| Kernel-based tests for joint independence N Pfister, P Bühlmann, B Schölkopf, J Peters Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2018 | 160 | 2018 |
| Invariant causal prediction for sequential data N Pfister, P Bühlmann, J Peters Journal of the American Statistical Association 114 (527), 1264-1276, 2019 | 76 | 2019 |
| A causal framework for distribution generalization R Christiansen, N Pfister, ME Jakobsen, N Gnecco, J Peters IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 | 35* | 2021 |
| Learning stable and predictive structures in kinetic systems N Pfister, S Bauer, J Peters Proceedings of the National Academy of Sciences 116 (51), 25405-25411, 2019 | 35* | 2019 |
| Stabilizing variable selection and regression N Pfister, EG Williams, J Peters, R Aebersold, P Bühlmann The Annals of Applied Statistics 15 (3), 1220-1246, 2021 | 28 | 2021 |
| Causal models for dynamical systems J Peters, S Bauer, N Pfister Probabilistic and Causal Inference: The Works of Judea Pearl, 671-690, 2022 | 24 | 2022 |
| Robustifying independent component analysis by adjusting for group-wise stationary noise N Pfister, S Weichwald, P Bühlmann, B Schölkopf Journal of Machine Learning Research 20 (147), 1-50, 2019 | 19 | 2019 |
| Multiomic profiling of the liver across diets and age in a diverse mouse population EG Williams, N Pfister, S Roy, C Statzer, J Haverty, J Ingels, C Bohl, ... Cell Systems 13 (1), 43-57. e6, 2022 | 18* | 2022 |
| dHSIC: Independence testing via Hilbert Schmidt independence criterion N Pfister, J Peters R Package version 2, 2017 | 12 | 2017 |
| Statistical testing under distributional shifts N Thams, S Saengkyongam, N Pfister, J Peters arXiv preprint arXiv:2105.10821, 2021 | 6 | 2021 |
| dHSIC: Independence Testing via Hilbert Schmidt Independence Criterion, 2017 N Pfister, J Peters URL https://CRAN. R-project. org/package= dHSIC. R package version 2, 1, 0 | 6 | |
| Learning by doing: Controlling a dynamical system using causality, control, and reinforcement learning S Weichwald, SW Mogensen, TE Lee, D Baumann, O Kroemer, I Guyon, ... NeurIPS 2021 Competitions and Demonstrations Track, 246-258, 2022 | 5 | 2022 |
| Exploiting independent instruments: Identification and distribution generalization S Saengkyongam, L Henckel, N Pfister, J Peters International Conference on Machine Learning, 18935-18958, 2022 | 5 | 2022 |
| Invariant policy learning: A causal perspective S Saengkyongam, N Thams, J Peters, N Pfister IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 | 4 | 2023 |
| Identifiability of sparse causal effects using instrumental variables N Pfister, J Peters Uncertainty in Artificial Intelligence, 1613-1622, 2022 | 2 | 2022 |
| Joint independence testing N Pfister Master's Thesis, 2016 | 1 | 2016 |
| Lecture notes–Statistics B N Pfister | | 2023 |
| Interpreting tree ensemble machine learning models with endoR A Ruaud, N Pfister, RE Ley, ND Youngblut PLOS Computational Biology 18 (12), e1010714, 2022 | | 2022 |
| Package ‘groupICA’ N Pfister, S Weichwald, MN Pfister | | 2022 |
| Supervised Learning and Model Analysis with Compositional Data S Huang, E Ailer, N Kilbertus, N Pfister arXiv preprint arXiv:2205.07271, 2022 | | 2022 |
Jonas PetersProfessor of Statistics, ETH ZurichVerified email at stat.math.ethz.ch
