S. Saengkyongam, N. Thams, J. Peters, N. Pfister: Invariant Policy Learning: A causal perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (Accepted).
Preprint ArXiv.
A. Ruaud, N. Pfister, R. Ley, N. Youngblut: Interpreting tree ensemble machine learning models with endoR. PLOS Computational Biology, 18(12), 1-39.
https://doi.org/10.1371/journal.pcbi.1010714, BioRxiv.
S. Saengkyongam, L. Henckel, N. Pfister, J. Peters: Exploiting Independent Instruments: Identification and Distribution Generalization. In Proceedings of the 39th International Conference on Machine Learning (ICML), PMLR 162, 18935-18958.
https://proceedings.mlr.press/v162/saengkyongam22a.html, ArXiv.
J. Peters, S. Bauer, N. Pfister: Causal Models for Dynamical Systems. In Probabilistic and Causal Inference: The Works of Judea Pearl, pp. 671-690.
https://doi.org/10.1145/3501714.3501752, ArXiv.
S. Weichwald and S. W. Mogensen, T. E. Lee, D. Baumann, O. Kroemer, I. Guyon, S. Trimpe, J. Peters, N. Pfister: Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning. Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, PMLR 176, 246-258.
https://proceedings.mlr.press/v176/weichwald22a.html, ArXiv.