Jonas Peters - RankMe

78 papers A* 9A 7Journal 53Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Anti-causal domain generalization: Leveraging unlabeled data. CoRR Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller, Jonas Peters, Nicolai Meinshausen, Christina Heinze-Deml
2026	J	`jnl`	Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models. J. Mach. Learn. Res. Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister
2026	J	`jnl`	Invariance-Based Dynamic Regret Minimization. CoRR Margherita Lazzaretto, Jonas Peters, Niklas Pfister
2026	J	`jnl`	Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible? CoRR Felix Schur, Niklas Pfister, Peng Ding, Sach Mukherjee, Jonas Peters
2026	J	`jnl`	Worst-case low-rank approximations. CoRR Anya Fries, Markus Reichstein, David Blei, Jonas Peters
2025	J	`jnl`	Causal chambers as a real-world physical testbed for AI methodology. Nat. Mac. Intell. Juan L. Gamella, Jonas Peters, Peter Bühlmann
2025	J	`jnl`	Invariant Subspace Decomposition. J. Mach. Learn. Res. Margherita Lazzaretto, Jonas Peters, Niklas Pfister
2025	J	`jnl`	Maximum Risk Minimization with Random Forests. CoRR Francesco Freni, Anya Fries, Linus Kühne, Markus Reichstein, Jonas Peters
2025	J	`jnl`	Representation Learning for Distributional Perturbation Extrapolation. CoRR Julius von Kügelgen, Jakob Ketterer, Xinwei Shen, Nicolai Meinshausen, Jonas Peters
2025	J	`jnl`	Transferring Causal Effects using Proxies. CoRR Manuel Iglesias-Alonso, Felix Schur, Julius von Kügelgen, Jonas Peters
2024	J	`jnl`	DecoR: Deconfounding Time Series with Robust Regression. CoRR Felix Schur, Jonas Peters
2024	J	`jnl`	Effect-Invariant Mechanisms for Policy Generalization. J. Mach. Learn. Res. Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan A. Murphy, Jonas Peters
2024	J	`jnl`	Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past. J. Mach. Learn. Res. Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, Jonas Peters
2024	A*	`conf`	Identifying Representations for Intervention Extrapolation. ICLR Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Kumar Ravikumar, Niklas Pfister, Jonas Peters
2024	J	`jnl`	Invariant Subspace Decomposition. CoRR Margherita Lazzaretto, Jonas Peters, Niklas Pfister
2024	J	`jnl`	The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology. CoRR Juan L. Gamella, Jonas Peters, Peter Bühlmann
2023	J	`jnl`	Boosted Control Functions. CoRR Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister
2023	J	`jnl`	Effect-Invariant Mechanisms for Policy Generalization. CoRR Sorawit Saengkyongam, Niklas Pfister, Predrag V. Klasnja, Susan A. Murphy, Jonas Peters
2023	J	`jnl`	Identifying Representations for Intervention Extrapolation. CoRR Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar, Niklas Pfister, Jonas Peters
2023	J	`jnl`	Invariant Policy Learning: A Causal Perspective. IEEE Trans. Pattern Anal. Mach. Intell. Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, Niklas Pfister
2023	J	`jnl`	Unfair Utilities and First Steps Towards Improving Them. CoRR Frederik Hytting Jørgensen, Sebastian Weichwald, Jonas Peters
2022	J	`jnl`	A Causal Framework for Distribution Generalization. IEEE Trans. Pattern Anal. Mach. Intell. Rune Christiansen, Niklas Pfister, Martin Emil Jakobsen, Nicola Gnecco, Jonas Peters
2022	—	`ch.`	Causal Models for Dynamical Systems. Probabilistic and Causal Inference Jonas Peters, Stefan Bauer, Niklas Pfister
2022	A*	`conf`	Exploiting Independent Instruments: Identification and Distribution Generalization. ICML Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters
2022	J	`jnl`	Exploiting Independent Instruments: Identification and Distribution Generalization. CoRR Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters
2022	A	`conf`	Identifiability of sparse causal effects using instrumental variables. UAI Niklas Pfister, Jonas Peters
2022	A*	`conf`	Invariant Ancestry Search. ICML Phillip B. Mogensen, Nikolaj Thams, Jonas Peters
2022	J	`jnl`	Invariant Ancestry Search. CoRR Phillip B. Mogensen, Nikolaj Thams, Jonas Peters
2022	J	`jnl`	Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning. CoRR Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee, Dominik Baumann, Oliver Kroemer, Isabelle Guyon, Sebastian Trimpe, Jonas Peters, Niklas Pfister
2022	J	`jnl`	Structure Learning for Directed Trees. J. Mach. Learn. Res. Martin Emil Jakobsen, Rajen Dinesh Shah, Peter Bühlmann, Jonas Peters
2021	J	`jnl`	Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness. J. Cogn. Neurosci. Sebastian Weichwald, Jonas Peters
2021	J	`jnl`	Invariant Policy Learning: A Causal Perspective. CoRR Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, Niklas Pfister
2021	—	`conf`	Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning. NeurIPS (Competition and Demos) Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee, Dominik Baumann, Oliver Kroemer, Isabelle Guyon, Sebastian Trimpe, Jonas Peters, Niklas Pfister
2021	A*	`conf`	Regularizing towards Causal Invariance: Linear Models with Proxies. ICML Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
2021	J	`jnl`	Regularizing towards Causal Invariance: Linear Models with Proxies. CoRR Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
2021	J	`jnl`	Structure Learning for Directed Trees. CoRR Martin Emil Jakobsen, Rajen Dinesh Shah, Peter Bühlmann, Jonas Peters
2020	J	`jnl`	Distributional Robustness of K-class Estimators and the PULSE. CoRR Martin Emil Jakobsen, Jonas Peters
2020	J	`jnl`	Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables. J. Mach. Learn. Res. Rune Christiansen, Jonas Peters
2018	J	`jnl`	Identifying Causal Structure in Large-Scale Kinetic Systems. CoRR Niklas Pfister, Stefan Bauer, Jonas Peters
2018	J	`jnl`	Invariant Models for Causal Transfer Learning. J. Mach. Learn. Res. Mateo Rojas-Carulla, Bernhard Schölkopf, Richard E. Turner, Jonas Peters
2016	J	`jnl`	Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. J. Mach. Learn. Res. Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
2016	J	`jnl`	Modeling confounding by half-sibling regression. Proc. Natl. Acad. Sci. USA Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
2016	J	`jnl`	Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions. CoRR Stephan Bongers, Jonas Peters, Bernhard Schölkopf, Joris M. Mooij
2016	A*	`conf`	The Arrow of Time in Multivariate Time Series. ICML Stefan Bauer, Bernhard Schölkopf, Jonas Peters
2015	—	`conf`	BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions. NIPS Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen
2015	J	`jnl`	Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. CoRR Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters
2015	J	`jnl`	Computing functions of random variables via reproducing kernel Hilbert space representations. Stat. Comput. Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Stefan Harmeling, Jonas Peters
2015	—	`ed.`	Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, The Netherlands, July 16, 2015. ACI@UAI Ricardo Silva, Ilya Shpitser, Robin J. Evans, Jonas Peters, Tom Claassen
2015	A*	`conf`	Removing systematic errors for exoplanet search via latent causes. ICML Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
2015	J	`jnl`	Removing systematic errors for exoplanet search via latent causes. CoRR Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
2015	J	`jnl`	Structural Intervention Distance for Evaluating Causal Graphs. Neural Comput. Jonas Peters, Peter Bühlmann
2014	J	`jnl`	Causal discovery with continuous additive noise models. J. Mach. Learn. Res. Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2014	J	`jnl`	Distinguishing cause from effect using observational data: methods and benchmarks. CoRR Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
2014	—	`ed.`	Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Quebec City, Canada, July 27, 2014. CI@UAI Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen
2013	J	`jnl`	CAM: Causal Additive Models, high-dimensional order search and penalized regression. CoRR Peter Bühlmann, Jonas Peters, Jan Ernest
2013	—	`conf`	Causal Inference on Time Series using Restricted Structural Equation Models. NIPS Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2013	J	`jnl`	Counterfactual reasoning and learning systems: the example of computational advertising. J. Mach. Learn. Res. Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Xavier Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Y. Simard, Ed Snelson
2013	A	`conf`	Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. UAI Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
2013	J	`jnl`	Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. CoRR Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
2013	—	`conf`	Semi-supervised Learning in Causal and Anticausal Settings. Empirical Inference Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
2012	J	`jnl`	Causal Inference on Time Series using Structural Equation Models CoRR Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2012	J	`jnl`	Counterfactual Reasoning and Learning Systems CoRR Léon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Xavier Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Y. Simard, Ed Snelson
2012	J	`jnl`	Detecting low-complexity unobserved causes CoRR Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf
2012	J	`jnl`	Identifiability of Causal Graphs using Functional Models CoRR Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2012	J	`jnl`	Identifying confounders using additive noise models CoRR Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf
2012	J	`jnl`	Kernel-based Conditional Independence Test and Application in Causal Discovery CoRR Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2012	A*	`conf`	On causal and anticausal learning. ICML Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
2011	J	`jnl`	Causal Inference on Discrete Data Using Additive Noise Models. IEEE Trans. Pattern Anal. Mach. Intell. Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2011	A	`conf`	Detecting low-complexity unobserved causes. UAI Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf
2011	A	`conf`	Identifiability of Causal Graphs using Functional Models. UAI Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
2011	A	`conf`	Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2011	J	`jnl`	Robust Learning via Cause-Effect Models CoRR Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Kun Zhang
2010	A	`conf`	Identifying Cause and Effect on Discrete Data using Additive Noise Models. AISTATS Jonas Peters, Dominik Janzing, Bernhard Schölkopf
2009	A*	`conf`	Detecting the direction of causal time series. ICML Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf
2009	A	`conf`	Identifying confounders using additive noise models. UAI Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf
2009	A*	`conf`	Regression by dependence minimization and its application to causal inference in additive noise models. ICML Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
2008	—	`conf`	Kernel Methods for Detecting the Direction of Time Series. GfKl Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf
2008	—	`conf`	Nonlinear causal discovery with additive noise models. NIPS Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf