Mathias Drton

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 33

Top Topics

Graphical Representations

Maximum Likelihood Estimates

Structure Learning

Top Venues

Publications

Philipp Dettling, Mathias Drton, Mladen Kolar
On the Lasso for Graphical Continuous Lyapunov Models. CLeaR (2024)
David Strieder, Mathias Drton
Dual Likelihood for Causal Inference under Structure Uncertainty. CLeaR (2024)
Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash
Causal Effect Identification in LiNGAM Models with Latent Confounders. CoRR (2024)
Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Martin Roth, Steffen Sonntag
extttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. CLeaR (2024)
Daniele Tramontano, L. Waldmann, Mathias Drton, E. Duarte
Learning Linear Gaussian Polytree Models with Interventions. CoRR (2023)
Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler
Unpaired Multi-Domain Causal Representation Learning. CoRR (2023)
Jun Wu, Mathias Drton
Partial Homoscedasticity in Causal Discovery With Linear Models. IEEE J. Sel. Areas Inf. Theory 4 (2023)
Daniele Tramontano, L. Waldmann, Mathias Drton, Eliana Duarte
Learning Linear Gaussian Polytree Models With Interventions. IEEE J. Sel. Areas Inf. Theory 4 (2023)
Shiqing Yu, Mathias Drton, Ali Shojaie
Directed Graphical Models and Causal Discovery for Zero-Inflated Data. CLeaR (2023)
Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler
Unpaired Multi-Domain Causal Representation Learning. NeurIPS (2023)
Konstantin Göbler, Tobias Windisch, Tim Pychynski, Steffen Sonntag, Martin Roth, Mathias Drton
causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. CoRR (2023)
Grigor Keropyan, David Strieder, Mathias Drton
Rank-Based Causal Discovery for Post-Nonlinear Models. CoRR (2023)
Philipp Dettling, Roser Homs, Carlos Améndola, Mathias Drton, Niels Richard Hansen
Identifiability in Continuous Lyapunov Models. SIAM J. Matrix Anal. Appl. 44 (4) (2023)
Wenyu Chen, Mathias Drton, Ali Shojaie
Causal Structural Learning via Local Graphs. SIAM J. Math. Data Sci. 5 (2) (2023)
Grigor Keropyan, David Strieder, Mathias Drton
Rank-Based Causal Discovery for Post-Nonlinear Models. AISTATS (2023)
Shiqing Yu, Mathias Drton, Ali Shojaie
Interaction Models and Generalized Score Matching for Compositional Data. LoG (2023)
Daniele Tramontano, Anthea Monod, Mathias Drton
Learning linear non-Gaussian polytree models. UAI (2022)
Krzysztof Rusek, Mathias Drton
Fine-grained network traffic prediction from coarse data. CoRR (2022)
Daniele Tramontano, Anthea Monod, Mathias Drton
Learning Linear Non-Gaussian Polytree Models. CoRR (2022)
Thijs van Ommen, Mathias Drton
Graphical Representations for Algebraic Constraints of Linear Structural Equations Models. PGM (2022)
Konstantin Göbler, Anne Miloschewski, Mathias Drton, Sach Mukherjee
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data. CoRR (2022)
Thijs van Ommen, Mathias Drton
Graphical Representations for Algebraic Constraints of Linear Structural Equations Models. CoRR (2022)
Wenyu Chen, Mathias Drton, Ali Shojaie
Definite Non-Ancestral Relations and Structure Learning. CoRR (2021)
David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton
Confidence in causal discovery with linear causal models. UAI (2021)
Shiqing Yu, Mathias Drton, Daniel E. L. Promislow, Ali Shojaie
CorDiffViz: an R package for visualizing multi-omics differential correlation networks. BMC Bioinform. 22 (1) (2021)
Carlos Améndola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu
Structure Learning for Cyclic Linear Causal Models. UAI (2020)
Lina Lin, Mathias Drton, Ali Shojaie
Statistical Significance in High-dimensional Linear Mixed Models. FODS (2020)
Shiqing Yu, Mathias Drton, Ali Shojaie
Generalized Score Matching for Non-Negative Data. J. Mach. Learn. Res. 20 (2019)
Dennis Leung, Mathias Drton
Algebraic tests of general Gaussian latent tree models. NeurIPS (2018)
Shiqing Yu, Mathias Drton, Ali Shojaie
Generalized Score Matching for Non-Negative Data. CoRR (2018)
Shiqing Yu, Mathias Drton, Ali Shojaie
Graphical Models for Non-Negative Data Using Generalized Score Matching. AISTATS (2018)
Luca Weihs, Mathias Drton, Dennis Leung
Efficient computation of the Bergsma-Dassios sign covariance. Comput. Stat. 31 (1) (2016)
Carlos Améndola, Mathias Drton, Bernd Sturmfels
Maximum Likelihood Estimates for Gaussian Mixtures Are Transcendental. MACIS (2015)
Michael Finegold, Mathias Drton
Robust Graphical Modeling with t-Distributions. CoRR (2014)
Naftali Harris, Mathias Drton
PC algorithm for nonparanormal graphical models. J. Mach. Learn. Res. 14 (1) (2013)
Mathias Drton, Thomas S. Richardson
A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence CoRR (2012)
Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty
Nonparametric Reduced Rank Regression. NIPS (2012)
Mathias Drton, Thomas S. Richardson
Iterative Conditional Fitting for Gaussian Ancestral Graph Models CoRR (2012)
Mathias Drton, Josephine Yu
On a Parametrization of Positive Semidefinite Matrices with Zeros. SIAM J. Matrix Anal. Appl. 31 (5) (2010)
Rina Foygel, Mathias Drton
Extended Bayesian Information Criteria for Gaussian Graphical Models. NIPS (2010)
Mathias Drton, Michael Eichler, Thomas S. Richardson
Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors. J. Mach. Learn. Res. 10 (2009)
Michael Finegold, Mathias Drton
Robust Graphical Modeling with t-Distributions. UAI (2009)
Mathias Drton, Thomas S. Richardson
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models. J. Mach. Learn. Res. 9 (2008)
Mathias Drton
Computing all roots of the likelihood equations of seemingly unrelated regressions. J. Symb. Comput. 41 (2) (2006)
Mathias Drton, Thomas S. Richardson
Iterative Conditional Fitting for Gaussian Ancestral Graph Models. UAI (2004)
Mathias Drton, Thomas S. Richardson
A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence. UAI (2003)