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ORCIDMathias Drton
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2020 – today
- 2025
[j14]Mathias Drton, Benjamin Hollering
, Jun Wu:
Identifiability of homoscedastic linear structural equation models using algebraic matroids. Adv. Appl. Math. 163: 102794 (2025)- 2024
- [c21]David Strieder, Mathias Drton:
Dual Likelihood for Causal Inference under Structure Uncertainty. CLeaR 2024: 1-17 - [c20]Philipp Dettling, Mathias Drton, Mladen Kolar:
On the Lasso for Graphical Continuous Lyapunov Models. CLeaR 2024: 514-550 - [c19]Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Martin Roth, Steffen Sonntag:
extttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. CLeaR 2024: 609-642 - [c18]Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash:
Causal Effect Identification in LiNGAM Models with Latent Confounders. ICML 2024 - [i16]Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash:
Causal Effect Identification in LiNGAM Models with Latent Confounders. CoRR abs/2406.02049 (2024) - [i15]Daniela Schkoda, Elina Robeva, Mathias Drton:
Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding. CoRR abs/2408.04907 (2024) - [i14]Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton:
Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models. CoRR abs/2408.10976 (2024) - 2023
- [j13]Y. Samuel Wang, Mathias Drton:
Causal Discovery with Unobserved Confounding and Non-Gaussian Data. J. Mach. Learn. Res. 24: 271:1-271:61 (2023) - [j12]Daniele Tramontano, L. Waldmann, Mathias Drton, Eliana Duarte:
Learning Linear Gaussian Polytree Models With Interventions. IEEE J. Sel. Areas Inf. Theory 4: 569-578 (2023) - [j11]Jun Wu, Mathias Drton:
Partial Homoscedasticity in Causal Discovery With Linear Models. IEEE J. Sel. Areas Inf. Theory 4: 639-650 (2023) - [j10]Philipp Dettling, Roser Homs, Carlos Améndola, Mathias Drton, Niels Richard Hansen:
Identifiability in Continuous Lyapunov Models. SIAM J. Matrix Anal. Appl. 44(4): 1799-1821 (2023) - [j9]Wenyu Chen, Mathias Drton, Ali Shojaie:
Causal Structural Learning via Local Graphs. SIAM J. Math. Data Sci. 5(2): 280-305 (2023) - [c17]Grigor Keropyan, David Strieder, Mathias Drton:
Rank-Based Causal Discovery for Post-Nonlinear Models. AISTATS 2023: 7849-7870 - [c16]Shiqing Yu, Mathias Drton, Ali Shojaie:
Directed Graphical Models and Causal Discovery for Zero-Inflated Data. CLeaR 2023: 27-67 - [c15]Shiqing Yu, Mathias Drton, Ali Shojaie:
Interaction Models and Generalized Score Matching for Compositional Data. LoG 2023: 20 - [c14]Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler:
Unpaired Multi-Domain Causal Representation Learning. NeurIPS 2023 - [i13]Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler:
Unpaired Multi-Domain Causal Representation Learning. CoRR abs/2302.00993 (2023) - [i12]Grigor Keropyan, David Strieder, Mathias Drton:
Rank-Based Causal Discovery for Post-Nonlinear Models. CoRR abs/2302.12341 (2023) - [i11]Konstantin Göbler, Tobias Windisch, Tim Pychynski, Steffen Sonntag, Martin Roth, Mathias Drton:
causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. CoRR abs/2306.10816 (2023) - [i10]Daniele Tramontano, L. Waldmann, Mathias Drton, Eliana Duarte:
Learning Linear Gaussian Polytree Models with Interventions. CoRR abs/2311.04636 (2023) - 2022
- [c13]Thijs van Ommen, Mathias Drton:
Graphical Representations for Algebraic Constraints of Linear Structural Equations Models. PGM 2022: 409-420 - [c12]Daniele Tramontano, Anthea Monod, Mathias Drton:
Learning linear non-Gaussian polytree models. UAI 2022: 1960-1969 - [i9]Krzysztof Rusek, Mathias Drton:
Fine-grained network traffic prediction from coarse data. CoRR abs/2201.07179 (2022) - [i8]Thijs van Ommen, Mathias Drton:
Graphical Representations for Algebraic Constraints of Linear Structural Equations Models. CoRR abs/2208.00926 (2022) - [i7]Daniele Tramontano, Anthea Monod, Mathias Drton:
Learning Linear Non-Gaussian Polytree Models. CoRR abs/2208.06701 (2022) - [i6]Konstantin Göbler, Anne Miloschewski, Mathias Drton, Sach Mukherjee:
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data. CoRR abs/2211.11700 (2022) - 2021
- [j8]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): 486 (2021) - [c11]David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton:
Confidence in causal discovery with linear causal models. UAI 2021: 1217-1226 - [i5]Wenyu Chen, Mathias Drton, Ali Shojaie:
Definite Non-Ancestral Relations and Structure Learning. CoRR abs/2105.10350 (2021) - 2020
- [c10]Lina Lin, Mathias Drton, Ali Shojaie:
Statistical Significance in High-dimensional Linear Mixed Models. FODS 2020: 171-181 - [c9]Carlos Améndola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu:
Structure Learning for Cyclic Linear Causal Models. UAI 2020: 999-1008
2010 – 2019
- 2019
- [j7]Shiqing Yu, Mathias Drton, Ali Shojaie:
Generalized Score Matching for Non-Negative Data. J. Mach. Learn. Res. 20: 76:1-76:70 (2019) - 2018
- [c8]Shiqing Yu, Mathias Drton, Ali Shojaie:
Graphical Models for Non-Negative Data Using Generalized Score Matching. AISTATS 2018: 1781-1790 - [c7]Dennis Leung, Mathias Drton:
Algebraic tests of general Gaussian latent tree models. NeurIPS 2018: 6301-6310 - [i4]Shiqing Yu, Mathias Drton, Ali Shojaie:
Generalized Score Matching for Non-Negative Data. CoRR abs/1812.10551 (2018) - 2016
- [j6]Luca Weihs, Mathias Drton, Dennis Leung:
Efficient computation of the Bergsma-Dassios sign covariance. Comput. Stat. 31(1): 315-328 (2016) - 2015
- [c6]Carlos Améndola, Mathias Drton, Bernd Sturmfels:
Maximum Likelihood Estimates for Gaussian Mixtures Are Transcendental. MACIS 2015: 579-590 - 2014
- [i3]Michael Finegold, Mathias Drton:
Robust Graphical Modeling with t-Distributions. CoRR abs/1408.2033 (2014) - 2013
- [j5]Naftali Harris, Mathias Drton:
PC algorithm for nonparanormal graphical models. J. Mach. Learn. Res. 14(1): 3365-3383 (2013) - 2012
- [c5]Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty:
Nonparametric Reduced Rank Regression. NIPS 2012: 1637-1645 - [i2]Mathias Drton, Thomas S. Richardson:
Iterative Conditional Fitting for Gaussian Ancestral Graph Models. CoRR abs/1207.4118 (2012) - [i1]Mathias Drton, Thomas S. Richardson:
A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence. CoRR abs/1212.2462 (2012) - 2010
- [j4]Mathias Drton, Josephine Yu:
On a Parametrization of Positive Semidefinite Matrices with Zeros. SIAM J. Matrix Anal. Appl. 31(5): 2665-2680 (2010) - [c4]Rina Foygel, Mathias Drton:
Extended Bayesian Information Criteria for Gaussian Graphical Models. NIPS 2010: 604-612
2000 – 2009
- 2009
- [j3]Mathias Drton, Michael Eichler, Thomas S. Richardson:
Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors. J. Mach. Learn. Res. 10: 2329-2348 (2009) - [c3]Michael Finegold, Mathias Drton:
Robust Graphical Modeling with t-Distributions. UAI 2009: 169-176 - 2008
- [j2]Mathias Drton, Thomas S. Richardson:
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models. J. Mach. Learn. Res. 9: 893-914 (2008) - 2006
- [j1]Mathias Drton:
Computing all roots of the likelihood equations of seemingly unrelated regressions. J. Symb. Comput. 41(2): 245-254 (2006) - 2004
- [c2]Mathias Drton, Thomas S. Richardson:
Iterative Conditional Fitting for Gaussian Ancestral Graph Models. UAI 2004: 130-137 - 2003
- [c1]Mathias Drton, Thomas S. Richardson:
A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence. UAI 2003: 184-191
Coauthor Index
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