Peter Bühlmann
19 papers · 2006–2024 · 2 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+4 more ↓ Show less ↑
🐣 Hot Topic Early Bird 🌍 Conference Polyglot · Bronze 🏃 Academic Marathon · Full 🌉 Interdisciplinary Bridge · Silver 🗺️ Taxonomy Completionist (27)
📈
Trend Setter
💎
Century Club · Bronze
🔥
Unstoppable · Bronze
🗃️
Keyword Collector
(88)
Top co-authors
Research topics
Machine Learning
(15)
Core Methods
(12)
Optimization & Theory
(10)
Artificial Intelligence
(9)
Core AI
(8)
Knowledge & Reasoning
(6)
Reasoning
(6)
Mathematics & Optimization
(4)
Learning Types
(4)
Mathematics
(3)
Data Science & Analytics
(2)
Application Areas
(2)
Bayesian & Probabilistic · Machine Learning
(2)
Methods
(2)
Bayesian & Probabilistic · Artificial Intelligence
(1)
Keywords
causal discovery
(5)
causal inference
(5)
directed acyclic graph
(4)
graphical model
(4)
structure learning
(3)
high-dimensional data
(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
Learning and scoring Gaussian latent variable causal models with unknown additive interventions
JMLR 2024
Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries
JMLR 2024
Confidence and Uncertainty Assessment for Distributional Random Forests
JMLR 2023
On the Identifiability and Estimation of Causal Location-Scale Noise Models
ICML 2023
Random Forests for Change Point Detection
JMLR 2023
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
JMLR 2022
Structure Learning for Directed Trees
JMLR 2022
The Weighted Generalised Covariance Measure
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
High-Dimensional Learning of Linear Causal Networks via Inverse Covariance Estimation
JMLR 2014
Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems
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