Drton, Mathias

30 publications

UAI 2025 Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Mathias Drton, Marina Garrote-López, Niko Nikov, Elina Robeva, Y. Samuel Wang

ICML 2025 Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Negar Kiyavash, Mathias Drton

UAI 2025 Nonlinear Causal Discovery for Grouped Data Konstantin Göbler, Tobias Windisch, Mathias Drton

AISTATS 2025 Robust Score Matching Richard Schwank, Andrew McCormack, Mathias Drton

CLeaR 2024 $\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Martin Roth, Steffen Sonntag

ICML 2024 Causal Effect Identification in LiNGAM Models with Latent Confounders Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash

CLeaR 2024 Dual Likelihood for Causal Inference Under Structure Uncertainty David Strieder, Mathias Drton

PGM 2024 Identifying Total Causal Effects in Linear Models Under Partial Homoscedasticity David Strieder, Mathias Drton

PGM 2024 Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton

CLeaR 2024 On the Lasso for Graphical Continuous Lyapunov Models Philipp Dettling, Mathias Drton, Mladen Kolar

JMLR 2023 Causal Discovery with Unobserved Confounding and Non-Gaussian Data Y. Samuel Wang, Mathias Drton

CLeaR 2023 Directed Graphical Models and Causal Discovery for Zero-Inflated Data Shiqing Yu, Mathias Drton, Ali Shojaie

LoG 2023 Interaction Models and Generalized Score Matching for Compositional Data Shiqing Yu, Mathias Drton, Ali Shojaie

AISTATS 2023 Rank-Based Causal Discovery for Post-Nonlinear Models Grigor Keropyan, David Strieder, Mathias Drton

NeurIPS 2023 Unpaired Multi-Domain Causal Representation Learning Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler

PGM 2022 Graphical Representations for Algebraic Constraints of Linear Structural Equations Models Thijs Ommen, Mathias Drton

UAI 2022 Learning Linear Non-Gaussian Polytree Models Daniele Tramontano, Anthea Monod, Mathias Drton

UAI 2021 Confidence in Causal Discovery with Linear Causal Models David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton

UAI 2020 Structure Learning for Cyclic Linear Causal Models Carlos Amendola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu

JMLR 2019 Generalized Score Matching for Non-Negative Data Shiqing Yu, Mathias Drton, Ali Shojaie

NeurIPS 2018 Algebraic Tests of General Gaussian Latent Tree Models Dennis Leung, Mathias Drton

AISTATS 2018 Graphical Models for Non-Negative Data Using Generalized Score Matching Shiqing Yu, Mathias Drton, Ali Shojaie

JMLR 2013 PC Algorithm for Nonparanormal Graphical Models Naftali Harris, Mathias Drton

NeurIPS 2012 Nonparametric Reduced Rank Regression Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty

NeurIPS 2010 Extended Bayesian Information Criteria for Gaussian Graphical Models Rina Foygel, Mathias Drton

JMLR 2009 Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors Mathias Drton, Michael Eichler, Thomas S. Richardson

UAI 2009 Robust Graphical Modeling with T-Distributions Michael Finegold, Mathias Drton

JMLR 2008 Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models Mathias Drton, Thomas S. Richardson

UAI 2004 Iterative Conditional Fitting for Gaussian Ancestral Graph Models Mathias Drton, Thomas S. Richardson

UAI 2003 A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence Mathias Drton, Thomas S. Richardson