Mathias Drton

According to our database, Mathias Drton authored at least 41 papers between 2003 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Identifiability of homoscedastic linear structural equation models using algebraic matroids.

Mathias Drton, Benjamin Hollering, Jun Wu

Adv. Appl. Math., 2025

2024

Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models.

Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton

CoRR, 2024

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding.

Daniela Schkoda, Elina Robeva, Mathias Drton

CoRR, 2024

Causal Effect Identification in LiNGAM Models with Latent Confounders.

Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Dual Likelihood for Causal Inference under Structure Uncertainty.

David Strieder, Mathias Drton

Proceedings of the Causal Learning and Reasoning, 2024

extttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery.

Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Martin Roth, Steffen Sonntag

Proceedings of the Causal Learning and Reasoning, 2024

On the Lasso for Graphical Continuous Lyapunov Models.

Philipp Dettling, Mathias Drton, Mladen Kolar

Proceedings of the Causal Learning and Reasoning, 2024

2023

Identifiability in Continuous Lyapunov Models.

Philipp Dettling, Roser Homs, Carlos Améndola, Mathias Drton, Niels Richard Hansen

SIAM J. Matrix Anal. Appl., December, 2023

Causal Structural Learning via Local Graphs.

Wenyu Chen, Mathias Drton, Ali Shojaie

SIAM J. Math. Data Sci., June, 2023

Partial Homoscedasticity in Causal Discovery With Linear Models.

Jun Wu, Mathias Drton

IEEE J. Sel. Areas Inf. Theory, 2023

Learning Linear Gaussian Polytree Models With Interventions.

Daniele Tramontano, L. Waldmann, Mathias Drton, Eliana Duarte

IEEE J. Sel. Areas Inf. Theory, 2023

Causal Discovery with Unobserved Confounding and Non-Gaussian Data.

Y. Samuel Wang, Mathias Drton

J. Mach. Learn. Res., 2023

causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery.

Konstantin Göbler, Tobias Windisch, Tim Pychynski, Steffen Sonntag, Martin Roth, Mathias Drton

CoRR, 2023

Unpaired Multi-Domain Causal Representation Learning.

Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Interaction Models and Generalized Score Matching for Compositional Data.

Shiqing Yu, Mathias Drton, Ali Shojaie

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

Directed Graphical Models and Causal Discovery for Zero-Inflated Data.

Shiqing Yu, Mathias Drton, Ali Shojaie

Proceedings of the Conference on Causal Learning and Reasoning, 2023

Rank-Based Causal Discovery for Post-Nonlinear Models.

Grigor Keropyan, David Strieder, Mathias Drton

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data.

Konstantin Göbler, Anne Miloschewski, Mathias Drton, Sach Mukherjee

CoRR, 2022

Fine-grained network traffic prediction from coarse data.

Krzysztof Rusek, Mathias Drton

CoRR, 2022

Learning linear non-Gaussian polytree models.

Daniele Tramontano, Anthea Monod, Mathias Drton

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Graphical Representations for Algebraic Constraints of Linear Structural Equations Models.

Thijs van Ommen, Mathias Drton

Proceedings of the International Conference on Probabilistic Graphical Models, 2022

2021

Definite Non-Ancestral Relations and Structure Learning.

Wenyu Chen, Mathias Drton, Ali Shojaie

CoRR, 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

Confidence in causal discovery with linear causal models.

David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

2020

Structure Learning for Cyclic Linear Causal Models.

Carlos Améndola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Statistical Significance in High-dimensional Linear Mixed Models.

Lina Lin, Mathias Drton, Ali Shojaie

Proceedings of the FODS '20: ACM-IMS Foundations of Data Science Conference, 2020

2019

Generalized Score Matching for Non-Negative Data.

Shiqing Yu, Mathias Drton, Ali Shojaie

J. Mach. Learn. Res., 2019

2018

Algebraic tests of general Gaussian latent tree models.

Dennis Leung, Mathias Drton

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Graphical Models for Non-Negative Data Using Generalized Score Matching.

Shiqing Yu, Mathias Drton, Ali Shojaie

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2016

Efficient computation of the Bergsma-Dassios sign covariance.

Luca Weihs, Mathias Drton, Dennis Leung

Comput. Stat., 2016

2015

Maximum Likelihood Estimates for Gaussian Mixtures Are Transcendental.

Carlos Améndola, Mathias Drton, Bernd Sturmfels

Proceedings of the Mathematical Aspects of Computer and Information Sciences, 2015

2013

PC algorithm for nonparanormal graphical models.

Naftali Harris, Mathias Drton

J. Mach. Learn. Res., 2013

2012

Nonparametric Reduced Rank Regression.

Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2010

On a Parametrization of Positive Semidefinite Matrices with Zeros.

Mathias Drton, Josephine Yu

SIAM J. Matrix Anal. Appl., 2010

Extended Bayesian Information Criteria for Gaussian Graphical Models.

Rina Foygel, Mathias Drton

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors.

Mathias Drton, Michael Eichler, Thomas S. Richardson

J. Mach. Learn. Res., 2009

Robust Graphical Modeling with t-Distributions.

Michael Finegold, Mathias Drton

Proceedings of the UAI 2009, 2009

2008

Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models.

Mathias Drton, Thomas S. Richardson

J. Mach. Learn. Res., 2008

2006

Computing all roots of the likelihood equations of seemingly unrelated regressions.

Mathias Drton

J. Symb. Comput., 2006

2004

Iterative Conditional Fitting for Gaussian Ancestral Graph Models.

Mathias Drton, Thomas S. Richardson

Proceedings of the UAI '04, 2004

2003

A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence.

Mathias Drton, Thomas S. Richardson

Proceedings of the UAI '03, 2003