Peter Bühlmann

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 30

Top Topics

Physical Systems

Top Venues

J. Mach. Learn. Res.

Publications

Juan L. Gamella, Jonas Peters, Peter Bühlmann
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology. CoRR (2024)
Yihong Gu, Cong Fang, Peter Bühlmann, Jianqing Fan
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning. CoRR (2024)
Xinwei Shen, Peter Bühlmann, Armeen Taeb
Causality-oriented robustness: exploiting general additive interventions. CoRR (2023)
Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
On the Identifiability and Estimation of Causal Location-Scale Noise Models. ICML (2023)
Zhenyu Wang, Peter Bühlmann, Zijian Guo
Distributionally Robust Machine Learning with Multi-source Data. CoRR (2023)
Fernando Marmolejo-Ramos, Mauricio Tejo, Marek Brabec, Jakub Kuzilek, Srecko Joksimovic, Vitomir Kovanovic, Jorge González, Thomas Kneib, Peter Bühlmann, Lucas Kook, Guillermo Briseño-Sánchez, Raydonal Ospina
Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics. WIREs Data Mining Knowl. Discov. 13 (1) (2023)
Malte Londschien, Peter Bühlmann, Solt Kovács
Random Forests for Change Point Detection. J. Mach. Learn. Res. 24 (2023)
Alexander Henzi, Xinwei Shen, Michael Law, Peter Bühlmann
Invariant Probabilistic Prediction. CoRR (2023)
Michael J. Zellinger, Peter Bühlmann
repliclust: Synthetic Data for Cluster Analysis. CoRR (2023)
Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
On the Identifiability and Estimation of Causal Location-Scale Noise Models. CoRR (2022)
Martin Emil Jakobsen, Rajen Dinesh Shah, Peter Bühlmann, Jonas Peters
Structure Learning for Directed Trees. J. Mach. Learn. Res. 23 (2022)
Lucas Kook, Beate Sick, Peter Bühlmann
Distributional anchor regression. Stat. Comput. 32 (3) (2022)
Domagoj Cevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. J. Mach. Learn. Res. 23 (2022)
Kim Philipp Jablonski, Martin Pirkl, Domagoj Cevid, Peter Bühlmann, Niko Beerenwinkel
Identifying cancer pathway dysregulations using differential causal effects. Bioinform. 38 (6) (2022)
Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann
The Weighted Generalised Covariance Measure. J. Mach. Learn. Res. 23 (2022)
Martin Emil Jakobsen, Rajen D. Shah, Peter Bühlmann, Jonas Peters
Structure Learning for Directed Trees. CoRR (2021)
Michael Moor, Nicolas Bennett, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten M. Borgwardt
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning. CoRR (2021)
Malte Londschien, Solt Kovács, Peter Bühlmann
Change-Point Detection for Graphical Models in the Presence of Missing Values. J. Comput. Graph. Stat. 30 (3) (2021)
Yuansi Chen, Peter Bühlmann
Domain adaptation under structural causal models. J. Mach. Learn. Res. 22 (2021)
Claude Renaux, Laura Buzdugan, Markus Kalisch, Peter Bühlmann
Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software. Comput. Stat. 35 (1) (2020)
Yuansi Chen, Peter Bühlmann
Domain adaptation under structural causal models. CoRR (2020)
Solt Kovács, Housen Li, Lorenz Haubner, Axel Munk, Peter Bühlmann
Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries. CoRR (2020)
Domagoj Cevid, Loris Michel, Nicolai Meinshausen, Peter Bühlmann
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. CoRR (2020)
Adrian Egli, Manuel Battegay, Andrea C. Büchler, Peter Bühlmann, Thierry Calandra, Philippe Eckert, Hansjakob Furrer, Gilbert Greub, Stephan M. Jakob, Laurent Kaiser, Stephen L. Leib, Stephan Marsch, Nicolai Meinshausen, Jean-Luc Pagani, Jerome Pugin, Gunnar Rätsch, Jacques Schrenzel, Reto Schüpbach, Martin Siegemund, Nicola Zamboni, Reinhard Zbinden, Annelies Zinkernagel, Karsten M. Borgwardt
SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. MIE (2020)
Claude Renaux, Laura Buzdugan, Markus Kalisch, Peter Bühlmann
Hierarchical inference for genome-wide association studies: a view on methodology with software. Comput. Stat. 35 (1) (2020)
Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise. J. Mach. Learn. Res. 20 (2019)
Malte Londschien, Solt Kovács, Peter Bühlmann
Change point detection for graphical models in presence of missing values. CoRR (2019)
Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf
groupICA: Independent component analysis for grouped data. CoRR (2018)
Peter Bühlmann, Nicolai Meinshausen
Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data. Proc. IEEE 104 (1) (2016)
Laura Buzdugan, Markus Kalisch, Arcadi Navarro, Daniel Schunk, Ernst Fehr, Peter Bühlmann
Assessing statistical significance in multivariable genome wide association analysis. Bioinform. 32 (13) (2016)
Jonas Peters, Peter Bühlmann
Structural Intervention Distance for Evaluating Causal Graphs. Neural Comput. 27 (3) (2015)
Peter Bühlmann, Jacopo Mandozzi
High-dimensional variable screening and bias in subsequent inference, with an empirical comparison. Comput. Stat. 29 (3) (2014)
Po-Ling Loh, Peter Bühlmann
High-dimensional learning of linear causal networks via inverse covariance estimation. J. Mach. Learn. Res. 15 (1) (2014)
Shaowei Lin, Caroline Uhler, Bernd Sturmfels, Peter Bühlmann
Hypersurfaces and Their Singularities in Partial Correlation Testing. Found. Comput. Math. 14 (5) (2014)
Nicolas Städler, Daniel J. Stekhoven, Peter Bühlmann
Pattern alternating maximization algorithm for missing data in high-dimensional problems. J. Mach. Learn. Res. 15 (1) (2014)
Alain Hauser, Peter Bühlmann
Two optimal strategies for active learning of causal models from interventional data. Int. J. Approx. Reason. 55 (4) (2014)
Peter Bühlmann, Jonas Peters, Jan Ernest
CAM: Causal Additive Models, high-dimensional order search and penalized regression. CoRR (2013)
Peter Bühlmann
Causal statistical inference in high dimensions. Math. Methods Oper. Res. 77 (3) (2013)
Bernd Fellinghauer, Peter Bühlmann, Martin Ryffel, Michael von Rhein, Jan D. Reinhardt
Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables. Comput. Stat. Data Anal. 64 (2013)
Alain Hauser, Peter Bühlmann
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. J. Mach. Learn. Res. 13 (2012)
Daniel J. Stekhoven, Peter Bühlmann
MissForest - non-parametric missing value imputation for mixed-type data. Bioinform. 28 (1) (2012)
Nicolas Städler, Peter Bühlmann
Missing values: sparse inverse covariance estimation and an extension to sparse regression. Stat. Comput. 22 (1) (2012)
Daniel J. Stekhoven, Izabel Moraes, Gardar Sveinbjörnsson, Lars Hennig, Marloes H. Maathuis, Peter Bühlmann
Causal stability ranking. Bioinform. 28 (21) (2012)
Alain Hauser, Peter Bühlmann
Two Optimal Strategies for Active Learning of Causal Models from Interventions CoRR (2012)
Alain Hauser, Peter Bühlmann
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract). UAI (2011)
Alain Hauser, Peter Bühlmann
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs CoRR (2011)
Shuheng Zhou, Philipp Rütimann, Min Xu, Peter Bühlmann
High-dimensional Covariance Estimation Based On Gaussian Graphical Models. J. Mach. Learn. Res. 12 (2011)
Peter Bühlmann, Torsten Hothorn
Twin Boosting: improved feature selection and prediction. Stat. Comput. 20 (2) (2010)
Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner
Model-based Boosting 2.0. J. Mach. Learn. Res. 11 (2010)
Roman Werner Lutz, Markus Kalisch, Peter Bühlmann
boosting. Comput. Stat. Data Anal. 52 (7) (2008)
Daniel Schöner, Markus Kalisch, Christian Leisner, Lukas Meier, Marc Sohrmann, Mahamadou Faty, Yves Barral, Matthias Peter, Wilhelm Gruissem, Peter Bühlmann
Annotating novel genes by integrating synthetic lethals and genomic information. BMC Syst. Biol. 2 (2008)
Corinne Dahinden, Giovanni Parmigiani, Mark C. Emerick, Peter Bühlmann
Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries. BMC Bioinform. 8 (2007)
Jelle J. Goeman, Peter Bühlmann
Analyzing gene expression data in terms of gene sets: methodological issues. Bioinform. 23 (8) (2007)
Markus Kalisch, Peter Bühlmann
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm. J. Mach. Learn. Res. 8 (2007)
Peter Bühlmann, Bin Yu
Sparse Boosting. J. Mach. Learn. Res. 7 (2006)
Amela Prelic, Stefan Bleuler, Philip Zimmermann, Anja Wille, Peter Bühlmann, Wilhelm Gruissem, Lars Hennig, Lothar Thiele, Eckart Zitzler
A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinform. 22 (9) (2006)
Torsten Hothorn, Peter Bühlmann
Model-based boosting in high dimensions. Bioinform. 22 (22) (2006)
Peter Bühlmann
-Penalty Methods for High-dimensional Data with Some Applications in Genomics. GfKl (2005)
Marcel Dettling, Peter Bühlmann
Boosting for Tumor Classification with Gene Expression Data. Bioinform. 19 (9) (2003)