L. Kook, N. Pfister: Instrumental Variable Estimation of Distributional Causal Effects.
Preprint ArXiv.
S. Huang, J. Peters, N. Pfister: Causal Change Point Detection and Localization.
Preprint ArXiv.
N. Pfister, P. Bühlmann: Extrapolation-Aware Nonparametric Statistical Inference.
Preprint ArXiv.
A. R. Lundborg, N. Pfister: Perturbation-based Effect Measures for Compositional Data.
Preprint ArXiv.
N. Gnecco, J. Peters, S. Engelke, N. Pfister: Boosted Control Functions.
Preprint ArXiv.
N. Pfister, V. Volhejn, M. Knott, S. Arias, J. Bazińska, M. Bichurin, A. Commike, J. Darling, P. Dienes, M. Fiedler, D. Haber, M. Kraft, M. Lancini, M. Mathys, D. Pascual-Ortiz, J. Podolak, A. Romero-López, K. Shiarlis, A. Signer, Z. Terek, A. Theocharis, D. Timbrell, S. Trautwein, S. Watts, N. Wu, M. Rojas-Carulla: Gandalf the Red: Adaptive Security for LLMs. In Proceedings of the 42nd International Conference on Machine Learning (accepted).
Preprint ArXiv.
J. Liu, T. Steensgaard, M. N. Wright, N. Pfister, M. Hiabu: Fast Estimation of Partial Dependence Functions using Trees. In Proceedings of the 42nd International Conference on Machine Learning (accepted).
Preprint ArXiv.
M. Lazzaretto, J. Peters, N. Pfister: Invariant Subspace Decomposition. Journal of Machine Learning Research (accepted).
Preprint ArXiv.
S. Huang, N. Pfister, J. Bowden: Sparse Causal Effect Estimation using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding. In Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (accepted).
Preprint ArXiv.
S. Saengkyongam, E. Rosenfeld, P. Ravikumar, N. Pfister, J. Peters: Identifying Representations for Intervention Extrapolation. In Proceedings of the 12th International Conference on Learning Representations.
ArXiv.
S. Saengkyongam, N. Pfister, P. Klasnja, S. Murphy, J. Peters: Effect-Invariant Mechanisms for Policy Generalization. Journal of Machine Learning Research, 25(34), 1-36.
https://jmlr.org/papers/volume25/23-0802/23-0802.pdf, ArXiv.
S. Huang, E. Ailer, N. Kilbertus, N. Pfister: Supervised Learning and Model Analysis with Compositional Data. PLOS Computational Biology, 19(6), 1-19.
https://doi.org/10.1371/journal.pcbi.1011240, ArXiv.
N. Thams, S. Saengkyongam, N. Pfister, J. Peters: Statistical Testing under Distributional Shifts. Journal of the Royal Statistical Society: Series B, 85(3), 597-663.
https://doi.org/10.1093/jrsssb/qkad018, ArXiv.
S. Saengkyongam, N. Thams, J. Peters, N. Pfister: Invariant Policy Learning: A causal perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(7), 8606-8620.
https://doi.org/10.1109/TPAMI.2022.3232363, 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.