Niklas Pfister
Niklas Pfister
Assistant Professor, University of Copenhagen
Verified email at - Homepage
Cited by
Cited by
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
Invariant causal prediction for sequential data
N Pfister, P Bühlmann, J Peters
Journal of the American Statistical Association 114 (527), 1264-1276, 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
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
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
Causal models for dynamical systems
J Peters, S Bauer, N Pfister
Probabilistic and Causal Inference: The Works of Judea Pearl, 671-690, 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
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
dHSIC: Independence testing via Hilbert Schmidt independence criterion
N Pfister, J Peters
R Package version 2, 2017
Statistical testing under distributional shifts
N Thams, S Saengkyongam, N Pfister, J Peters
arXiv preprint arXiv:2105.10821, 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
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
Exploiting independent instruments: Identification and distribution generalization
S Saengkyongam, L Henckel, N Pfister, J Peters
International Conference on Machine Learning, 18935-18958, 2022
Invariant policy learning: A causal perspective
S Saengkyongam, N Thams, J Peters, N Pfister
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Identifiability of sparse causal effects using instrumental variables
N Pfister, J Peters
Uncertainty in Artificial Intelligence, 1613-1622, 2022
Joint independence testing
N Pfister
Master's Thesis, 2016
Lecture notes–Statistics B
N Pfister
Interpreting tree ensemble machine learning models with endoR
A Ruaud, N Pfister, RE Ley, ND Youngblut
PLOS Computational Biology 18 (12), e1010714, 2022
Package ‘groupICA’
N Pfister, S Weichwald, MN Pfister
Supervised Learning and Model Analysis with Compositional Data
S Huang, E Ailer, N Kilbertus, N Pfister
arXiv preprint arXiv:2205.07271, 2022
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