Peter Bühlmann
Full Professor
Researches high-dimensional statistics and causal inference, including the lasso, stability selection and invariance-based causal learning.
Peter Bühlmann's lab at ETH Zurich works on learning theory & optimization, probabilistic & causal inference and efficient training & inference. 38 papers since 2023, 5 at top venues, mostly JMLR and ICML.
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 13%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 9 papers2026 · T1 0 · collecting
- 9 papers2025 · T1 1
- 6 papers2024 · T1 1
- 14 papers2023 · T1 3
T1 venuesOtherVenues: JMLR, ICML, Nature Machine Intelligence
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference14 papers
- Learning theory & optimization9 papers
- Efficient training & inference6 papers
- causal6 papers
- stability0 papers
Paper list
From the lab homepage.
20269 papers
- Journal of the American Statistical Association 121, 704-715. Download.Causality-oriented robustness: exploiting general noise interventions (opens in a new tab)
- Annals of Statistics 54, 570-596. Chosen as invited poster presentation at NeurIPS .Distributionally robust learning for multisource unsupervised domain adaptation (opens in a new tab)
- RSS Data Science and Artificial Intelligence 2, udag001, 1-31.Domain generalization and adaptation in intensive care with anchor regression (opens in a new tab)
- Journal of Machine Learning Research 27 (150), 1-59.Extrapolation-aware nonparametric statistical inference (opens in a new tab)
- Electronic Journal of Statistics 20, 718-770.Inference for heterogeneous treatment effects with efficient instruments and machine learning (opens in a new tab)
- Journal of Machine Learning Research 27 (198), 1-67.Robustness against weak or invalid instruments: exploring nonlinear treatment models with machine learning (opens in a new tab)
- Journal of Computational and Graphical Statistics 35, 758-768. DownloadSpectrally deconfounded Random Forests (opens in a new tab)
- Statistical Learning and Data Science (SLADS) 1, 100006, 1-6.Statistics in the next quarter-century: playing also in the frontyard? (opens in a new tab)
- Journal of Econometrics 256, Part A, 106239, 1-31.Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets (opens in a new tab)
20259 papers
- Critical Care and Resuscitation 27 (1) 100105, 1-7.A framework and analytical exploration for a data-driven update of the Sequential Organ Failure Assessment (SOFA) score in sepsis (opens in a new tab)
- Journal of Causal Inference 13 (1) 20240011, 1-25.Ancestor regression in structural vector autoregressive models (opens in a new tab)
- Nature Machine Intelligence (T1)Causal Chambers as a real-world physical testbed for AI methodology (opens in a new tab)
- Annals of Statistics 53, 2230-2257.Causality pursuit from heterogeneous environments via neural adversarial invariance learning (opens in a new tab)
- Biometrika 112 (1), asae063 https://doi.org/10.1093/biomet/asae063, 1-22.Invariant probabilistic prediction (opens in a new tab)
- Journal of Classification, https://doi.org/10.1007/s00357-025-09501-w, 1-27.Natural language-based synthetic data generation for cluster analysis (opens in a new tab)
- ACM/IMS Journal of Data Science 2 (1), 1-52.Spectral deconfounding for high-dimensional sparse additive models (opens in a new tab)
- Journal of Causal Inference 13 (1) 20230082, 1-36.Treatment effect estimation with observational network data using machine learning (opens in a new tab)
- Journal of Statistical Software 114, doi: 10.18637/jss.v114.i07, 1-21.TSCI: two stage curvature identification for causal inference with invalid instruments in R (opens in a new tab)
20246 papers
- Cell Genomics 4 (11) 100691, 1-16.AI-empowered perturbation proteomics for complex biological systems (opens in a new tab)
- Journal of the American Statistical Association 119, 1019-1031.Assessing the goodness of fit of linear regression via higher-order least squares (opens in a new tab)
- JMLR (T1)Assessing the overall and partial causal well-specification of nonlinear additive noise models (opens in a new tab)
- Journal of Machine Learning Research 25 (293): 1-68.Learning and scoring Gaussian latent variable causal models with unknown additive interventions (opens in a new tab)
- Proceedings of the National Academy of Sciences USAModel selection over partially ordered sets (opens in a new tab)
- Journal of Machine Learning Research 25 (297): 1-64.Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries (opens in a new tab)
202314 papers
- Biometrika 110, 1117-1124.Ancestor regression in linear structural equation models (opens in a new tab)
- JMLR (T1)Confidence and uncertainty assessment for distributional Random Forests (opens in a new tab)
- Journal of the American Statistical Association 118, 1578-1583.Discussion of “A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models (Dai, Lin, Xing and Liu)” (opens in a new tab)
- WIREs Data Mining and Knowledge Discovery 13, e1479, 1-22. DownloadDistributional regression modeling via generalized additive models for location, scale, and shape: An overview through a dataset from learning analytics (opens in a new tab)
- Statistical ScienceDistributionally robust and generalizable inference (opens in a new tab)
- Statistical Science 38, 68-91.Double-estimation-friendly inference for high-dimensional misspecified models (opens in a new tab)
- ICML (T1)On the identifiability and estimation of causal location-scale noise models (opens in a new tab)
- Journal of Causal Inference 11(1), jci- -0068, 1-11.On the pitfalls of Gaussian likelihood scoring for causal discovery (opens in a new tab)
- Scandinavian Journal of Statistics 50, 1553-1567.Plug-in machine learning for partially linear mixed-effects models with repeated measurements (opens in a new tab)
- eClinicalMedicine 62, Article 103124, 1-13.Predicting sepsis using deep learning across international sites: a retrospective development and validation study (opens in a new tab)
- JMLR (T1)Random Forests for change point detection (opens in a new tab)
- GigaScience 12, giad041, 1-8.ricu: R's interface to intensive care data (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
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