Peter Bühlmann

According to our database, Peter Bühlmann authored at least 56 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models.

Christoph Schultheiss, Peter Bühlmann

J. Mach. Learn. Res., 2024

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning.

Yihong Gu, Cong Fang, Peter Bühlmann, Jianqing Fan

CoRR, 2024

The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology.

Juan L. Gamella, Jonas Peters, Peter Bühlmann

CoRR, 2024

2023

Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics.

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

WIREs Data Mining Knowl. Discov., 2023

Confidence and Uncertainty Assessment for Distributional Random Forests.

Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann, Nicolai Meinshausen

J. Mach. Learn. Res., 2023

Random Forests for Change Point Detection.

Malte Londschien, Peter Bühlmann, Solt Kovács

J. Mach. Learn. Res., 2023

Invariant Probabilistic Prediction.

Alexander Henzi, Xinwei Shen, Michael Law, Peter Bühlmann

CoRR, 2023

Distributionally Robust Machine Learning with Multi-source Data.

Zhenyu Wang, Peter Bühlmann, Zijian Guo

CoRR, 2023

Causality-oriented robustness: exploiting general additive interventions.

Xinwei Shen, Peter Bühlmann, Armeen Taeb

CoRR, 2023

repliclust: Synthetic Data for Cluster Analysis.

Michael J. Zellinger, Peter Bühlmann

CoRR, 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

Proceedings of the International Conference on Machine Learning, 2023

2022

Distributional anchor regression.

Lucas Kook, Beate Sick, Peter Bühlmann

Stat. Comput., 2022

The Weighted Generalised Covariance Measure.

Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann

J. Mach. Learn. Res., 2022

Structure Learning for Directed Trees.

Martin Emil Jakobsen, Rajen Dinesh Shah, Peter Bühlmann, Jonas Peters

J. Mach. Learn. Res., 2022

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression.

Domagoj Cevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen

J. Mach. Learn. Res., 2022

Identifying cancer pathway dysregulations using differential causal effects.

Kim Philipp Jablonski, Martin Pirkl, Domagoj Cevid, Peter Bühlmann, Niko Beerenwinkel

Bioinform., 2022

2021

Domain adaptation under structural causal models.

Yuansi Chen, Peter Bühlmann

J. Mach. Learn. Res., 2021

Change-Point Detection for Graphical Models in the Presence of Missing Values.

Malte Londschien, Solt Kovács, Peter Bühlmann

J. Comput. Graph. Stat., 2021

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning.

Michael Moor, Nicolas Bennett, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten M. Borgwardt

CoRR, 2021

2020

Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software.

Claude Renaux, Laura Buzdugan, Markus Kalisch, Peter Bühlmann

Comput. Stat., 2020

Hierarchical inference for genome-wide association studies: a view on methodology with software.

Claude Renaux, Laura Buzdugan, Markus Kalisch, Peter Bühlmann

Comput. Stat., 2020

Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries.

Solt Kovács, Housen Li, Lorenz Haubner, Axel Munk, Peter Bühlmann

CoRR, 2020

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression.

Domagoj Cevid, Loris Michel, Nicolai Meinshausen, Peter Bühlmann

CoRR, 2020

SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research.

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

Proceedings of the Digital Personalized Health and Medicine - Proceedings of MIE 2020, Medical Informatics Europe, Geneva, Switzerland, April 28, 2020

2019

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise.

Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf

J. Mach. Learn. Res., 2019

2018

groupICA: Independent component analysis for grouped data.

Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf

CoRR, 2018

2016

Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data.

Peter Bühlmann, Nicolai Meinshausen

Proc. IEEE, 2016

Assessing statistical significance in multivariable genome wide association analysis.

Laura Buzdugan, Markus Kalisch, Arcadi Navarro, Daniel Schunk, Ernst Fehr, Peter Bühlmann

Bioinform., 2016

2015

Structural Intervention Distance for Evaluating Causal Graphs.

Jonas Peters, Peter Bühlmann

Neural Comput., 2015

2014

Pattern alternating maximization algorithm for missing data in high-dimensional problems.

Nicolas Städler, Daniel J. Stekhoven, Peter Bühlmann

J. Mach. Learn. Res., 2014

High-dimensional learning of linear causal networks via inverse covariance estimation.

Po-Ling Loh, Peter Bühlmann

J. Mach. Learn. Res., 2014

Two optimal strategies for active learning of causal models from interventional data.

Alain Hauser, Peter Bühlmann

Int. J. Approx. Reason., 2014

Hypersurfaces and Their Singularities in Partial Correlation Testing.

Shaowei Lin, Caroline Uhler, Bernd Sturmfels, Peter Bühlmann

Found. Comput. Math., 2014

High-dimensional variable screening and bias in subsequent inference, with an empirical comparison.

Peter Bühlmann, Jacopo Mandozzi

Comput. Stat., 2014

2013

Causal statistical inference in high dimensions.

Peter Bühlmann

Math. Methods Oper. Res., 2013

Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables.

Bernd Fellinghauer, Peter Bühlmann, Martin Ryffel, Michael von Rhein, Jan D. Reinhardt

Comput. Stat. Data Anal., 2013

CAM: Causal Additive Models, high-dimensional order search and penalized regression.

Peter Bühlmann, Jonas Peters, Jan Ernest

CoRR, 2013

2012

Missing values: sparse inverse covariance estimation and an extension to sparse regression.

Nicolas Städler, Peter Bühlmann

Stat. Comput., 2012

Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs.

Alain Hauser, Peter Bühlmann

J. Mach. Learn. Res., 2012

Two Optimal Strategies for Active Learning of Causal Models from Interventions

Alain Hauser, Peter Bühlmann

CoRR, 2012

Causal stability ranking.

Daniel J. Stekhoven, Izabel Moraes, Gardar Sveinbjörnsson, Lars Hennig, Marloes H. Maathuis, Peter Bühlmann

Bioinform., 2012

MissForest - non-parametric missing value imputation for mixed-type data.

Daniel J. Stekhoven, Peter Bühlmann

Bioinform., 2012

2011

High-dimensional Covariance Estimation Based On Gaussian Graphical Models.

Shuheng Zhou, Philipp Rütimann, Min Xu, Peter Bühlmann

J. Mach. Learn. Res., 2011

Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract).

Alain Hauser, Peter Bühlmann

Proceedings of the UAI 2011, 2011

2010

Twin Boosting: improved feature selection and prediction.

Peter Bühlmann, Torsten Hothorn

Stat. Comput., 2010

Model-based Boosting 2.0.

Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner

J. Mach. Learn. Res., 2010

2008

Robustified L₂ boosting.

Roman Werner Lutz, Markus Kalisch, Peter Bühlmann

Comput. Stat. Data Anal., 2008

Annotating novel genes by integrating synthetic lethals and genomic information.

Daniel Schöner, Markus Kalisch, Christian Leisner, Lukas Meier, Marc Sohrmann, Mahamadou Faty, Yves Barral, Matthias Peter, Wilhelm Gruissem, Peter Bühlmann

BMC Syst. Biol., 2008

2007

Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm.

Markus Kalisch, Peter Bühlmann

J. Mach. Learn. Res., 2007

Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries.

Corinne Dahinden, Giovanni Parmigiani, Mark C. Emerick, Peter Bühlmann

BMC Bioinform., 2007

Analyzing gene expression data in terms of gene sets: methodological issues.

Jelle J. Goeman, Peter Bühlmann

Bioinform., 2007

2006

Sparse Boosting.

Peter Bühlmann, Bin Yu

J. Mach. Learn. Res., 2006

A systematic comparison and evaluation of biclustering methods for gene expression data.

Amela Prelic, Stefan Bleuler, Philip Zimmermann, Anja Wille, Peter Bühlmann, Wilhelm Gruissem, Lars Hennig, Lothar Thiele, Eckart Zitzler

Bioinform., 2006

Model-based boosting in high dimensions.

Torsten Hothorn, Peter Bühlmann

Bioinform., 2006

2005

Boosting and l¹-Penalty Methods for High-dimensional Data with Some Applications in Genomics.

Peter Bühlmann

Proceedings of the From Data and Information Analysis to Knowledge Engineering, 2005

2003

Boosting for Tumor Classification with Gene Expression Data.

Marcel Dettling, Peter Bühlmann

Bioinform., 2003