ML Anthology

Peters, Jonas

33 publications

JMLR 2025 Invariant Subspace Decomposition Margherita Lazzaretto, Jonas Peters, Niklas Pfister
ICLRW 2025 Representation Learning for Distributional Perturbation Extrapolation Julius von Kügelgen, Xinwei Shen, Jakob Ketterer, Nicolai Meinshausen, Jonas Peters
NeurIPS 2025 Transferring Causal Effects Using Proxies Manuel Iglesias-Alonso, Felix Schur, Julius von Kügelgen, Jonas Peters
JMLR 2024 Effect-Invariant Mechanisms for Policy Generalization Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters
JMLR 2024 Identifying Causal Effects Using Instrumental Time Series: Nuisance IV and Correcting for the past Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, Jonas Peters
ICLR 2024 Identifying Representations for Intervention Extrapolation Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Kumar Ravikumar, Niklas Pfister, Jonas Peters
NeurIPSW 2023 Identifying Representations for Intervention Extrapolation Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Kumar Ravikumar, Niklas Pfister, Jonas Peters
ICML 2022 Exploiting Independent Instruments: Identification and Distribution Generalization Sorawit Saengkyongam, Leonard Henckel, Niklas Pfister, Jonas Peters
UAI 2022 Identifiability of Sparse Causal Effects Using Instrumental Variables Niklas Pfister, Jonas Peters
ICML 2022 Invariant Ancestry Search Phillip B Mogensen, Nikolaj Thams, Jonas Peters
JMLR 2022 Structure Learning for Directed Trees Martin E. Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters
ICML 2021 Regularizing Towards Causal Invariance: Linear Models with Proxies Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag
JMLR 2020 Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables Rune Christiansen, Jonas Peters
JMLR 2018 Invariant Models for Causal Transfer Learning Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters
JMLR 2016 Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
ICML 2016 The Arrow of Time in Multivariate Time Series Stefan Bauer, Bernhard Schölkopf, Jonas Peters
NeurIPS 2015 BACKSHIFT: Learning Causal Cyclic Graphs from Unknown Shift Interventions Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen
UAI 2015 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 Ricardo Silva, Ilya Shpitser, Robin J. Evans, Jonas Peters, Tom Claassen
ICML 2015 Removing Systematic Errors for Exoplanet Search via Latent Causes Bernhard Schölkopf, David Hogg, Dun Wang, Dan Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
JMLR 2014 Causal Discovery with Continuous Additive Noise Models Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
UAI 2014 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 Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen
NeurIPS 2013 Causal Inference on Time Series Using Restricted Structural Equation Models Jonas Peters, Dominik Janzing, Bernhard Schölkopf
JMLR 2013 Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X. Charles, D. Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson
UAI 2013 Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
ICML 2012 On Causal and Anticausal Learning Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij
UAI 2011 Detecting Low-Complexity Unobserved Causes Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf
UAI 2011 Identifiability of Causal Graphs Using Functional Models Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf
UAI 2011 Kernel-Based Conditional Independence Test and Application in Causal Discovery Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf
AISTATS 2010 Identifying Cause and Effect on Discrete Data Using Additive Noise Models Jonas Peters, Dominik Janzing, Bernhard Schölkopf
ICML 2009 Detecting the Direction of Causal Time Series Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf
UAI 2009 Identifying Confounders Using Additive Noise Models Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf
ICML 2009 Regression by Dependence Minimization and Its Application to Causal Inference in Additive Noise Models Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
NeurIPS 2008 Nonlinear Causal Discovery with Additive Noise Models Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf