A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence. - Mathias Drton; Thomas S. Richardson 001 (UAI, 2003)

Iterative Conditional Fitting for Gaussian Ancestral Graph Models. - Mathias Drton; Thomas S. Richardson 001 (UAI, 2004)

Computing all roots of the likelihood equations of seemingly unrelated regressions. - Mathias Drton (J. Symb. Comput., 2006)

Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models. - Mathias Drton; Thomas S. Richardson 001 (J. Mach. Learn. Res., 2008)

Robust Graphical Modeling with t-Distributions. - Michael Finegold; Mathias Drton (UAI, 2009)

Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors. - Mathias Drton; Michael Eichler; Thomas S. Richardson 001 (J. Mach. Learn. Res., 2009)

Extended Bayesian Information Criteria for Gaussian Graphical Models. - Rina Foygel; Mathias Drton (NIPS, 2010)

On a Parametrization of Positive Semidefinite Matrices with Zeros. - Mathias Drton; Josephine Yu (SIAM J. Matrix Anal. Appl., 2010)

Nonparametric Reduced Rank Regression. - Rina Foygel; Michael Horrell; Mathias Drton; John D. Lafferty (NIPS, 2012)

PC algorithm for nonparanormal graphical models. - Naftali Harris; Mathias Drton (J. Mach. Learn. Res., 2013)

Maximum Likelihood Estimates for Gaussian Mixtures Are Transcendental. - Carlos Améndola; Mathias Drton; Bernd Sturmfels (MACIS, 2015)

Efficient computation of the Bergsma-Dassios sign covariance. - Luca Weihs; Mathias Drton; Dennis Leung (Comput. Stat., 2016)

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

Algebraic tests of general Gaussian latent tree models. - Dennis Leung; Mathias Drton (NeurIPS, 2018)

Generalized Score Matching for Non-Negative Data. - Shiqing Yu; Mathias Drton; Ali Shojaie (J. Mach. Learn. Res., 2019)

Statistical Significance in High-dimensional Linear Mixed Models. - Lina Lin; Mathias Drton; Ali Shojaie (FODS, 2020)

Structure Learning for Cyclic Linear Causal Models. - Carlos Améndola; Philipp Dettling; Mathias Drton; Federica Onori; Jun Wu (UAI, 2020)

Confidence in causal discovery with linear causal models. - David Strieder; Tobias Freidling; Stefan Haffner; Mathias Drton (UAI, 2021)

CorDiffViz: an R package for visualizing multi-omics differential correlation networks. - Shiqing Yu; Mathias Drton; Daniel E. L. Promislow; Ali Shojaie (BMC Bioinform., 2021)

Definite Non-Ancestral Relations and Structure Learning. - Wenyu Chen; Mathias Drton; Ali Shojaie (CoRR, 2021)

Graphical Representations for Algebraic Constraints of Linear Structural Equations Models. - Thijs van Ommen; Mathias Drton (PGM, 2022)

Learning linear non-Gaussian polytree models. - Daniele Tramontano; Anthea Monod; Mathias Drton (UAI, 2022)

Fine-grained network traffic prediction from coarse data. - Krzysztof Rusek; Mathias Drton (CoRR, 2022)

High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data. - Konstantin Göbler; Anne Miloschewski; Mathias Drton; Sach Mukherjee (CoRR, 2022)

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

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

Interaction Models and Generalized Score Matching for Compositional Data. - Shiqing Yu; Mathias Drton; Ali Shojaie (LoG, 2023)

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

causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. - Konstantin Göbler; Tobias Windisch; Tim Pychynski; Steffen Sonntag; Martin Roth; Mathias Drton (CoRR, 2023)

Causal Discovery with Unobserved Confounding and Non-Gaussian Data. - Y. Samuel Wang; Mathias Drton (J. Mach. Learn. Res., 2023)

Learning Linear Gaussian Polytree Models With Interventions. - Daniele Tramontano; L. Waldmann; Mathias Drton; Eliana Duarte (IEEE J. Sel. Areas Inf. Theory, 2023)

Partial Homoscedasticity in Causal Discovery With Linear Models. - Jun Wu; Mathias Drton (IEEE J. Sel. Areas Inf. Theory, 2023)

Causal Structural Learning via Local Graphs. - Wenyu Chen; Mathias Drton; Ali Shojaie (SIAM J. Math. Data Sci., 2023)

Identifiability in Continuous Lyapunov Models. - Philipp Dettling; Roser Homs; Carlos Améndola; Mathias Drton; Niels Richard Hansen (SIAM J. Matrix Anal. Appl., 2023)

On the Lasso for Graphical Continuous Lyapunov Models. - Philipp Dettling; Mathias Drton; Mladen Kolar (CLeaR, 2024)

extttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. - Konstantin Göbler; Tobias Windisch; Mathias Drton; Tim Pychynski; Martin Roth; Steffen Sonntag (CLeaR, 2024)

Dual Likelihood for Causal Inference under Structure Uncertainty. - David Strieder; Mathias Drton (CLeaR, 2024)

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

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

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding. - Daniela Schkoda; Elina Robeva; Mathias Drton (CoRR, 2024)

Robust Score Matching. - Richard Schwank; Andrew McCormack; Mathias Drton (AISTATS, 2025)

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

Nonlinear Causal Discovery for Grouped Data. - Konstantin Göbler; Tobias Windisch; Mathias Drton (UAI, 2025)

Identifiability of homoscedastic linear structural equation models using algebraic matroids. - Mathias Drton; Benjamin Hollering; Jun Wu (Adv. Appl. Math., 2025)

Identifying total causal effects in linear models under partial homoscedasticity. - David Strieder; Mathias Drton (Int. J. Approx. Reason., 2025)

Algebraic Sparse Factor Analysis. - Mathias Drton; Alexandros Grosdos; Irem Portakal; Nils Sturma (SIAM J. Appl. Algebra Geom., 2025)

Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants. - Daniele Tramontano; Yaroslav Kivva; Saber Salehkaleybar; Mathias Drton; Negar Kiyavash (CoRR, 2025)