← Authors

Peter Bühlmann

19 papers · 2006–2024 · 2 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (5) 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (2) 🏃 Academic Marathon (18) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (4) 🗺️ Taxonomy Completionist (13) 🧭 Keyword Pioneer 📈 Trend Setter 🔥 Unstoppable (6) 🗃️ Keyword Collector (88) 💎 Century Club (19)

Conferences

JMLR (18) ICML (1)

Top co-authors

Nicolai Meinshausen (3) Bernhard Schölkopf (2) Christoph Schultheiss (2) Jeffrey Näf (2) Solt Kovács (2) Domagoj Ćevid (2) Philipp Rütimann (1) Nicolas Städler (1) Yuansi Chen (1) Martin E. Jakobsen (1)

Keywords

causal discovery (5) causal inference (5) directed acyclic graph (4) graphical model (4) structure learning (3) high-dimensional datum (3) conditional distribution (3) change point detection (2) heteroscedastic noise (2) random forest (2) probabilistic modeling (1) em algorithm (1) graph theory (1) statistical inference (1) model misspecification (1) sparse learning (1) variable selection (1) causal structure learning (1) high-dimensional statistics (1) sparse estimation (1)

Papers

Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models JMLR 2024
Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries JMLR 2024
Learning and scoring Gaussian latent variable causal models with unknown additive interventions JMLR 2024
On the Identifiability and Estimation of Causal Location-Scale Noise Models ICML 2023
Random Forests for Change Point Detection JMLR 2023
Confidence and Uncertainty Assessment for Distributional Random Forests JMLR 2023
The Weighted Generalised Covariance Measure JMLR 2022
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression JMLR 2022
Structure Learning for Directed Trees JMLR 2022
Domain adaptation under structural causal models JMLR 2021
Spectral Deconfounding via Perturbed Sparse Linear Models JMLR 2020
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise JMLR 2019
Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems JMLR 2014
High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation JMLR 2014
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs JMLR 2012
High-dimensional Covariance Estimation Based On Gaussian Graphical Models JMLR 2011
Model-based Boosting 2.0 JMLR 2010
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm JMLR 2007
Sparse Boosting JMLR 2006