Peter Bühlmann

Orcid: 0000-0002-1782-6015

Affiliations:
  • ETH Zürich, Switzerland


According to our database1, Peter Bühlmann authored at least 56 papers between 2003 and 2024.

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Bibliography

2024
Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models.
J. Mach. Learn. Res., 2024

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning.
CoRR, 2024

The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology.
CoRR, 2024

2023
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., 2023

Confidence and Uncertainty Assessment for Distributional Random Forests.
J. Mach. Learn. Res., 2023

Random Forests for Change Point Detection.
J. Mach. Learn. Res., 2023

Invariant Probabilistic Prediction.
CoRR, 2023

Distributionally Robust Machine Learning with Multi-source Data.
CoRR, 2023

Causality-oriented robustness: exploiting general additive interventions.
CoRR, 2023

repliclust: Synthetic Data for Cluster Analysis.
CoRR, 2023

On the Identifiability and Estimation of Causal Location-Scale Noise Models.
Proceedings of the International Conference on Machine Learning, 2023

2022
Distributional anchor regression.
Stat. Comput., 2022

The Weighted Generalised Covariance Measure.
J. Mach. Learn. Res., 2022

Structure Learning for Directed Trees.
J. Mach. Learn. Res., 2022

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression.
J. Mach. Learn. Res., 2022

Identifying cancer pathway dysregulations using differential causal effects.
Bioinform., 2022

2021
Domain adaptation under structural causal models.
J. Mach. Learn. Res., 2021

Change-Point Detection for Graphical Models in the Presence of Missing Values.
J. Comput. Graph. Stat., 2021

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning.
CoRR, 2021

2020
Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software.
Comput. Stat., 2020

Hierarchical inference for genome-wide association studies: a view on methodology with software.
Comput. Stat., 2020

Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries.
CoRR, 2020

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression.
CoRR, 2020

SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research.
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.
J. Mach. Learn. Res., 2019

2018
groupICA: Independent component analysis for grouped data.
CoRR, 2018

2016
Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data.
Proc. IEEE, 2016

Assessing statistical significance in multivariable genome wide association analysis.
Bioinform., 2016

2015
Structural Intervention Distance for Evaluating Causal Graphs.
Neural Comput., 2015

2014
Pattern alternating maximization algorithm for missing data in high-dimensional problems.
J. Mach. Learn. Res., 2014

High-dimensional learning of linear causal networks via inverse covariance estimation.
J. Mach. Learn. Res., 2014

Two optimal strategies for active learning of causal models from interventional data.
Int. J. Approx. Reason., 2014

Hypersurfaces and Their Singularities in Partial Correlation Testing.
Found. Comput. Math., 2014

High-dimensional variable screening and bias in subsequent inference, with an empirical comparison.
Comput. Stat., 2014

2013
Causal statistical inference in high dimensions.
Math. Methods Oper. Res., 2013

Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables.
Comput. Stat. Data Anal., 2013

CAM: Causal Additive Models, high-dimensional order search and penalized regression.
CoRR, 2013

2012
Missing values: sparse inverse covariance estimation and an extension to sparse regression.
Stat. Comput., 2012

Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs.
J. Mach. Learn. Res., 2012

Two Optimal Strategies for Active Learning of Causal Models from Interventions
CoRR, 2012

Causal stability ranking.
Bioinform., 2012

MissForest - non-parametric missing value imputation for mixed-type data.
Bioinform., 2012

2011
High-dimensional Covariance Estimation Based On Gaussian Graphical Models.
J. Mach. Learn. Res., 2011

Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract).
Proceedings of the UAI 2011, 2011

2010
Twin Boosting: improved feature selection and prediction.
Stat. Comput., 2010

Model-based Boosting 2.0.
J. Mach. Learn. Res., 2010

2008
Robustified L<sub>2</sub> boosting.
Comput. Stat. Data Anal., 2008

Annotating novel genes by integrating synthetic lethals and genomic information.
BMC Syst. Biol., 2008

2007
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm.
J. Mach. Learn. Res., 2007

Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries.
BMC Bioinform., 2007

Analyzing gene expression data in terms of gene sets: methodological issues.
Bioinform., 2007

2006
Sparse Boosting.
J. Mach. Learn. Res., 2006

A systematic comparison and evaluation of biclustering methods for gene expression data.
Bioinform., 2006

Model-based boosting in high dimensions.
Bioinform., 2006

2005
Boosting and l<sup>1</sup>-Penalty Methods for High-dimensional Data with Some Applications in Genomics.
Proceedings of the From Data and Information Analysis to Knowledge Engineering, 2005

2003
Boosting for Tumor Classification with Gene Expression Data.
Bioinform., 2003


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