Bühlmann, Peter

20 publications

JMLR 2024 Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models Christoph Schultheiss, Peter Bühlmann

JMLR 2024 Learning and Scoring Gaussian Latent Variable Causal Models with Unknown Additive Interventions Armeen Taeb, Juan L. Gamella, Christina Heinze-Deml, Peter Bühlmann

JMLR 2024 Optimistic Search: Change Point Estimation for Large-Scale Data via Adaptive Logarithmic Queries Solt Kovács, Housen Li, Lorenz Haubner, Axel Munk, Peter Bühlmann

JMLR 2023 Confidence and Uncertainty Assessment for Distributional Random Forests Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann, Nicolai Meinshausen

ICML 2023 On the Identifiability and Estimation of Causal Location-Scale Noise Models Alexander Immer, Christoph Schultheiss, Julia E Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx

JMLR 2023 Random Forests for Change Point Detection Malte Londschien, Peter Bühlmann, Solt Kovács

JMLR 2022 Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression Domagoj Cevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen

JMLR 2022 Structure Learning for Directed Trees Martin E. Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters

JMLR 2022 The Weighted Generalised Covariance Measure Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann

JMLR 2021 Domain Adaptation Under Structural Causal Models Yuansi Chen, Peter Bühlmann

JMLR 2020 Spectral Deconfounding via Perturbed Sparse Linear Models Domagoj Ćevid, Peter Bühlmann, Nicolai Meinshausen

JMLR 2019 Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf

JMLR 2014 High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation Po-Ling Loh, Peter Bühlmann

JMLR 2014 Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems Nicolas Städler, Daniel J. Stekhoven, Peter Bühlmann

JMLR 2012 Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs Alain Hauser, Peter Bühlmann

UAI 2011 Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract) Alain Hauser, Peter Bühlmann

JMLR 2011 High-Dimensional Covariance Estimation Based on Gaussian Graphical Models Shuheng Zhou, Philipp Rütimann, Min Xu, Peter Bühlmann

MLOSS 2010 Model-Based Boosting 2.0 Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner

JMLR 2007 Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm Markus Kalisch, Peter Bühlmann

JMLR 2006 Sparse Boosting Peter Bühlmann, Bin Yu