Jonas Peters

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 31

Top Topics

Transfer Learning

Top Venues

J. Mach. Learn. Res.

IEEE Trans. Pattern Anal. Mach. Intell.

Publications

Juan L. Gamella, Jonas Peters, Peter Bühlmann
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology. CoRR (2024)
Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Kumar Ravikumar, Niklas Pfister, Jonas Peters
Identifying Representations for Intervention Extrapolation. ICLR (2024)
Felix Schur, Jonas Peters
DecoR: Deconfounding Time Series with Robust Regression. CoRR (2024)
Margherita Lazzaretto, Jonas Peters, Niklas Pfister
Invariant Subspace Decomposition. CoRR (2024)
Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, Niklas Pfister
Invariant Policy Learning: A Causal Perspective. IEEE Trans. Pattern Anal. Mach. Intell. 45 (7) (2023)
Frederik Hytting Jørgensen, Sebastian Weichwald, Jonas Peters
Unfair Utilities and First Steps Towards Improving Them. CoRR (2023)
Sorawit Saengkyongam, Niklas Pfister, Predrag V. Klasnja, Susan Murphy, Jonas Peters
Effect-Invariant Mechanisms for Policy Generalization. CoRR (2023)
Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister
Boosted Control Functions. CoRR (2023)
Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar, Niklas Pfister, Jonas Peters
Identifying Representations for Intervention Extrapolation. CoRR (2023)
Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters
Exploiting Independent Instruments: Identification and Distribution Generalization. CoRR (2022)
Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee, Dominik Baumann, Oliver Kroemer, Isabelle Guyon, Sebastian Trimpe, Jonas Peters, Niklas Pfister
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning. CoRR (2022)
Phillip B. Mogensen, Nikolaj Thams, Jonas Peters
Invariant Ancestry Search. CoRR (2022)
Martin Emil Jakobsen, Rajen Dinesh Shah, Peter Bühlmann, Jonas Peters
Structure Learning for Directed Trees. J. Mach. Learn. Res. 23 (2022)
Phillip B. Mogensen, Nikolaj Thams, Jonas Peters
Invariant Ancestry Search. ICML (2022)
Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters
Exploiting Independent Instruments: Identification and Distribution Generalization. ICML (2022)
Niklas Pfister, Jonas Peters
Identifiability of sparse causal effects using instrumental variables. UAI (2022)
Rune Christiansen, Niklas Pfister, Martin Emil Jakobsen, Nicola Gnecco, Jonas Peters
A Causal Framework for Distribution Generalization. IEEE Trans. Pattern Anal. Mach. Intell. 44 (10) (2022)
Martin Emil Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters
Structure Learning for Directed Trees. CoRR (2021)
Sebastian Weichwald, Jonas Peters
Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness. J. Cogn. Neurosci. 33 (2) (2021)
Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, Niklas Pfister
Invariant Policy Learning: A Causal Perspective. CoRR (2021)
Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee, Dominik Baumann, Oliver Kroemer, Isabelle Guyon, Sebastian Trimpe, Jonas Peters, Niklas Pfister
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning. NeurIPS (Competition and Demos) (2021)
Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
Regularizing towards Causal Invariance: Linear Models with Proxies. ICML (2021)
Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
Regularizing towards Causal Invariance: Linear Models with Proxies. CoRR (2021)
Rune Christiansen, Jonas Peters
Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables. J. Mach. Learn. Res. 21 (2020)
Martin Emil Jakobsen, Jonas Peters
Distributional Robustness of K-class Estimators and the PULSE. CoRR (2020)
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard E. Turner, Jonas Peters
Invariant Models for Causal Transfer Learning. J. Mach. Learn. Res. 19 (2018)
Niklas Pfister, Stefan Bauer, Jonas Peters
Identifying Causal Structure in Large-Scale Kinetic Systems. CoRR (2018)
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. J. Mach. Learn. Res. 17 (2016)
Stephan Bongers, Jonas Peters, Bernhard Schölkopf, Joris M. Mooij
Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions. CoRR (2016)
Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
Modeling confounding by half-sibling regression. Proc. Natl. Acad. Sci. USA 113 (27) (2016)
Stefan Bauer, Bernhard Schölkopf, Jonas Peters
The Arrow of Time in Multivariate Time Series. ICML (2016)
Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
Removing systematic errors for exoplanet search via latent causes. ICML (2015)
Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
Removing systematic errors for exoplanet search via latent causes. CoRR (2015)
Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions. NIPS (2015)
Jonas Peters, Peter Bühlmann
Structural Intervention Distance for Evaluating Causal Graphs. Neural Comput. 27 (3) (2015)
Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. CoRR (2015)
Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Stefan Harmeling, Jonas Peters
Computing functions of random variables via reproducing kernel Hilbert space representations. Stat. Comput. 25 (4) (2015)
Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
Causal discovery with continuous additive noise models. J. Mach. Learn. Res. 15 (1) (2014)
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
Distinguishing cause from effect using observational data: methods and benchmarks. CoRR (2014)
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
Semi-supervised Learning in Causal and Anticausal Settings. Empirical Inference (2013)
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Causal Inference on Time Series using Restricted Structural Equation Models. NIPS (2013)
Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. CoRR (2013)
Peter Bühlmann, Jonas Peters, Jan Ernest
CAM: Causal Additive Models, high-dimensional order search and penalized regression. CoRR (2013)
Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. UAI (2013)
Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Xavier Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Y. Simard, Ed Snelson
Counterfactual reasoning and learning systems: the example of computational advertising. J. Mach. Learn. Res. 14 (1) (2013)
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Causal Inference on Time Series using Structural Equation Models CoRR (2012)
Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf
Identifying confounders using additive noise models CoRR (2012)
Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
Identifiability of Causal Graphs using Functional Models CoRR (2012)
Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Xavier Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Y. Simard, Ed Snelson
Counterfactual Reasoning and Learning Systems CoRR (2012)
Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Kernel-based Conditional Independence Test and Application in Causal Discovery CoRR (2012)
Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf
Detecting low-complexity unobserved causes CoRR (2012)
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
On causal and anticausal learning. ICML (2012)
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Causal Inference on Discrete Data Using Additive Noise Models. IEEE Trans. Pattern Anal. Mach. Intell. 33 (12) (2011)
Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
Identifiability of Causal Graphs using Functional Models. UAI (2011)
Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI (2011)
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Kun Zhang
Robust Learning via Cause-Effect Models CoRR (2011)
Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf
Detecting low-complexity unobserved causes. UAI (2011)
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Identifying Cause and Effect on Discrete Data using Additive Noise Models. AISTATS (2010)
Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
Regression by dependence minimization and its application to causal inference in additive noise models. ICML (2009)
Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf
Identifying confounders using additive noise models. UAI (2009)
Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf
Detecting the direction of causal time series. ICML (2009)
Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf
Nonlinear causal discovery with additive noise models. NIPS (2008)
Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf
Kernel Methods for Detecting the Direction of Time Series. GfKl (2008)