% Encoding: ISO-8859-1

@article{bai2019deep,
	title={Deep equilibrium models},
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@article{johari2022experimental,
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	year={2022},
	publisher={INFORMS}
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@article{el2021implicit,
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@article{amos2018differentiable,
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@article{wood2018,
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	AUTHOR={Wallenborn, Gr{\'egoire}},   
	
	TITLE={Rebounds Are Structural Effects of Infrastructures and Markets},      
	
	JOURNAL={Frontiers in Energy Research},      
	
	VOLUME={6},      
	
	PAGES={99},     
	
	YEAR={2018},      
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	AUTHOR={Arrobbio, Osman and Padovan, Dario},   
	
	TITLE={A Vicious Tenacity: The Efficiency Strategy Confronted With the Rebound Effect},      
	
	JOURNAL={Frontiers in Energy Research},      
	
	VOLUME={6},      
	
	PAGES={114},     
	
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  booktitle = {Advances in Neural Information Processing Systems 32},
  publisher = {Curran Associates, Inc.},
  title     = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
  year      = {2019},
  pages     = {8024--8035}
}

@inproceedings{Besserve_Sun_Janzing_Scholkopf_2021, 
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booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, 
author={Besserve, Michel and Sun, Remy and Janzing, Dominik and Sch\"olkopf, Bernhard}, 
year={2021}, 
pages={6741-6749} }

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@InProceedings{besserve2018counterfactuals,
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  Booktitle                = {ICLR2020},
  Year                     = {2020},

  Journal                  = {arXiv preprint arXiv:1812.03253}
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  Booktitle                = {AISTATS},
  Year                     = {2018},

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  Month                    = {jun},

  Day                      = {12},
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  Timestamp                = {2017.05.19},
  Type                     = {RESEARCH\_ARTICLE},
  Urldate                  = {2017-05-19}
}

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  Title                    = {On quantum R{\'e}nyi entropies: A new generalization and some properties},
  Author                   = {M{\"u}ller-Lennert, Martin and Dupuis, Fr{\'e}d{\'e}ric and Szehr, Oleg and Fehr, Serge and Tomamichel, Marco},
  Journal                  = {Journal of Mathematical Physics},
  Year                     = {2013},
  Number                   = {12},
  Pages                    = {122203},
  Volume                   = {54},

  Publisher                = {AIP Publishing}
}

@Book{mallat1999wavelet,
  Title                    = {A wavelet tour of signal processing},
  Author                   = {Mallat, St{\'e}phane},
  Publisher                = {Academic press},
  Year                     = {1999}
}

@Article{martius2016extrapolation,
  Title                    = {Extrapolation and learning equations},
  Author                   = {Martius, G. and Lampert, C. H.},
  Journal                  = {arXiv preprint arXiv:1610.02995},
  Year                     = {2016}
}

@InProceedings{mathieu2016disentangling,
  Title                    = {Disentangling factors of variation in deep representation using adversarial training},
  Author                   = {Mathieu, M. F. and Zhao, J. J. and Ramesh, A. and Sprechmann, P. and LeCun, Y.},
  Booktitle                = {Advances in Neural Information Processing Systems},
  Year                     = {2016},
  Pages                    = {5041--5049}
}

@Article{mcintosh2000towards,
  Title                    = {Towards a network theory of cognition},
  Author                   = {McIntosh, Anthony Randal},
  Journal                  = {Neural Networks},
  Year                     = {2000},
  Number                   = {8},
  Pages                    = {861--870},
  Volume                   = {13},

  Publisher                = {Elsevier}
}

@Article{michel2012towards,
  Title                    = {Towards the utilization of EEG as a brain imaging tool},
  Author                   = {Michel, Christoph M and Murray, Micah M},
  Journal                  = {Neuroimage},
  Year                     = {2012},
  Number                   = {2},
  Pages                    = {371--385},
  Volume                   = {61},

  Publisher                = {Elsevier}
}

@Article{mirza2014conditional,
  Title                    = {Conditional generative adversarial nets},
  Author                   = {Mirza, M and Osindero, S},
  Journal                  = {arXiv preprint arXiv:1411.1784},
  Year                     = {2014}
}

@Article{neyshabur2017geometry,
  Title                    = {Geometry of optimization and implicit regularization in deep learning},
  Author                   = {Neyshabur, B. and Tomioka, R. and Salakhutdinov, R. and Srebro, N.},
  Journal                  = {arXiv preprint arXiv:1705.03071},
  Year                     = {2017}
}

@Book{spirtes2000causation,
  Title                    = {Causation, prediction, and search},
  Author                   = {P.~Spirtes, and C.N.~Glymour, and R.~Scheines},
  Publisher                = {MIT press},
  Year                     = {2000},
  Volume                   = {81}
}

@InProceedings{poczos2010estimation,
  Title                    = {Estimation of R{\'e}nyi entropy and mutual information based on generalized nearest-neighbor graphs},
  Author                   = {P{\'o}czos, Barnab{\'a}s and Szepesv{\'a}ri, Csaba and Tax, David},
  Booktitle                = {Advances in Neural Information Processing Systems},
  Year                     = {2010},
  Pages                    = {1849--1857}
}

@Article{paatero1994positive,
  Title                    = {Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values},
  Author                   = {Paatero, P. and Tapper, U.},
  Journal                  = {Environmetrics},
  Year                     = {1994},
  Number                   = {2},
  Pages                    = {111--126},
  Volume                   = {5},

  Publisher                = {Wiley Online Library}
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@InProceedings{pmlr-v80-parascandolo18a,
  Title                    = {Learning Independent Causal Mechanisms},
  Author                   = {Parascandolo, G. and Kilbertus, N. and Rojas-Carulla, M. and Sch{\"o}lkopf, B.},
  Booktitle                = {ICML},
  Year                     = {2018},
  Pages                    = {4036--4044},

  Abstract                 = {Statistical learning relies upon data sampled from a distribution, and we usually do not care what actually generated it in the first place. From the point of view of causal modeling, the structure of each distribution is induced by physical mechanisms that give rise to dependences between observables. Mechanisms, however, can be meaningful autonomous modules of generative models that make sense beyond a particular entailed data distribution, lending themselves to transfer between problems. We develop an algorithm to recover a set of independent (inverse) mechanisms from a set of transformed data points. The approach is unsupervised and based on a set of experts that compete for data generated by the mechanisms, driving specialization. We analyze the proposed method in a series of experiments on image data. Each expert learns to map a subset of the transformed data back to a reference distribution. The learned mechanisms generalize to novel domains. We discuss implications for transfer learning and links to recent trends in generative modeling.},
  File                     = {parascandolo18a.pdf:http\://proceedings.mlr.press/v80/parascandolo18a/parascandolo18a.pdf:PDF}
}

@InProceedings{pmlr-v80-parascandolo18a,
  Title                    = {Learning Independent Causal Mechanisms},
  Author                   = {Parascandolo, G. and Kilbertus, N. and Rojas-Carulla, M. and Sch{\"o}lkopf, B.},
  Booktitle                = {ICML},
  Year                     = {2018},
  Pages                    = {4036--4044},

  Abstract                 = {Statistical learning relies upon data sampled from a distribution, and we usually do not care what actually generated it in the first place. From the point of view of causal modeling, the structure of each distribution is induced by physical mechanisms that give rise to dependences between observables. Mechanisms, however, can be meaningful autonomous modules of generative models that make sense beyond a particular entailed data distribution, lending themselves to transfer between problems. We develop an algorithm to recover a set of independent (inverse) mechanisms from a set of transformed data points. The approach is unsupervised and based on a set of experts that compete for data generated by the mechanisms, driving specialization. We analyze the proposed method in a series of experiments on image data. Each expert learns to map a subset of the transformed data back to a reference distribution. The learned mechanisms generalize to novel domains. We discuss implications for transfer learning and links to recent trends in generative modeling.},
  File                     = {parascandolo18a.pdf:http\://proceedings.mlr.press/v80/parascandolo18a/parascandolo18a.pdf:PDF}
}

@InCollection{pearl2012causal,
  Title                    = {The causal foundations of structural equation modeling},
  Author                   = {Pearl, Judea},
  Booktitle                = {Handbook of Structural Equation Modeling},
  Publisher                = {Guilford Press},
  Year                     = {2014},
  Chapter                  = {5},
  Editor                   = {Rick H. Hoyle}
}

@Book{pearl2009causality,
  Title                    = {Causality},
  Author                   = {Pearl, Judea},
  Publisher                = {Cambridge university press},
  Year                     = {2009},
  Edition                  = {second}
}

@Book{pearl2000causality,
  Title                    = {Causality: models, reasoning and inference},
  Author                   = {Pearl, Judea},
  Publisher                = {Cambridge Univ Press},
  Year                     = {2000},
  Volume                   = {29}
}

@Article{penrose2000central,
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  Author                   = {Penrose, Mathew D},
  Journal                  = {Stochastic processes and their applications},
  Year                     = {2000},
  Number                   = {2},
  Pages                    = {295--320},
  Volume                   = {85},

  Publisher                = {Elsevier}
}

@Book{percival1993spectral,
  Title                    = {Spectral analysis for physical applications},
  Author                   = {Percival, Donald B},
  Publisher                = {Cambridge University Press},
  Year                     = {1993}
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@Article{peters2016causal,
  Title                    = {Causal inference by using invariant prediction: identification and confidence intervals},
  Author                   = {Peters, Jonas and B{\"u}hlmann, Peter and Meinshausen, Nicolai},
  Journal                  = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  Year                     = {2016},
  Number                   = {5},
  Pages                    = {947--1012},
  Volume                   = {78},

  Publisher                = {Wiley Online Library}
}

@InProceedings{peters2009detecting,
  Title                    = {Detecting the direction of causal time series},
  Author                   = {Peters, Jonas and Janzing, Dominik and Gretton, Arthur and Sch{\"o}lkopf, Bernhard},
  Booktitle                = {ICML 2009}
}

@Article{peters2012causal,
  Title                    = {Causal inference on time series using structural equation models},
  Author                   = {Peters, Jonas and Janzing, Dominik and Sch{\"o}lkopf, Bernhard},
  Journal                  = {arXiv preprint arXiv:1207.5136},
  Year                     = {2012}
}

@Book{causality_book,
  Title                    = {Elements of Causal Inference -- Foundations and Learning Algorithms},
  Author                   = {Peters, Jonas and Janzing, Dominik and Sch\"olkopf, Bernhard},
  Publisher                = {MIT Press},
  Year                     = {2017},

  Optaddress               = {Cambridge, MA, USA}
}

@Article{peters2011causal,
  Title                    = {Causal inference on discrete data using additive noise models},
  Author                   = {Peters, Jonas and Janzing, Dominik and Scholkopf, Bernhard},
  Journal                  = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
  Year                     = {2011},
  Number                   = {12},
  Pages                    = {2436--2450},
  Volume                   = {33},

  Publisher                = {IEEE}
}

@Article{proakis1996digital,
  Title                    = {Digital signal processing: principles, algorithms, and applications.},
  Author                   = {J.G.~Proakis},
  Year                     = {1996}
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@Book{pugh2002real,
  Title                    = {Real mathematical analysis},
  Author                   = {Pugh, Charles C},
  Publisher                = {Springer},
  Year                     = {2002}
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@Article{quiroga2000learning,
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  Author                   = {Quiroga, R Quian and Arnhold, J and Grassberger, P},
  Journal                  = {Physical Review E},
  Year                     = {2000},
  Number                   = {5},
  Pages                    = {5142},
  Volume                   = {61},

  Publisher                = {APS}
}

@Article{radford2015unsupervised,
  Title                    = {Unsupervised representation learning with deep convolutional generative adversarial networks},
  Author                   = {Radford, A. and Metz, L. and Chintala, S.},
  Journal                  = {arXiv preprint arXiv:1511.06434},
  Year                     = {2015}
}

@Article{ranneby1996spatial,
  Title                    = {Spatial and temporal models in contextual classification},
  Author                   = {Ranneby, Bo},
  Journal                  = {UNITED STATES DEPARTMENT OF AGRICULTURE FOREST SERVICE GENERAL TECHNICAL REPORT RM},
  Year                     = {1996},
  Pages                    = {451--458},

  Publisher                = {US DEPARTMENT OF AGRICULTURE}
}

@Article{ranneby1984maximum,
  Title                    = {The maximum spacing method. An estimation method related to the maximum likelihood method},
  Author                   = {Ranneby, Bo},
  Journal                  = {Scandinavian Journal of Statistics},
  Year                     = {1984},
  Pages                    = {93--112},

  Publisher                = {JSTOR}
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@Article{rao1999predictive,
  Title                    = {Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects},
  Author                   = {Rao, R. P. N. and Ballard, D. H.},
  Journal                  = {Nature neuroscience},
  Year                     = {1999},
  Number                   = {1},
  Pages                    = {79},
  Volume                   = {2},

  Publisher                = {Nature Publishing Group}
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@Article{rasmussen2006gaussian,
  Title                    = {Gaussian processes for machine learning},
  Author                   = {Rasmussen, Carl Edward},
  Year                     = {2006},

  Publisher                = {Citeseer}
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@Book{reichenbach1991direction,
  Title                    = {The direction of time},
  Author                   = {Reichenbach, Hans},
  Publisher                = {Univ of California Press},
  Year                     = {1991},
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@Article{rezende2015variational,
  Title                    = {Variational inference with normalizing flows},
  Author                   = {Rezende, D. J. and Mohamed, S.},
  Journal                  = {arXiv preprint arXiv:1505.05770},
  Year                     = {2015}
}

@Article{rezende2014stochastic,
  Title                    = {Stochastic backpropagation and approximate inference in deep generative models},
  Author                   = {Rezende, D. J. and Mohamed, S. and Wierstra, D.},
  Journal                  = {arXiv preprint arXiv:1401.4082},
  Year                     = {2014}
}

@Article{rojas2015causal,
  Title                    = {Causal Transfer in Machine Learning},
  Author                   = {Rojas-Carulla, M. and Sch{\"o}lkopf, B. and Turner, R. and Peters, J.},
  Journal                  = {arXiv preprint arXiv:1507.05333},
  Year                     = {2015}
}

@Article{rosanova2012recovery,
  Title                    = {Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients},
  Author                   = {Rosanova, Mario and Gosseries, Olivia and Casarotto, Silvia and Boly, M{\'e}lanie and Casali, Adenauer G and Bruno, Marie-Aur{\'e}lie and Mariotti, Maurizio and Boveroux, Pierre and Tononi, Giulio and Laureys, Steven and others},
  Journal                  = {Brain},
  Year                     = {2012},
  Pages                    = {awr340},

  Publisher                = {Oxford Univ Press}
}

@Article{rosenblum2001detecting,
  Title                    = {Detecting direction of coupling in interacting oscillators},
  Author                   = {Rosenblum, Michael G and Pikovsky, Arkady S},
  Journal                  = {PHYSICAL REVIEW-SERIES E-},
  Year                     = {2001},
  Number                   = {4; PART 1},
  Pages                    = {045202--R},
  Volume                   = {64},

  Publisher                = {American Physical Society}
}

@article{walker2011anderson,
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  year={2011},
  publisher={SIAM}
}


@Book{rudin2011fourier,
  Title                    = {Fourier analysis on groups},
  Author                   = {Rudin, Walter},
  Publisher                = {John Wiley \& Sons},
  Year                     = {2011}
}

@Article{shimizu2006linear,
  Title                    = {A linear non-Gaussian acyclic model for causal discovery},
  Author                   = {S.~Shimizu, and P.O.~Hoyer, and A. Hyv{\"a}rinen, and A. Kerminen,},
  Journal                  = {The Journal of Machine Learning Research},
  Year                     = {2006},
  Pages                    = {2003--2030},
  Volume                   = {7},

  Publisher                = {JMLR. org}
}

@InProceedings{sabour2017dynamic,
  Title                    = {Dynamic routing between capsules},
  Author                   = {Sabour, S. and Frosst, N. and Hinton, G.},
  Booktitle                = {Advances in Neural Information Processing Systems},
  Year                     = {2017},
  Pages                    = {3859--3869}
}

@Article{sagun2017empirical,
  Title                    = {Empirical Analysis of the Hessian of Over-Parametrized Neural Networks},
  Author                   = {Sagun, L. and Evci, U. and Guney, V. U. and Dauphin, Y. and Bottou, L.},
  Journal                  = {arXiv preprint arXiv:1706.04454},
  Year                     = {2017}
}

@Article{sajjadi2018assessing,
  Title                    = {Assessing Generative Models via Precision and Recall},
  Author                   = {Sajjadi, M. S. M. and Bachem, O. and Lucic, M. and Bousquet, O. and Gelly, S.},
  Journal                  = {arXiv preprint arXiv:1806.00035},
  Year                     = {2018}
}

@InProceedings{anticausal,
  Title                    = {On causal and anticausal learning},
  Author                   = {Sch\"olkopf, B. and Janzing, D. and Peters, J. and Sgouritsa, E. and Zhang, K. and Mooij, J.},
  Booktitle                = {ICML 2012},
  Year                     = {2012}
}

@Article{schiff1996detecting,
  Title                    = {Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble},
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  Year                     = {1996},
  Number                   = {6},
  Pages                    = {6708},
  Volume                   = {54},

  Publisher                = {APS}
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  Author                   = {Schreiber, Thomas},
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  Year                     = {2000},
  Number                   = {2},
  Pages                    = {461},
  Volume                   = {85},

  Publisher                = {APS}
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@InProceedings{SgoJanHenSch15,
  Title                    = {Inference of Cause and Effect with Unsupervised Inverse Regression},
  Author                   = {Sgouritsa, E. and Janzing, D. and Hennig, P. and Sch{\"o}lkopf, B.},
  Booktitle                = {ICML 2015},
  Year                     = {2015},

  Department               = {Department Sch{\"o}lkopf},
  Event_name               = {AISTATS 2015},
  Event_place              = {San Diego, California, USA},
  State                    = {4}
}

@InProceedings{shajarisale2015,
  Title                    = {Telling cause from effect in deterministic linear dynamical systems},
  Author                   = {Shajarisales, N. and Janzing, D. and Sch\"{o}lkopf, B. and Besserve, M.},
  Booktitle                = {ICML 2015},
  Year                     = {2015},

  Owner                    = {besserve},
  Timestamp                = {2015.05.24}
}

@InProceedings{shalit2017estimating,
  Title                    = {Estimating individual treatment effect: generalization bounds and algorithms},
  Author                   = {Shalit, U. and Johansson, F. D. and Sontag, D.},
  Booktitle                = {Proceedings of the 34th International Conference on Machine Learning-Volume 70},
  Year                     = {2017},
  Organization             = {JMLR. org},
  Pages                    = {3076--3085}
}

@InCollection{shalizi2006methods,
  Title                    = {Methods and techniques of complex systems science: An overview},
  Author                   = {Shalizi, Cosma Rohilla},
  Booktitle                = {Complex systems science in biomedicine},
  Publisher                = {Springer},
  Year                     = {2006},
  Pages                    = {33--114}
}

@Article{shao1995limit,
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  Author                   = {Shao, Yongzhao and Hahn, Marjorie G},
  Journal                  = {Statistics \& probability letters},
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  Number                   = {2},
  Pages                    = {121--132},
  Volume                   = {24},

  Publisher                = {Elsevier}
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@Book{simon1977definition,
  Title                    = {On the definition of the causal relation},
  Author                   = {Simon, Herbert A},
  Publisher                = {Springer},
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@Article{simon1954spurious,
  Title                    = {Spurious correlation: A causal interpretation*},
  Author                   = {Simon, Herbert A},
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  Pages                    = {467--479},
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  Publisher                = {Taylor \& Francis}
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  Author                   = {Srivastava, N. and Hinton, G. E. and Krizhevsky, A. and Sutskever, I. and Salakhutdinov, R.},
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  Year                     = {2014},
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@Article{stephan2012short,
  Title                    = {A short history of causal modeling of fMRI data},
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  Year                     = {2012},
  Number                   = {2},
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  Volume                   = {62},

  Publisher                = {Elsevier}
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  Year                     = {2011},
  Number                   = {2},
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  Volume                   = {24}
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@InProceedings{tang2013tensor,
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  Author                   = {Tang, Y. and Salakhutdinov, R. and Hinton, G.},
  Booktitle                = {International Conference on Machine Learning},
  Year                     = {2013},
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  Title                    = {Detection of n: m phase locking from noisy data: application to magnetoencephalography},
  Author                   = {Tass, Peter and Rosenblum, MG and Weule, J and Kurths, J and Pikovsky, A and Volkmann, J and Schnitzler, A and Freund, H-J},
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@InProceedings{tsai2016tensor,
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  Booktitle                = {Advances in Neural Information Processing Systems},
  Year                     = {2016},
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  Publisher                = {JSTOR}
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@Article{tsybakov1996root,
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  Year                     = {1996},
  Pages                    = {75--83},

  Publisher                = {JSTOR}
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@Article{,
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@Book{,
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@Comment{jabref-meta: databaseType:bibtex;}

@INPROCEEDINGS{MooJanSch13,
  author = {Mooij, Joris and Janzing, Dominik and Sch{\"o}lkopf, Bernhard},
  title = {From Ordinary Differential Equations to Structural Causal Models:
	the deterministic case},
  booktitle = {Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty
	in Artificial Intelligence},
  year = {2013},
  pages = {440-448},
  address = {Corvallis, OR},
  publisher = {AUAI Press},
  department = {Department Sch{\"o}lkopf},
  event_name = {UAI}
}
