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Jonas Peters

Max Planck Institute for Intelligent Systems

Invariant Policy Learning: A Causal Perspective


Jun 07, 2021
Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, Niklas Pfister


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Regularizing towards Causal Invariance: Linear Models with Proxies


Mar 03, 2021
Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag


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Distributional Robustness of K-class Estimators and the PULSE


May 07, 2020
Martin Emil Jakobsen, Jonas Peters

* 58 pages, 3 figures 

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Identifying Causal Structure in Large-Scale Kinetic Systems


Oct 28, 2018
Niklas Pfister, Stefan Bauer, Jonas Peters


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Invariant Models for Causal Transfer Learning


Sep 24, 2018
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters

* Journal of Machine Learning Research. 19 (2018) 

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Theoretical Aspects of Cyclic Structural Causal Models


Aug 05, 2018
Stephan Bongers, Jonas Peters, Bernhard Schölkopf, Joris M. Mooij

* Will probably be submitted to The Annals of Statistics 

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Kernel-based Tests for Joint Independence


Nov 04, 2016
Niklas Pfister, Peter BĂŒhlmann, Bernhard Schölkopf, Jonas Peters

* 67 pages 

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Distinguishing cause from effect using observational data: methods and benchmarks


Dec 24, 2015
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf

* Journal of Machine Learning Research 17(32):1-102, 2016 
* 101 pages, second revision submitted to Journal of Machine Learning Research 

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backShift: Learning causal cyclic graphs from unknown shift interventions


Nov 18, 2015
Dominik RothenhÀusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen

* Advances in Neural Information Processing Systems 28 (2015) 1513-1521 

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Removing systematic errors for exoplanet search via latent causes


May 12, 2015
Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters

* Extended version of a paper appearing in the Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015 

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Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations


Jan 27, 2015
Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters

* Statistics and Computing 25:755-766 (2015) 

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CAM: Causal additive models, high-dimensional order search and penalized regression


Dec 01, 2014
Peter BĂŒhlmann, Jonas Peters, Jan Ernest

* Annals of Statistics 2014, Vol. 42, No. 6, 2526-2556 
* Published in at http://dx.doi.org/10.1214/14-AOS1260 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

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Structural Intervention Distance (SID) for Evaluating Causal Graphs


Apr 07, 2014
Jonas Peters, Peter BĂŒhlmann

* Neural Computation 27:771-799, 2015 

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Causal Discovery with Continuous Additive Noise Models


Apr 06, 2014
Jonas Peters, Joris Mooij, Dominik Janzing, Bernhard Schölkopf

* Journal of Machine Learning Research 15:2009-2053, 2014 

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On the Intersection Property of Conditional Independence and its Application to Causal Discovery


Mar 04, 2014
Jonas Peters

* Journal of Causal Inference 3:97-108, 2015 

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Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders


Sep 26, 2013
Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schoelkopf

* Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013) 

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Identifiability of Gaussian structural equation models with equal error variances


Aug 28, 2013
Jonas Peters, Peter BĂŒhlmann

* Biometrika 2014, Vol. 101, No. 1, 219-228 

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Counterfactual Reasoning and Learning Systems


Jul 27, 2013
Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson

* revised version 

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Causal Inference on Time Series using Structural Equation Models


Jul 21, 2012
Jonas Peters, Dominik Janzing, Bernhard Schölkopf

* Advances in Neural Information Processing Systems 26, 154-162, 2014 

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On Causal and Anticausal Learning


Jun 27, 2012
Bernhard Schoelkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris Mooij

* Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012). arXiv admin note: substantial text overlap with arXiv:1112.2738 

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Identifying confounders using additive noise models


May 09, 2012
Dominik Janzing, Jonas Peters, Joris Mooij, Bernhard Schoelkopf

* Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009) 

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Detecting low-complexity unobserved causes


Feb 14, 2012
Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schoelkopf


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Identifiability of Causal Graphs using Functional Models


Feb 14, 2012
Jonas Peters, Joris Mooij, Dominik Janzing, Bernhard Schoelkopf


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Kernel-based Conditional Independence Test and Application in Causal Discovery


Feb 14, 2012
Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schoelkopf


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Robust Learning via Cause-Effect Models


Dec 12, 2011
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Kun Zhang

* A version of this paper has been published as "On Causal and Anticausal Learning" in Proceedings of the 29th International Conference on Machine Learning (ICML 2012) 

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Causal Inference on Discrete Data using Additive Noise Models


Nov 02, 2009
Jonas Peters, Dominik Janzing, Bernhard Schölkopf

* IEEE TPAMI vol. 33 no. 12 (2011) 2436-2450 

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