Jonas Peters

Orcid: 0000-0002-1487-7511

According to our database1, Jonas Peters authored at least 50 papers between 2008 and 2024.

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Bibliography

2024
Effect-Invariant Mechanisms for Policy Generalization.
J. Mach. Learn. Res., 2024

DecoR: Deconfounding Time Series with Robust Regression.
CoRR, 2024

The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology.
CoRR, 2024

Invariant Subspace Decomposition.
CoRR, 2024

Identifying Representations for Intervention Extrapolation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Invariant Policy Learning: A Causal Perspective.
IEEE Trans. Pattern Anal. Mach. Intell., July, 2023

Boosted Control Functions.
CoRR, 2023

Unfair Utilities and First Steps Towards Improving Them.
CoRR, 2023

2022
A Causal Framework for Distribution Generalization.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Structure Learning for Directed Trees.
J. Mach. Learn. Res., 2022

Identifiability of sparse causal effects using instrumental variables.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Exploiting Independent Instruments: Identification and Distribution Generalization.
Proceedings of the International Conference on Machine Learning, 2022

Invariant Ancestry Search.
Proceedings of the International Conference on Machine Learning, 2022

Causal Models for Dynamical Systems.
Proceedings of the Probabilistic and Causal Inference: The Works of Judea Pearl, 2022

2021
Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness.
J. Cogn. Neurosci., 2021

Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning.
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Regularizing towards Causal Invariance: Linear Models with Proxies.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables.
J. Mach. Learn. Res., 2020

Distributional Robustness of K-class Estimators and the PULSE.
CoRR, 2020

2018
Invariant Models for Causal Transfer Learning.
J. Mach. Learn. Res., 2018

Identifying Causal Structure in Large-Scale Kinetic Systems.
CoRR, 2018

2016
Modeling confounding by half-sibling regression.
Proc. Natl. Acad. Sci. USA, 2016

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks.
J. Mach. Learn. Res., 2016

Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions.
CoRR, 2016

The Arrow of Time in Multivariate Time Series.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Computing functions of random variables via reproducing kernel Hilbert space representations.
Stat. Comput., 2015

Structural Intervention Distance for Evaluating Causal Graphs.
Neural Comput., 2015

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations.
CoRR, 2015

BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions.
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.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Causal discovery with continuous additive noise models.
J. Mach. Learn. Res., 2014

2013
Counterfactual reasoning and learning systems: the example of computational advertising.
J. Mach. Learn. Res., 2013

CAM: Causal Additive Models, high-dimensional order search and penalized regression.
CoRR, 2013

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Causal Inference on Time Series using Restricted Structural Equation Models.
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.
Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2012
Counterfactual Reasoning and Learning Systems
CoRR, 2012

Causal Inference on Time Series using Structural Equation Models
CoRR, 2012

On causal and anticausal learning.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Causal Inference on Discrete Data Using Additive Noise Models.
IEEE Trans. Pattern Anal. Mach. Intell., 2011

Robust Learning via Cause-Effect Models
CoRR, 2011

Kernel-based Conditional Independence Test and Application in Causal Discovery.
Proceedings of the UAI 2011, 2011

Identifiability of Causal Graphs using Functional Models.
Proceedings of the UAI 2011, 2011

Detecting low-complexity unobserved causes.
Proceedings of the UAI 2011, 2011

2010
Identifying Cause and Effect on Discrete Data using Additive Noise Models.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

2009
Identifying confounders using additive noise models.
Proceedings of the UAI 2009, 2009

Detecting the direction of causal time series.
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.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Nonlinear causal discovery with additive noise models.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Kernel Methods for Detecting the Direction of Time Series.
Proceedings of the Advances in Data Analysis, Data Handling and Business Intelligence, 2008


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