Niklas Pfister

According to our database, Niklas Pfister authored at least 16 papers between 2018 and 2024.

Collaborative distances:

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

2024

Effect-Invariant Mechanisms for Policy Generalization.

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

J. Mach. Learn. Res., 2024

Fast Estimation of Partial Dependence Functions using Trees.

Jinyang Liu, Tessa Steensgaard, Marvin N. Wright, Niklas Pfister, Munir Hiabu

CoRR, 2024

Invariant Subspace Decomposition.

Margherita Lazzaretto, Jonas Peters, Niklas Pfister

CoRR, 2024

Identifying Representations for Intervention Extrapolation.

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Invariant Policy Learning: A Causal Perspective.

Sorawit Saengkyongam, Nikolaj Thams, Jonas Peters, Niklas Pfister

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

Supervised learning and model analysis with compositional data.

Shimeng Huang, Elisabeth Ailer, Niki Kilbertus, Niklas Pfister

PLoS Comput. Biol., 2023

Boosted Control Functions.

Nicola Gnecco, Jonas Peters, Sebastian Engelke, Niklas Pfister

CoRR, 2023

2022

Interpreting tree ensemble machine learning models with endoR.

Albane Ruaud, Niklas Pfister, Ruth E. Ley, Nicholas D. Youngblut

PLoS Comput. Biol., December, 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

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

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

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

2019

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise.

Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf

J. Mach. Learn. Res., 2019

2018

Identifying Causal Structure in Large-Scale Kinetic Systems.

Niklas Pfister, Stefan Bauer, Jonas Peters

CoRR, 2018

groupICA: Independent component analysis for grouped data.

Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf

CoRR, 2018