Preprints

• 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.
  Preprint ArXiv.

• S. Huang, N. Pfister, J. Bowden: Sparse Causal Effect Estimation using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding.
  Preprint ArXiv.

• J. Liu, T. Steensgaard, M. N. Wright, N. Pfister, M. Hiabu: Fast Estimation of Partial Dependence Functions using Trees.
  Preprint ArXiv.

• L. Kook, N. Pfister: Instrumental Variable Estimation of Distributional Causal Effects.
  Preprint ArXiv.

• M. Lazzaretto, J. Peters, N. Pfister: Invariant Subspace Decomposition.
  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.

2024

• 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 (accepted).
  Preprint 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.

2023

• 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.

2022

• 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.

• N. Pfister, J. Peters: Identifiability of Sparse Causal Effects using Instrumental Variables. In Proceedings of the 38th Annual Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 180, 1613-1622.
  https://proceedings.mlr.press/v180/pfister22a.html, ArXiv.
  • Supplementary code

• 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.

• R. Christiansen, N. Pfister, M. Jakobsen, N. Gnecco, J. Peters: A Causal Framework for Distribution Generalization. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44(10), 6614–6630.
  https://doi.org/10.1109/TPAMI.2021.3094760, ArXiv.

2021

• N. Pfister, E. Williams, R. Aebersold, P. Bühlmann: Stabilizing Variable Selection and Regression. Annals of Applied Statistics, 15(3), 1220-1246.
  https://doi.org/10.1214/21-AOAS1487, ArXiv.
  • Supplementary code
• E. G. Williams, N. Pfister, S. Roy, C. Statzer, J. Haverty, J. Ingels, C. Bohl, M. Hasan, J. Cuklina, P. Bühlmann, N. Zamboni, L. Lu, C. Y. Ewald, R. W. Williams, R. Aebersold: Multi-Omic Profiling of the Liver Across Diets and Age in a Diverse Mouse Population. Cell Systems, 13(1), 43-57.
  https://doi.org/10.1016/j.cels.2021.09.005, BioRxiv.

2019

• N. Pfister, S. Bauer, J. Peters: Learning Stable and Predictive Structures in Kinetic Systems. Proceedings of the National Academy of Sciences (PNAS), 116(51), 25405-25411.
  https://doi.org/10.1073/pnas.1905688116, ArXiv.
  • Project website
• N. Pfister, S. Weichwald, P. Bühlmann, B. Schölkopf: Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise. Journal of Machine Learning Research, 20(147), 1-50.
  http://www.jmlr.org/papers/volume20/18-399/18-399.pdf, ArXiv.
  • Project website
• N. Pfister, P. Bühlmann, J. Peters: Invariant Causal Prediction for Sequential Data. Journal of the American Statistical Association, 114(527), 1264-1276.
  https://www.tandfonline.com/doi/full/10.1080/01621459.2018.1491403, ArXiv.

2018

• N. Pfister, P. Bühlmann, B. Schölkopf, J. Peters: Kernel-based Tests for Joint Independence. Journal of the Royal Statistical Society: Series B, 80(1), 5-31.
  http://onlinelibrary.wiley.com/doi/10.1111/rssb.12235/full, ArXiv.
  • Supplementary code

Theses

• Master Thesis: Joint Independence Testing, ETH Zurich 2016. This version includes minor corrections.
• Semester Thesis: A Literature Review of High Order ODE Solvers, ETH Zurich 2015.
• Bachelor Thesis: Duality and Utility Maximization, ETH Zurich 2013.