekli, Ali and Bilge, Alper and Kaleli, Cihan},
  journal={Artificial Intelligence Review},
  volume={52},
  pages={1--37},
  year={2019},
  publisher={Springer}
}

@article{ref:alamdari2020systematic,
  title={A systematic study on the recommender systems in the E-commerce},
  author={Alamdari, Pegah Malekpour and Navimipour, Nima Jafari and Hosseinzadeh, Mehdi and Safaei, Ali Asghar and Darwesh, Aso},
  journal={IEEE Access},
  volume={8},
  pages={115694--115716},
  year={2020},
  publisher={IEEE}
}

@article{ref:jiang2019trust,
  title={A trust-based collaborative filtering algorithm for E-commerce recommendation system},
  author={Jiang, Liaoliang and Cheng, Yuting and Yang, Li and Li, Jing and Yan, Hongyang and Wang, Xiaoqin},
  journal={Journal of Ambient Intelligence and Humanized Computing},
  volume={10},
  pages={3023--3034},
  year={2019},
  publisher={Springer}
}

@article{ref:natarajan2020resolving,
  title={Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data},
  author={Natarajan, Senthilselvan and Vairavasundaram, Subramaniyaswamy and Natarajan, Sivaramakrishnan and Gandomi, Amir H},
  journal={Expert Systems with Applications},
  volume={149},
  pages={113248},
  year={2020},
  publisher={Elsevier}
}

@inproceedings{ref:wu2019neural,
  title={A neural influence diffusion model for social recommendation},
  author={Wu, Le and Sun, Peijie and Fu, Yanjie and Hong, Richang and Wang, Xiting and Wang, Meng},
  booktitle={Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={235--244},
  year={2019}
}

@article{ref:khanal2020systematic,
  title={A systematic review: machine learning based recommendation systems for e-learning},
  author={Khanal, Shristi Shakya and Prasad, PWC and Alsadoon, Abeer and Maag, Angelika},
  journal={Education and Information Technologies},
  volume={25},
  pages={2635--2664},
  year={2020},
  publisher={Springer}
}

@article{ref:george2019review,
  title={Review of ontology-based recommender systems in e-learning},
  author={George, Gina and Lal, Anisha M},
  journal={Computers \& Education},
  volume={142},
  pages={103642},
  year={2019},
  publisher={Elsevier}
}

@inproceedings{ref:mohamed2019recommender,
  title={Recommender systems challenges and solutions survey},
  author={Mohamed, Marwa Hussien and Khafagy, Mohamed Helmy and Ibrahim, Mohamed Hasan},
  booktitle={2019 International Conference on Innovative Trends in Computer Engineering (ITCE)},
  pages={149--155},
  year={2019},
  organization={IEEE}
}

@article{ref:guo2020survey,
  title={A survey on knowledge graph-based recommender systems},
  author={Guo, Qingyu and Zhuang, Fuzhen and Qin, Chuan and Zhu, Hengshu and Xie, Xing and Xiong, Hui and He, Qing},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={34},
  number={8},
  pages={3549--3568},
  year={2020},
  publisher={IEEE}
}

@inproceedings{ref:kweon2020deep,
  title={Deep Rating Elicitation for New Users in Collaborative Filtering},
  author={Kweon, Wonbin and Kang, Seongku and Hwang, Junyoung and Yu, Hwanjo},
  booktitle={Proceedings of The Web Conference 2020},
  pages={2810--2816},
  year={2020}
}


@inproceedings{kalloori2019item,
  title={Item recommendation by combining relative and absolute feedback data},
  author={Kalloori, Saikishore and Li, Tianyu and Ricci, Francesco},
  booktitle={Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={933--936},
  year={2019}
}

@article{ref:kulesza2012determinantal,
  title={Determinantal point processes for machine learning},
  author={Kulesza, Alex and Taskar, Ben and others},
  journal={Foundations and Trends{\textregistered} in Machine Learning},
  volume={5},
  number={2--3},
  pages={123--286},
  year={2012},
  publisher={Now Publishers, Inc.}
}

@article{ref:perez2021multi,
  title={Multi-document summarization with determinantal point process attention},
  author={Perez-Beltrachini, Laura and Lapata, Mirella},
  journal={Journal of Artificial Intelligence Research},
  volume={71},
  pages={371--399},
  year={2021}
}

@inproceedings{ref:chao2015large,
  title={Large-Margin Determinantal Point Processes.},
  author={Chao, Wei-Lun and Gong, Boqing and Grauman, Kristen and Sha, Fei},
  booktitle={UAI},
  pages={191--200},
  year={2015}
}

@article{ref:chen2018fast,
  title={Fast greedy map inference for determinantal point process to improve recommendation diversity},
  author={Chen, Laming and Zhang, Guoxin and Zhou, Eric},
  journal={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}

@article{ref:gillenwater2012near,
  title={Near-optimal map inference for determinantal point processes},
  author={Gillenwater, Jennifer and Kulesza, Alex and Taskar, Ben},
  journal={Advances in Neural Information Processing Systems},
  volume={25},
  year={2012}
}

@article{ref:adomavicius2005toward,
  title={Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions},
  author={Adomavicius, Gediminas and Tuzhilin, Alexander},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={17},
  number={6},
  pages={734--749},
  year={2005},
  publisher={IEEE}
}

@article{ref:schafer2007collaborative,
  title={Collaborative filtering recommender systems},
  author={Schafer, J Ben and Frankowski, Dan and Herlocker, Jon and Sen, Shilad},
  journal={The Adaptive Web: Methods and Strategies of Web Personalization},
  pages={291--324},
  year={2007},
  publisher={Springer}
}

@article{ref:bobadilla2013recommender,
  title={Recommender systems survey},
  author={Bobadilla, Jes{\'u}s and Ortega, Fernando and Hernando, Antonio and Guti{\'e}rrez, Abraham},
  journal={Knowledge-based systems},
  volume={46},
  pages={109--132},
  year={2013},
  publisher={Elsevier}
}

@article{ref:civril2009selecting,
  title={On selecting a maximum volume sub-matrix of a matrix and related problems},
  author={Civril, Ali and Magdon-Ismail, Malik},
  journal={Theoretical Computer Science},
  volume={410},
  number={47-49},
  pages={4801--4811},
  year={2009},
  publisher={Elsevier}
}

@article{ref:bobadilla2012collaborative,
  title={A collaborative filtering approach to mitigate the new user cold start problem},
  author={Bobadilla, Jes{\'u}S and Ortega, Fernando and Hernando, Antonio and Bernal, Jes{\'u}s},
  journal={Knowledge-based Systems},
  volume={26},
  pages={225--238},
  year={2012},
  publisher={Elsevier}
}

@inproceedings{ref:sedhain2014social,
  title={Social collaborative filtering for cold-start recommendations},
  author={Sedhain, Suvash and Sanner, Scott and Braziunas, Darius and Xie, Lexing and Christensen, Jordan},
  booktitle={Proceedings of the 8th ACM Conference on Recommender systems},
  pages={345--348},
  year={2014}
}

@article{ref:ahn2008new,
  title={A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem},
  author={Ahn, Hyung Jun},
  journal={Information Sciences},
  volume={178},
  number={1},
  pages={37--51},
  year={2008},
  publisher={Elsevier}
}

@article{ref:camacho2018social,
  title={Social network data to alleviate cold-start in recommender system: A systematic review},
  author={Camacho, Lesly Alejandra Gonzalez and Alves-Souza, Solange Nice},
  journal={Information Processing \& Management},
  volume={54},
  number={4},
  pages={529--544},
  year={2018},
  publisher={Elsevier}
}

@article{ref:son2017content,
  title={Content-based filtering for recommendation systems using multiattribute networks},
  author={Son, Jieun and Kim, Seoung Bum},
  journal={Expert Systems with Applications},
  volume={89},
  pages={404--412},
  year={2017},
  publisher={Elsevier}
}

@article{ref:zhang2019deep,
  title={Deep learning based recommender system: A survey and new perspectives},
  author={Zhang, Shuai and Yao, Lina and Sun, Aixin and Tay, Yi},
  journal={ACM Computing Surveys (CSUR)},
  volume={52},
  number={1},
  pages={1--38},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@inproceedings{ref:wang2015collaborative,
  title={Collaborative deep learning for recommender systems},
  author={Wang, Hao and Wang, Naiyan and Yeung, Dit-Yan},
  booktitle={Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={1235--1244},
  year={2015}
}

@article{ref:wei2017collaborative,
  title={Collaborative filtering and deep learning based recommendation system for cold start items},
  author={Wei, Jian and He, Jianhua and Chen, Kai and Zhou, Yi and Tang, Zuoyin},
  journal={Expert Systems with Applications},
  volume={69},
  pages={29--39},
  year={2017},
  publisher={Elsevier}
}

@article{ref:pu2012evaluating,
  title={Evaluating recommender systems from the user's perspective: {S}urvey of the state of the art},
  author={Pu, Pearl and Chen, Li and Hu, Rong},
  journal={User Modeling and User-Adapted Interaction},
  volume={22},
  number={4},
  pages={317--355},
  year={2012},
  publisher={Springer Netherlands}
}

@INPROCEEDINGS{ref:eldar2008statistical,
  author={Eldar, Yonina C.},
  booktitle={2008 IEEE International Conference on Acoustics, Speech and Signal Processing}, 
  title={Statistical aspects of the chebyshev center estimate}, 
  year={2008},
  volume={},
  number={},
  pages={3677-3680},
  doi={10.1109/ICASSP.2008.4518450}}

@inproceedings{ref:wei2020fast,
  title={Fast adaptation for cold-start collaborative filtering with meta-learning},
  author={Wei, Tianxin and Wu, Ziwei and Li, Ruirui and Hu, Ziniu and Feng, Fuli and He, Xiangnan and Sun, Yizhou and Wang, Wei},
  booktitle={2020 IEEE International Conference on Data Mining (ICDM)},
  pages={661--670},
  year={2020},
  organization={IEEE}
}

@article{ref:anwar2022collaborative,
  title={Collaborative filtering and {kNN} based recommendation to overcome cold start and sparsity issues: A comparative analysis},
  author={Anwar, Taushif and Uma, V. and Hussain, Md. Imran and Pantula, Muralidhar},
  journal={Multimedia Tools and Applications},
  volume={81},
  number={25},
  pages={35693--35711},
  year={2022},
  publisher={Springer}
}

@inproceedings{ref:rajapakse2022fast,
  title={Fast and Accurate User Cold-Start Learning Using {M}onte {C}arlo Tree Search},
  author={Rajapakse, Dilina Chandika and Leith, Douglas},
  booktitle={Proceedings of the 16th ACM Conference on Recommender Systems},
  pages={350--359},
  year={2022}
}

@inproceedings{ref:salha2021cold,
  title={Cold start similar artists ranking with gravity-inspired graph autoencoders},
  author={Salha-Galvan, Guillaume and Hennequin, Romain and Chapus, Benjamin and Tran, Viet-Anh and Vazirgiannis, Michalis},
  booktitle={Proceedings of the 15th ACM Conference on Recommender Systems},
  pages={443--452},
  year={2021}
}

@inproceedings{ref:raziperchikolaei2021shared,
  title={Shared neural item representations for completely cold start problem},
  author={Raziperchikolaei, Ramin and Liang, Guannan and Chung, Young-joo},
  booktitle={Proceedings of the 15th ACM Conference on Recommender Systems},
  pages={422--431},
  year={2021}
}

@inproceedings{ref:chu2023meta,
  title={Meta Policy Learning for Cold-Start Conversational Recommendation},
  author={Chu, Zhendong and Wang, Hongning and Xiao, Yun and Long, Bo and Wu, Lingfei},
  booktitle={Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining},
  pages={222--230},
  year={2023}
}

@inproceedings{ref:tao2022sminet,
  title={{SMINet}: {S}tate-{A}ware {M}ulti-{A}spect {I}nterests {R}epresentation {N}etwork for {C}old-{S}tart {U}sers {R}ecommendation},
  author={Tao, Wanjie and Li, Yu and Li, Liangyue and Chen, Zulong and Wen, Hong and Chen, Peilin and Liang, Tingting and Lu, Quan},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  pages={8476--8484},
  year={2022}
}

@inproceedings{ref:yu2021personalized,
  title={Personalized adaptive meta learning for cold-start user preference prediction},
  author={Yu, Runsheng and Gong, Yu and He, Xu and Zhu, Yu and Liu, Qingwen and Ou, Wenwu and An, Bo},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  pages={10772--10780},
  year={2021}
}

@inproceedings{ref:zheng2021cold,
  title={Cold-start sequential recommendation via meta learner},
  author={Zheng, Yujia and Liu, Siyi and Li, Zekun and Wu, Shu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  pages={4706--4713},
  year={2021}
}

@article{ref:silva2023user,
  title={User cold-start problem in multi-armed bandits: When the first recommendations guide the user’s experience},
  author={Silva, Nicollas and Silva, Thiago and Werneck, Heitor and Rocha, Leonardo and Pereira, Adriano},
  journal={ACM Transactions on Recommender Systems},
  volume={1},
  number={1},
  pages={1--24},
  year={2023},
  publisher={ACM New York, NY}
}

@inproceedings{ref:gong2023full,
  title={Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation},
  author={Gong, Zhen and Wu, Xin and Chen, Lei and Zheng, Zhenzhe and Wang, Shengjie and Xu, Anran and Wang, Chong and Wu, Fan},
  booktitle={Proceedings of the 17th ACM Conference on Recommender Systems},
  pages={47--57},
  year={2023}
}