| 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 | 225 | 2018 |
| Invariant causal prediction for sequential data N Pfister, P Bühlmann, J Peters Journal of the American Statistical Association 114 (527), 1264-1276, 2019 | 141 | 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 | 86 | 2021 |
| Causal models for dynamical systems J Peters, S Bauer, N Pfister Probabilistic and Causal Inference: The Works of Judea Pearl, 671-690, 2022 | 53 | 2022 |
| 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 | 50 | 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 | 45* | 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 | 40 | 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 | 25 | 2019 |
| Invariant policy learning: A causal perspective S Saengkyongam, N Thams, J Peters, N Pfister IEEE transactions on pattern analysis and machine intelligence 45 (7), 8606-8620, 2023 | 21 | 2023 |
| Exploiting independent instruments: Identification and distribution generalization S Saengkyongam, L Henckel, N Pfister, J Peters International Conference on Machine Learning, 18935-18958, 2022 | 21 | 2022 |
| dHSIC: Independence testing via Hilbert Schmidt independence criterion N Pfister, J Peters R Package version 2, 2017 | 18 | 2017 |
| Identifying representations for intervention extrapolation S Saengkyongam, E Rosenfeld, P Ravikumar, N Pfister, J Peters arXiv preprint arXiv:2310.04295, 2023 | 16 | 2023 |
| Statistical testing under distributional shifts N Thams, S Saengkyongam, N Pfister, J Peters Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023 | 13 | 2023 |
| 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 | 10 | 2022 |
| 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, 424, 0 | 10 | |
| Supervised learning and model analysis with compositional data S Huang, E Ailer, N Kilbertus, N Pfister PLOS Computational Biology 19 (6), e1011240, 2023 | 9 | 2023 |
| Identifiability of sparse causal effects using instrumental variables N Pfister, J Peters Uncertainty in Artificial Intelligence, 1613-1622, 2022 | 9 | 2022 |
| Interpreting tree ensemble machine learning models with endoR A Ruaud, N Pfister, RE Ley, ND Youngblut PLOS Computational Biology 18 (12), e1010714, 2022 | 6 | 2022 |
| Extrapolation-aware nonparametric statistical inference N Pfister, P Bühlmann arXiv preprint arXiv:2402.09758, 2024 | 4 | 2024 |
| Perturbation-based Effect Measures for Compositional Data A Rask Lundborg, N Pfister arXiv e-prints, arXiv: 2311.18501, 2023 | 4* | 2023 |
Jonas PetersProfessor of Statistics, ETH Zurich確認したメール アドレス: stat.math.ethz.ch
