Jonas Peters

According to our database, Jonas Peters authored at least 47 papers between 2008 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Invariant Policy Learning: A Causal Perspective.

Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, Niklas Pfister

IEEE Trans. Pattern Anal. Mach. Intell., July, 2023

Boosted Control Functions.

Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister

CoRR, 2023

Identifying Representations for Intervention Extrapolation.

Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar, Niklas Pfister, Jonas Peters

CoRR, 2023

Effect-Invariant Mechanisms for Policy Generalization.

Sorawit Saengkyongam, Niklas Pfister, Predrag V. Klasnja, Susan A. Murphy, Jonas Peters

CoRR, 2023

Unfair Utilities and First Steps Towards Improving Them.

Frederik Hytting Jørgensen, Sebastian Weichwald, Jonas Peters

CoRR, 2023

2022

A Causal Framework for Distribution Generalization.

Rune Christiansen, Niklas Pfister, Martin Emil Jakobsen, Nicola Gnecco, Jonas Peters

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Structure Learning for Directed Trees.

Martin Emil Jakobsen, Rajen Dinesh Shah, Peter Bühlmann, Jonas Peters

J. Mach. Learn. Res., 2022

Identifiability of sparse causal effects using instrumental variables.

Niklas Pfister, Jonas Peters

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Exploiting Independent Instruments: Identification and Distribution Generalization.

Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters

Proceedings of the International Conference on Machine Learning, 2022

Invariant Ancestry Search.

Phillip B. Mogensen, Nikolaj Thams, Jonas Peters

Proceedings of the International Conference on Machine Learning, 2022

Causal Models for Dynamical Systems.

Jonas Peters, Stefan Bauer, Niklas Pfister

Proceedings of the Probabilistic and Causal Inference: The Works of Judea Pearl, 2022

2021

Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness.

Sebastian Weichwald, Jonas Peters

J. Cogn. Neurosci., 2021

Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning.

Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee, Dominik Baumann, Oliver Kroemer, Isabelle Guyon, Sebastian Trimpe, Jonas Peters, Niklas Pfister

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Regularizing towards Causal Invariance: Linear Models with Proxies.

Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables.

Rune Christiansen, Jonas Peters

J. Mach. Learn. Res., 2020

Distributional Robustness of K-class Estimators and the PULSE.

Martin Emil Jakobsen, Jonas Peters

CoRR, 2020

2018

Invariant Models for Causal Transfer Learning.

Mateo Rojas-Carulla, Bernhard Schölkopf, Richard E. Turner, Jonas Peters

J. Mach. Learn. Res., 2018

Identifying Causal Structure in Large-Scale Kinetic Systems.

Niklas Pfister, Stefan Bauer, Jonas Peters

CoRR, 2018

2016

Modeling confounding by half-sibling regression.

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

Proc. Natl. Acad. Sci. USA, 2016

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks.

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

J. Mach. Learn. Res., 2016

Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions.

Stephan Bongers, Jonas Peters, Bernhard Schölkopf, Joris M. Mooij

CoRR, 2016

The Arrow of Time in Multivariate Time Series.

Stefan Bauer, Bernhard Schölkopf, Jonas Peters

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Computing functions of random variables via reproducing kernel Hilbert space representations.

Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Stefan Harmeling, Jonas Peters

Stat. Comput., 2015

Structural Intervention Distance for Evaluating Causal Graphs.

Jonas Peters, Peter Bühlmann

Neural Comput., 2015

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations.

Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters

CoRR, 2015

BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions.

Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Removing systematic errors for exoplanet search via latent causes.

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

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Causal discovery with continuous additive noise models.

Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

J. Mach. Learn. Res., 2014

2013

Counterfactual reasoning and learning systems: the example of computational advertising.

Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Xavier Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Y. Simard, Ed Snelson

J. Mach. Learn. Res., 2013

CAM: Causal Additive Models, high-dimensional order search and penalized regression.

Peter Bühlmann, Jonas Peters, Jan Ernest

CoRR, 2013

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

Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Causal Inference on Time Series using Restricted Structural Equation Models.

Jonas Peters, Dominik Janzing, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Semi-supervised Learning in Causal and Anticausal Settings.

Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij

Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2012

Counterfactual Reasoning and Learning Systems

Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Xavier Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Y. Simard, Ed Snelson

CoRR, 2012

Causal Inference on Time Series using Structural Equation Models

Jonas Peters, Dominik Janzing, Bernhard Schölkopf

CoRR, 2012

On causal and anticausal learning.

Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Causal Inference on Discrete Data Using Additive Noise Models.

Jonas Peters, Dominik Janzing, Bernhard Schölkopf

IEEE Trans. Pattern Anal. Mach. Intell., 2011

Robust Learning via Cause-Effect Models

Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Kun Zhang

CoRR, 2011

Kernel-based Conditional Independence Test and Application in Causal Discovery.

Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf

Proceedings of the UAI 2011, 2011

Identifiability of Causal Graphs using Functional Models.

Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

Proceedings of the UAI 2011, 2011

Detecting low-complexity unobserved causes.

Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf

Proceedings of the UAI 2011, 2011

2010

Identifying Cause and Effect on Discrete Data using Additive Noise Models.

Jonas Peters, Dominik Janzing, Bernhard Schölkopf

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

2009

Identifying confounders using additive noise models.

Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf

Proceedings of the UAI 2009, 2009

Detecting the direction of causal time series.

Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Regression by dependence minimization and its application to causal inference in additive noise models.

Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Nonlinear causal discovery with additive noise models.

Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Kernel Methods for Detecting the Direction of Time Series.

Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf

Proceedings of the Advances in Data Analysis, Data Handling and Business Intelligence, 2008