Mathias Drton

Orcid: 0000-0001-5614-3025

According to our database1, Mathias Drton authored at least 32 papers between 2003 and 2023.

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Bibliography

2023
Identifiability in Continuous Lyapunov Models.
SIAM J. Matrix Anal. Appl., December, 2023

Causal Structural Learning via Local Graphs.
SIAM J. Math. Data Sci., June, 2023

Partial Homoscedasticity in Causal Discovery With Linear Models.
IEEE J. Sel. Areas Inf. Theory, 2023

Learning Linear Gaussian Polytree Models With Interventions.
IEEE J. Sel. Areas Inf. Theory, 2023

causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery.
CoRR, 2023

Unpaired Multi-Domain Causal Representation Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Directed Graphical Models and Causal Discovery for Zero-Inflated Data.
Proceedings of the Conference on Causal Learning and Reasoning, 2023

Rank-Based Causal Discovery for Post-Nonlinear Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data.
CoRR, 2022

Fine-grained network traffic prediction from coarse data.
CoRR, 2022

Learning linear non-Gaussian polytree models.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Graphical Representations for Algebraic Constraints of Linear Structural Equations Models.
Proceedings of the International Conference on Probabilistic Graphical Models, 2022

2021
Definite Non-Ancestral Relations and Structure Learning.
CoRR, 2021

CorDiffViz: an R package for visualizing multi-omics differential correlation networks.
BMC Bioinform., 2021

Confidence in causal discovery with linear causal models.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

2020
Structure Learning for Cyclic Linear Causal Models.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Statistical Significance in High-dimensional Linear Mixed Models.
Proceedings of the FODS '20: ACM-IMS Foundations of Data Science Conference, 2020

2019
Generalized Score Matching for Non-Negative Data.
J. Mach. Learn. Res., 2019

2018
Algebraic tests of general Gaussian latent tree models.
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.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2016
Efficient computation of the Bergsma-Dassios sign covariance.
Comput. Stat., 2016

2015
Maximum Likelihood Estimates for Gaussian Mixtures Are Transcendental.
Proceedings of the Mathematical Aspects of Computer and Information Sciences, 2015

2013
PC algorithm for nonparanormal graphical models.
J. Mach. Learn. Res., 2013

2012
Nonparametric Reduced Rank Regression.
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.
SIAM J. Matrix Anal. Appl., 2010

Extended Bayesian Information Criteria for Gaussian Graphical Models.
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.
J. Mach. Learn. Res., 2009

Robust Graphical Modeling with t-Distributions.
Proceedings of the UAI 2009, 2009

2008
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models.
J. Mach. Learn. Res., 2008

2006
Computing all roots of the likelihood equations of seemingly unrelated regressions.
J. Symb. Comput., 2006

2004
Iterative Conditional Fitting for Gaussian Ancestral Graph Models.
Proceedings of the UAI '04, 2004

2003
A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence.
Proceedings of the UAI '03, 2003


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