Peter Bühlmann

70 catalog papers. Identity-linked publications and additional name matches are distinguished below.

ORCID: 0000-0002-1782-6015

Publications

Distributionally robust learning for multisource unsupervised domain adaptation — The Annals of Statistics 2026 (name-matched, not ORCID-linked)
Causality-Oriented Robustness: Exploiting General Noise Interventions — Journal of the American Statistical Association 2026
Causality pursuit from heterogeneous environments via neural adversarial invariance learning — The Annals of Statistics 2025
Invariant probabilistic prediction — Biometrika 2025
Higher-Order Least Squares: Assessing Partial Goodness of Fit of Linear Causal Models — Journal of the American Statistical Association 2024
Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries — Journal of Machine Learning Research 2024 (name-matched, not ORCID-linked)
Learning and scoring Gaussian latent variable causal models with unknown additive interventions — Journal of Machine Learning Research 2024 (name-matched, not ORCID-linked)
Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models — Journal of Machine Learning Research 2024 (name-matched, not ORCID-linked)
Random Forests for Change Point Detection — Journal of Machine Learning Research 2023 (name-matched, not ORCID-linked)
Seeded binary segmentation: a general methodology for fast and optimal changepoint detection — Biometrika 2023
Ancestor regression in linear structural equation models — Biometrika 2023
Discussion of “A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models” — Journal of the American Statistical Association 2023
Confidence and Uncertainty Assessment for Distributional Random Forests — Journal of Machine Learning Research 2023 (name-matched, not ORCID-linked)
Structure Learning for Directed Trees — Journal of Machine Learning Research 2022 (name-matched, not ORCID-linked)
Doubly debiased lasso: High-dimensional inference under hidden confounding — The Annals of Statistics 2022
The Weighted Generalised Covariance Measure — Journal of Machine Learning Research 2022 (name-matched, not ORCID-linked)
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression — Journal of Machine Learning Research 2022 (name-matched, not ORCID-linked)
Domain adaptation under structural causal models — Journal of Machine Learning Research 2021 (name-matched, not ORCID-linked)
Stabilizing variable selection and regression — The Annals of Applied Statistics 2021
Anchor regression: Heterogeneous data meet causality — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2021
Spectral Deconfounding via Perturbed Sparse Linear Models — Journal of Machine Learning Research 2020 (name-matched, not ORCID-linked)
Goodness-of-fit testing in high dimensional generalized linear models — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2020
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise — Journal of Machine Learning Research 2019 (name-matched, not ORCID-linked)
Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions — The Annals of Statistics 2019
Invariant Causal Prediction for Sequential Data — Journal of the American Statistical Association 2019
Kernel-based tests for joint independence — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2018
Goodness-of-fit tests for high dimensional linear models — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2018
Hierarchical Testing in the High-Dimensional Setting With Correlated Variables — Journal of the American Statistical Association 2016
Causal inference by using invariant prediction: identification and confidence intervals — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2016
Maximin effects in inhomogeneous large-scale data — The Annals of Statistics 2015
Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2015
CAM: Causal additive models, high-dimensional order search and penalized regression — The Annals of Statistics 2014
High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation — Journal of Machine Learning Research 2014 (name-matched, not ORCID-linked)
Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems — Journal of Machine Learning Research 2014 (name-matched, not ORCID-linked)
Identifiability of Gaussian structural equation models with equal error variances — Biometrika 2014
Conditional transformation models — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2014
On asymptotically optimal confidence regions and tests for high-dimensional models — The Annals of Statistics 2014
Discussion: “A significance test for the lasso” — The Annals of Statistics 2014
$\ell_{0}$-penalized maximum likelihood for sparse directed acyclic graphs — The Annals of Statistics 2013
Geometry of the faithfulness assumption in causal inference — The Annals of Statistics 2013
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs — Journal of Machine Learning Research 2012 (name-matched, not ORCID-linked)
Introduction to the Lehmann special section — The Annals of Statistics 2011
Asymptotic optimality of the Westfall–Young permutation procedure for multiple testing under dependence — The Annals of Statistics 2011
Comment — Journal of the American Statistical Association 2011
High-dimensional Covariance Estimation Based On Gaussian Graphical Models — Journal of Machine Learning Research 2011 (name-matched, not ORCID-linked)
Stability selection — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2010
Variable selection in high-dimensional linear models: partially faithful distributions and the pc-simple algorithm — Biometrika 2010
Remembrance of Leo Breiman — The Annals of Applied Statistics 2010
Model-based Boosting 2.0 — Journal of Machine Learning Research 2010 (name-matched, not ORCID-linked)
p-Values for High-Dimensional Regression — Journal of the American Statistical Association 2009
Splines for financial volatility — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2009
Discussion: One-step sparse estimates in nonconcave penalized likelihood models — The Annals of Statistics 2008
Discussion of: Treelets—An adaptive multi-scale basis for sparse unordered data — The Annals of Applied Statistics 2008
The group lasso for logistic regression — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2008
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm — Journal of Machine Learning Research 2007 (name-matched, not ORCID-linked)
Boosting for high-dimensional linear models — The Annals of Statistics 2006
High-dimensional graphs and variable selection with the Lasso — The Annals of Statistics 2006
Sparse Boosting — Journal of Machine Learning Research 2006 (name-matched, not ORCID-linked)
Lower bounds for the number of false null hypotheses for multiple testing of associations under general dependence structures — Biometrika 2005
Discussions of boosting papers, and rejoinders — The Annals of Statistics 2004
Boosting With theL2Loss — Journal of the American Statistical Association 2003
Analyzing bagging — The Annals of Statistics 2002
Sieve Bootstrap With Variable-Length Markov Chains for Stationary Categorical Time Series — Journal of the American Statistical Association 2002
Weak dependence beyond mixing and asymptotics for nonparametric regression — The Annals of Statistics 2002
Tree-structured generalized autoregressive conditional heteroscedastic models — Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2001
Variable length Markov chains — The Annals of Statistics 1999
Comment — Journal of the American Statistical Association 1999
Dynamic adaptive partitioning for nonlinear time series — Biometrika 1999
Sieve bootstrap for smoothing in nonstationary time series — The Annals of Statistics 1998
Blockwise Bootstrapped Empirical Process for Stationary Sequences — The Annals of Statistics 1994