Statistics, Prediction and Causality for Large-Scale Data

A Look at Robustness and Stability of $\ell_{1}$-versus $\ell_{0}$-Regularization: Discussion of Papers by Bertsimas et al. and Hastie et al.

Autori: Yuansi Chen, Armeen Taeb, Peter Bühlmann
Pubblicato in: Statistical Science, Numero 35/4, 2020, ISSN 0883-4237
Editore: Institute of Mathematical Statistics
DOI: 10.1214/20-sts809

An Almost Constant Lower Bound of the Isoperimetric Coefficient in the KLS Conjecture

Autori: Yuansi Chen
Pubblicato in: Geometric and Functional Analysis, Numero 31/1, 2021, Pagina/e 34-61, ISSN 1016-443X
Editore: Birkhauser Verlag
DOI: 10.1007/s00039-021-00558-4

Ancestor regression in linear structural equation models

Autori: Christoph Schultheiss, Peter Bühlmann
Pubblicato in: Biometrika, 2023, ISSN 0006-3444
Editore: Oxford University Press
DOI: 10.48550/arxiv.2205.08925

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

Autori: 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
Pubblicato in: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13 (1), Numero 13, 2023, Pagina/e e1479, ISSN 1942-4795
Editore: Wiley
DOI: 10.1002/widm.1479

Higher-Order Least Squares: Assessing Partial Goodness of Fit of Linear Causal Models

Autori: Schultheiss, Christoph; Bühlmann, Peter; Yuan, Ming
Pubblicato in: Journal of the American Statistical Association, 2023, ISSN 0162-1459
Editore: American Statistical Association
DOI: 10.1080/01621459.2022.2157728

ricu: R’s interface to intensive care data

Autori: Nicolas Bennett; Drago Plečko; Ida-Fong Ukor; Nicolai Meinshausen; Peter Bühlmann
Pubblicato in: GigaScience, 12, Numero 12, 2023, Pagina/e giad041, ISSN 2047-217X
Editore: Oxford University Press
DOI: 10.1093/gigascience/giad041

The Weighted Generalised Covariance Measure

Autori: Scheidegger, Cyrill; Hörrmann, Julia; Bühlmann, Peter
Pubblicato in: Journal of Machine Learning Research, 2022, Pagina/e 1 - 68, ISSN 1532-4435
Editore: MIT Press
DOI: 10.3929/ethz-b-000580396

Goodness-of-fit testing in high dimensional generalized linear models

Autori: Jana Janková, Rajen D. Shah, Peter Bühlmann, Richard J. Samworth
Pubblicato in: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Numero 82/3, 2020, Pagina/e 773-795, ISSN 1369-7412
Editore: Blackwell Publishing Inc.
DOI: 10.1111/rssb.12371

Structure Learning for Directed Trees

Autori: Jakobsen, Martin Emil; Shah, Rajen D.; Bühlmann, Peter; Peters, Jonas
Pubblicato in: Journal of Machine Learning Research, 23, Numero 3, 2022, Pagina/e 1-97, ISSN 1532-4435
Editore: MIT Press
DOI: 10.48550/arxiv.2108.08871

Toward causality and improving external validity

Autori: Peter Bühlmann
Pubblicato in: Proceedings of the National Academy of Sciences, Numero 117/42, 2020, Pagina/e 25963-25965, ISSN 0027-8424
Editore: National Academy of Sciences
DOI: 10.1073/pnas.2018002117

On the pitfalls of Gaussian likelihood scoring for causal discovery

Autori: Christoph Schultheiss, Peter Bühlmann
Pubblicato in: Journal of Causal Inference, Numero 1, 2023, ISSN 2193-3677
Editore: De Gryter
DOI: 10.48550/arxiv.2210.11104

Confidence and Uncertainty Assessment for Distributional Random Forests

Autori: Näf, Jeffrey; id_orcid0000-0003-0920-1899; Emmenegger, Corinne; id_orcid0000-0003-0353-8888; Bühlmann, Peter; Meinshausen, Nicolai
Pubblicato in: Journal of Machine Learning Research, 24, Numero 24, 2023, Pagina/e 1-77, ISSN 1532-4435
Editore: MIT Press
DOI: 10.48550/arxiv.2302.05761

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression

Autori: Domagoj Cevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen
Pubblicato in: Journal of Machine Learning Research, 2022, Pagina/e 1−79, ISSN 1532-4435
Editore: MIT Press
DOI: 10.3929/ethz-b-000585771

Domain adaptation under structural causal models

Autori: Yuansi Chen, Peter Bühlmann
Pubblicato in: Journal of Machine Learning Research, 2021, Pagina/e 1 - 80, ISSN 1532-4435
Editore: MIT Press
DOI: 10.3929/ethz-b-000520176

Spectral Deconfounding via Perturbed Sparse Linear Models

Autori: Domagoj Ćevid, Peter Bühlmann, Nicolai Meinshausen
Pubblicato in: Journal of Machine Learning Research, 2018, ISSN 1532-4435
Editore: MIT Press
DOI: 10.3929/ethz-b-000459190

Plug-in Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements

Autori: Corinne Emmenegger, Peter Bühlmann
Pubblicato in: Scandinavian Journal of Statistics, 2023, ISSN 0303-6898
Editore: Blackwell Publishing Inc.
DOI: 10.1111/sjos.12639

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

Autori: Pfister, Niklas; Weichwald, Sebastian; Bühlmann, Peter; Schölkopf, Bernhard
Pubblicato in: Journal of Machine Learning Research, 2019, Pagina/e 1-50, ISSN 1532-4435
Editore: MIT Press
DOI: 10.3929/ethz-b-000374036

Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding

Autori: GUO, ZIJIAN; ĆEVID, DOMAGOJ; BÜHLMANN, PETER
Pubblicato in: The Annals of Statistics, 50 (3), Numero 12, 2022, Pagina/e 1320-1347, ISSN 0090-5364
Editore: Institute of Mathematical Statistics
DOI: 10.1214/21-aos2152

Distributional anchor regression

Autori: Lucas Kook; Beate Sick; Peter Bühlmann
Pubblicato in: Statistics and Computing, 32 (3), Numero 3, 2022, Pagina/e 1-19, ISSN 0960-3174
Editore: Kluwer Academic Publishers
DOI: 10.48550/arxiv.2101.08224

Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)

Autori: Kovács, Solt; Li, Housen; Bühlmann, Peter
Pubblicato in: Journal of the Korean Statistical Society, 2020, ISSN 1226-3192
Editore: Elsevier BV

Anchor regression: Heterogeneous Data Meet Causality

Autori: Dominik Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann, Jonas Peters
Pubblicato in: Journal of the Royal Statistical Society, Numero Series B., 2021, ISSN 0964-1998
Editore: Blackwell Publishing Inc.
DOI: 10.1111/rssb.12398

Change point detection for graphical models in presence of missing values

Autori: Londschien, Malte; Kovács, Solt; Bühlmann, Peter
Pubblicato in: Journal of Computational and Graphical Statistics, 2019, ISSN 1061-8600
Editore: American Statistical Association

Seeded binary segmentation: a general methodology for fast and optimal changepoint detection

Autori: Kovács, Solt; Li, Housen; Bühlmann, Peter; Munk, Axel
Pubblicato in: Biometrika, Numero 8, 2023, Pagina/e 249–256, ISSN 0006-3444
Editore: Oxford University Press
DOI: 10.1093/biomet/asac052

Double-estimation-friendly inference for high-dimensional misspecified models

Autori: Rajen D. Shah; Peter Bühlmann
Pubblicato in: Statistical Science, Numero 11, 2023, Pagina/e 68-91, ISSN 0883-4237
Editore: Institute of Mathematical Statistics
DOI: 10.48550/arxiv.1909.10828

Deconfounding and causal regularization for stability and external validity

Autori: Peter Bühlmann, Domagoj Ćevid
Pubblicato in: International Statistical Review, Numero 17515823, 2020, ISSN 1751-5823
Editore: John Wiley & Sons, Inc.
DOI: 10.1111/insr.12426

Rejoinder: Invariance, Causality and Robustness

Autori: Peter Bühlmann
Pubblicato in: Statistical Science, Numero 35/3, 2020, ISSN 0883-4237
Editore: Institute of Mathematical Statistics
DOI: 10.1214/20-sts797

Multicarving for high-dimensional post-selection inference

Autori: Christoph Schultheiss, Claude Renaux, Peter Bühlmann
Pubblicato in: Electronic Journal of Statistics, 2021, ISSN 1935-7524
Editore: Institute of Mathematical Statistics
DOI: 10.1214/21-ejs1825

Springs regarded as hydraulic features and interpreted in the context of basin-scale groundwater flow

Autori: Tóth, Ádám; Kovács, Solt; Kovács, József; Mádl-Szőnyi, Judit
Pubblicato in: Journal of Hydrology, 610, Numero 7, 2022, ISSN 0022-1694
Editore: Elsevier BV
DOI: 10.1016/j.jhydrol.2022.127907

"Discussion of ""A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models"""

Autori: Law, Michael; Bühlmann, Peter
Pubblicato in: Journal of the American Statistical Association, Numero 118(543), 2023, Pagina/e 1578 - 1583, ISSN 0162-1459
Editore: American Statistical Association
DOI: 10.1080/01621459.2023.2231063

Distributionally Robust and Generalizable Inference

Autori: Dominik Rothenhäusler, Peter Bühlmann
Pubblicato in: Statistical Science, Numero 38 (4), 2023, Pagina/e 527-542, ISSN 0883-4237
Editore: Institute of Mathematical Statistics
DOI: 10.1214/23-sts902

Multiomic profiling of the liver across diets and age in a diverse mouse population

Autori: Evan G. Williams; Niklas Pfister; Suheeta Roy; Cyril Statzer; Jack Haverty; Jesse Ingels; Casey E. Bohl; Moaraj Hasan; Jelena Čuklina; Peter Bühlmann; Nicola Zamboni; Lu Lu; Collin Y. Ewald; Robert W. Williams; Ruedi Aebersold; Ruedi Aebersold
Pubblicato in: Cell Systems, 13 (1), Numero 7, 2022, Pagina/e 43-57, ISSN 0020-0255
Editore: Elsevier BV
DOI: 10.1016/j.cels.2021.09.005

Group inference in high dimensions with applications to hierarchical testing

Autori: Guo, Zijian; Renaux, Claude; Bühlmann, Peter; Cai, Tony
Pubblicato in: Electronic Journal of Statistics, 15 (2), Numero 7, 2021, Pagina/e 6633 - 6676, ISSN 1935-7524
Editore: Institute of Mathematical Statistics
DOI: 10.3929/ethz-b-000525120

Random Forests for Change Point Detection

Autori: Londschien, Maltec; Bühlmann, Peter; Kovács, Solt
Pubblicato in: Journal of Machine Learning Research, Numero 24 (216), 2023, ISSN 1532-4435
Editore: MIT Press

One Modern Culture of Statistics: Comments on Statistical Modeling: The Two Cultures (Breiman, 2001b)

Autori: Peter Bühlmann
Pubblicato in: Observational Studies, Numero 7/1, 2021, Pagina/e 33-40, ISSN 2767-3324
Editore: Observational Studies
DOI: 10.1353/obs.2021.0020

Model selection over partially ordered sets

Autori: Taeb, Armeen; Bühlmann, Peter; Chandrasekaran, Venkat
Pubblicato in: Proceedings of the National Academy of Sciences of the United States of America, 121 (8), Numero 121 (8), 2024, Pagina/e e2314228121, ISSN 0027-8424
Editore: National Academy of Sciences
DOI: 10.1073/pnas.2314228121

Identifying cancer pathway dysregulations using differential causal effects

Autori: Kim Philipp Jablonski; Martin Pirkl; Domagoj Ćevid; Peter Bühlmann; Niko Beerenwinkel
Pubblicato in: Bioinformatics, 38 (5), Numero 7, 2022, Pagina/e 1550–1559, ISSN 1367-4803
Editore: Oxford University Press
DOI: 10.3929/ethz-b-000525133

Stabilizing variable selection and regression

Autori: Niklas Pfister, Evan G. Williams, Jonas Peters, Ruedi Aebersold, Peter Bühlmann
Pubblicato in: The Annals of Applied Statistics, Numero 15/3, 2021, Pagina/e 1220–1246, ISSN 1932-6157
Editore: Institute of Mathematical Statistics
DOI: 10.1214/21-aoas1487

Invariance, Causality and Robustness

Autori: Peter Bühlmann
Pubblicato in: Statistical Science, Numero 35/3, 2020, ISSN 0883-4237
Editore: Institute of Mathematical Statistics
DOI: 10.1214/19-sts721

Regularizing Double Machine Learning in Partially Linear Endogenous Models

Autori: Corinne Emmenegger; Peter Bühlmann
Pubblicato in: Electronic Journal of Statistics, 15 (2), Numero 15, 2021, Pagina/e 6461 - 6543, ISSN 1935-7524
Editore: Institute of Mathematical Statistics
DOI: 10.48550/arxiv.2101.12525

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

Autori: Juan L. Gamella, Jonas Peters, Peter Bühlmann
Pubblicato in: Cornell University, 2024, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2404.11341

Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries

Autori: Solt Kovács, Housen Li, Lorenz Haubner, Axel Munk, Peter Bühlmann
Pubblicato in: Cornell Iniversity, 2022, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2010.10194

Assessing the overall and partial causal well-specification of nonlinear additive noise models

Autori: Schultheiss, Christoph; Bühlmann, Peter
Pubblicato in: Cornell University, 2023, ISSN 2768-296X
Editore: Cornell University
DOI: 10.48550/arxiv.2310.16502

Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood

Autori: Armeen Taeb, Parikshit Shah, Venkat Chandrasekaran
Pubblicato in: Cornell University, 2020, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2010.09386

Spectral Deconfounding for High-Dimensional Sparse Additive Models

Autori: Cyrill Scheidegger, Zijian Guo, Peter Bühlmann
Pubblicato in: Cornell University, 2023, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2312.02860

Extrapolation-Aware Nonparametric Statistical Inference

Autori: Niklas Pfister, Peter Bühlmann
Pubblicato in: Cornell University, 2024, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2402.09758

Distributionally Robust Machine Learning with Multi-source Data

Autori: Wang, Zhenyu; Bühlmann, Peter; Guo, Zijian
Pubblicato in: Cornell Iniversity, Numero 16, 2023, ISSN 2768-296X
Editore: Cornell University
DOI: 10.48550/arxiv.2309.02211

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions

Autori: Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml, Peter Bühlmann
Pubblicato in: Cornell University, 2022, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2211.14897

Ancestor regression in structural vector autoregressive models

Autori: Christoph Schultheiss, Peter Bühlmann
Pubblicato in: Cornell University, 2024, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2403.03778

Treatment Effect Estimation with Observational Network Data using Machine Learning

Autori: Emmenegger, Corinne; Spohn, Meta-Lina; Elmer, Timon; Bühlmann, Peter
Pubblicato in: Cornell University, Numero 2, 2022, Pagina/e 2206.14591v2, ISSN 2768-296X
Editore: Cornell University
DOI: 10.48550/arxiv.2206.14591

TSCI: two stage curvature identification for causal inference with invalid instruments

Autori: Carl, David; Emmenegger, Corinne; Bühlmann, Peter; Guo, Zijian
Pubblicato in: Cornell University, Numero 16, 2023, ISSN 2768-296X
Editore: Cornell University
DOI: 10.48550/arxiv.2304.00513

Invariant Probabilistic Prediction

Autori: Henzi, Alexander; Shen, Xinwei; Law, Michael; Bühlmann, Peter
Pubblicato in: Cornell University, Numero 17, 2023, ISSN 2768-296X
Editore: Cornell University
DOI: 10.48550/arxiv.2309.10083

Learning and scoring Gaussian latent variable causal models with unknown additive interventions

Autori: Taeb, Armeen; Gamella, Juan L.; Heinze-Deml, Christina; Bühlmann, Peter
Pubblicato in: Cornell University, 2023, ISSN 2768-296X
Editore: Cornell University
DOI: 10.48550/arxiv.2101.06950

Causality-oriented robustness: exploiting general additive interventions

Autori: Shen, Xinwei; Bühlmann, Peter; Taeb, Armeen
Pubblicato in: Cornell University, 2023, ISSN 2768-296X
Editore: Cornell University
DOI: 10.48550/arxiv.2307.10299

Graphical Elastic Net and Target Matrices: Fast Algorithms and Software for Sparse Precision Matrix Estimation

Autori: Kovács, Solt; Ruckstuhl, Tobias; Obrist, Helena; Bühlmann, Peter
Pubblicato in: Cornell University, 2021, ISSN 2768-296X
Editore: Cornell University
DOI: 10.48550/arxiv.2101.02148

Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning

Autori: Zijian Guo, Mengchu Zheng, Peter Bühlmann
Pubblicato in: Cornell University, 2024, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2203.12808

Distributional Robustness and Transfer Learning Through Empirical Bayes

Autori: Law, Michael; Bühlmann, Peter; Ritov, Ya'acov
Pubblicato in: Cornell University, 2023, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2312.08485

A Rank-Based Sequential Test of Independence

Autori: Henzi, Alexander; Law, Michael
Pubblicato in: Cornell University, Numero 15, 2024, ISSN 2768-296X
Editore: Cornell University
DOI: 10.48550/arxiv.2305.13818

Change point detection algorithms and methodology for large-scale data

Autori: Kovács, Solt
Pubblicato in: Research Collection, 2021
Editore: ETH Zurich
DOI: 10.3929/ethz-b-000505005

Intervention stability in statistics: Benefiting from causality

Autori: Pfister Niklas
Pubblicato in: Research Collection, 2019
Editore: ETH Zurich
DOI: 10.3929/ethz-b-000376157

Confounding Adjustment for Causal Inference

Autori: Ćevid, Domagoj
Pubblicato in: Research Collection, 2021
Editore: ETH Zürich
DOI: 10.3929/ethz-b-000528993

Statistical Machine Learning for Complex Data

Autori: Emmenegger Corinne
Pubblicato in: ETH Zürich Research Collection, 2023
Editore: ETH Zürich
DOI: 10.3929/ethz-b-000615513

Double machine learning methods: Beyond independence

Autori: Corinne Emmenegger, Peter Bühlmann, Meta-Lina Spohn
Pubblicato in: Oberwolfach Report, 2023, Pagina/e 21 - 23
Editore: Mathematisches Forschungsinstitut Oberwolfach
DOI: 10.4171/owr/2022/25

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