Peter Bühlmann

Orcid: 0000-0002-1782-6015

Affiliations:

ETH Zürich, Switzerland

According to our database¹, Peter Bühlmann authored at least 52 papers between 2003 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2023

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

[BibT_eX]

[DOI]

Fernando Marmolejo-Ramos

Guillermo Briseño-Sánchez

Raydonal Ospina

WIREs Data Mining Knowl. Discov., 2023

Random Forests for Change Point Detection.

[BibT_eX]

[DOI]

Malte Londschien

Peter Bühlmann

Solt Kovács

J. Mach. Learn. Res., 2023

Invariant Probabilistic Prediction.

[BibT_eX]

[DOI]

CoRR, 2023

Distributionally Robust Machine Learning with Multi-source Data.

[BibT_eX]

[DOI]

Zhenyu Wang

Peter Bühlmann

Zijian Guo

CoRR, 2023

Causality-oriented robustness: exploiting general additive interventions.

[BibT_eX]

[DOI]

Xinwei Shen

Peter Bühlmann

Armeen Taeb

CoRR, 2023

repliclust: Synthetic Data for Cluster Analysis.

[BibT_eX]

[DOI]

Michael J. Zellinger

Peter Bühlmann

CoRR, 2023

On the Identifiability and Estimation of Causal Location-Scale Noise Models.

[BibT_eX]

[DOI]

Alexander Immer

Christoph Schultheiss

Proceedings of the International Conference on Machine Learning, 2023

2022

Distributional anchor regression.

[BibT_eX]

[DOI]

Lucas Kook

Beate Sick

Peter Bühlmann

Stat. Comput., 2022

The Weighted Generalised Covariance Measure.

[BibT_eX]

[DOI]

Cyrill Scheidegger

Julia Hörrmann

Peter Bühlmann

J. Mach. Learn. Res., 2022

Structure Learning for Directed Trees.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Identifying cancer pathway dysregulations using differential causal effects.

[BibT_eX]

[DOI]

Kim Philipp Jablonski

Bioinform., 2022

2021

Domain adaptation under structural causal models.

[BibT_eX]

[DOI]

Yuansi Chen

Peter Bühlmann

J. Mach. Learn. Res., 2021

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

[BibT_eX]

[DOI]

Malte Londschien

Solt Kovács

Peter Bühlmann

J. Comput. Graph. Stat., 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

2020

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

[BibT_eX]

[DOI]

Comput. Stat., 2020

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

[BibT_eX]

[DOI]

Comput. Stat., 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2019

2018

groupICA: Independent component analysis for grouped data.

[BibT_eX]

[DOI]

CoRR, 2018

2016

Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data.

[BibT_eX]

[DOI]

Peter Bühlmann

Nicolai Meinshausen

Proc. IEEE, 2016

Assessing statistical significance in multivariable genome wide association analysis.

[BibT_eX]

[DOI]

Bioinform., 2016

2015

Structural Intervention Distance for Evaluating Causal Graphs.

[BibT_eX]

[DOI]

Jonas Peters

Peter Bühlmann

Neural Comput., 2015

2014

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

[BibT_eX]

[DOI]

Nicolas Städler

Daniel J. Stekhoven

Peter Bühlmann

J. Mach. Learn. Res., 2014

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

[BibT_eX]

[DOI]

Po-Ling Loh

Peter Bühlmann

J. Mach. Learn. Res., 2014

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

[BibT_eX]

[DOI]

Alain Hauser

Peter Bühlmann

Int. J. Approx. Reason., 2014

Hypersurfaces and Their Singularities in Partial Correlation Testing.

[BibT_eX]

[DOI]

Found. Comput. Math., 2014

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

[BibT_eX]

[DOI]

Peter Bühlmann

Jacopo Mandozzi

Comput. Stat., 2014

2013

Causal statistical inference in high dimensions.

[BibT_eX]

[DOI]

Peter Bühlmann

Math. Methods Oper. Res., 2013

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

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2013

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

[BibT_eX]

[DOI]

Peter Bühlmann

Jonas Peters

Jan Ernest

CoRR, 2013

2012

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

[BibT_eX]

[DOI]

Nicolas Städler

Peter Bühlmann

Stat. Comput., 2012

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

[BibT_eX]

[DOI]

Alain Hauser

Peter Bühlmann

J. Mach. Learn. Res., 2012

Two Optimal Strategies for Active Learning of Causal Models from Interventions

[BibT_eX]

[DOI]

Alain Hauser

Peter Bühlmann

CoRR, 2012

Causal stability ranking.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Daniel J. Stekhoven

Peter Bühlmann

Bioinform., 2012

2011

High-dimensional Covariance Estimation Based On Gaussian Graphical Models.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2011

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

[BibT_eX]

[DOI]

Alain Hauser

Peter Bühlmann

Proceedings of the UAI 2011, 2011

2010

Twin Boosting: improved feature selection and prediction.

[BibT_eX]

[DOI]

Peter Bühlmann

Torsten Hothorn

Stat. Comput., 2010

Model-based Boosting 2.0.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2010

2008

Robustified L<sub>2</sub> boosting.

[BibT_eX]

[DOI]

Roman Werner Lutz

Markus Kalisch

Peter Bühlmann

Comput. Stat. Data Anal., 2008

Annotating novel genes by integrating synthetic lethals and genomic information.

[BibT_eX]

[DOI]

BMC Syst. Biol., 2008

2007

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

[BibT_eX]

[DOI]

Markus Kalisch

Peter Bühlmann

J. Mach. Learn. Res., 2007

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

[BibT_eX]

[DOI]

BMC Bioinform., 2007

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

[BibT_eX]

[DOI]

Jelle J. Goeman

Peter Bühlmann

Bioinform., 2007

2006

Sparse Boosting.

[BibT_eX]

[DOI]

Peter Bühlmann

Bin Yu

J. Mach. Learn. Res., 2006

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

[BibT_eX]

[DOI]

Bioinform., 2006

Model-based boosting in high dimensions.

[BibT_eX]

[DOI]

Torsten Hothorn

Peter Bühlmann

Bioinform., 2006

2005

Boosting and l<sup>1</sup>-Penalty Methods for High-dimensional Data with Some Applications in Genomics.

[BibT_eX]

[DOI]

Peter Bühlmann

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

2003

Boosting for Tumor Classification with Gene Expression Data.

[BibT_eX]

[DOI]

Marcel Dettling

Peter Bühlmann

Bioinform., 2003

Peter Bühlmann

Timeline

Legend:

Links

Online presence:

On csauthors.net:

Bibliography

Loading...