Mathias Drton - RankMe

65 papers A* 4A 11Journal 37Unranked 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Efficient Learning of Stationary Diffusions with Stein-type Discrepancies. CoRR Fabian Bleile, Sarah Lumpp, Mathias Drton
2025	J	`jnl`	Algebraic Sparse Factor Analysis. SIAM J. Appl. Algebra Geom. Mathias Drton, Alexandros Grosdos, Irem Portakal, Nils Sturma
2025	A	`conf`	Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles. UAI Mathias Drton, Marina Garrote-López, Niko Nikov, Elina Robeva, Y. Samuel Wang
2025	A*	`conf`	Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants. ICML Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Negar Kiyavash, Mathias Drton
2025	J	`jnl`	Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants. CoRR Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash
2025	—	`ed.`	Causal Learning and Reasoning, Lausanne, Switzerland, 7-9 May 2025. CLeaR Biwei Huang, Mathias Drton
2025	J	`jnl`	Identifiability of homoscedastic linear structural equation models using algebraic matroids. Adv. Appl. Math. Mathias Drton, Benjamin Hollering, Jun Wu
2025	J	`jnl`	Identifying total causal effects in linear models under partial homoscedasticity. Int. J. Approx. Reason. David Strieder, Mathias Drton
2025	A	`conf`	Nonlinear Causal Discovery for Grouped Data. UAI Konstantin Göbler, Tobias Windisch, Mathias Drton
2025	J	`jnl`	Nonlinear Causal Discovery for Grouped Data. CoRR Konstantin Göbler, Tobias Windisch, Mathias Drton
2025	A	`conf`	Robust Score Matching. AISTATS Richard Schwank, Andrew McCormack, Mathias Drton
2025	J	`jnl`	Robust Score Matching. CoRR Richard Schwank, Andrew McCormack, Mathias Drton
2025	J	`jnl`	Single-cell differential expression analysis between conditions within nested settings. Briefings Bioinform. Leon Hafner, Gregor Sturm, Sarah Lumpp, Mathias Drton, Markus List
2024	J	`jnl`	Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding. CoRR Daniela Schkoda, Elina Robeva, Mathias Drton
2024	A*	`conf`	Causal Effect Identification in LiNGAM Models with Latent Confounders. ICML Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash
2024	J	`jnl`	Causal Effect Identification in LiNGAM Models with Latent Confounders. CoRR Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash
2024	—	`conf`	Dual Likelihood for Causal Inference under Structure Uncertainty. CLeaR David Strieder, Mathias Drton
2024	—	`conf`	Identifying Total Causal Effects in Linear Models under Partial Homoscedasticity. PGM David Strieder, Mathias Drton
2024	—	`conf`	Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models. PGM Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton
2024	J	`jnl`	Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models. CoRR Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton
2024	—	`conf`	On the Lasso for Graphical Continuous Lyapunov Models. CLeaR Philipp Dettling, Mathias Drton, Mladen Kolar
2024	—	`conf`	extttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. CLeaR Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Martin Roth, Steffen Sonntag
2023	J	`jnl`	Causal Discovery with Unobserved Confounding and Non-Gaussian Data. J. Mach. Learn. Res. Y. Samuel Wang, Mathias Drton
2023	J	`jnl`	Causal Structural Learning via Local Graphs. SIAM J. Math. Data Sci. Wenyu Chen, Mathias Drton, Ali Shojaie
2023	—	`conf`	Directed Graphical Models and Causal Discovery for Zero-Inflated Data. CLeaR Shiqing Yu, Mathias Drton, Ali Shojaie
2023	J	`jnl`	Identifiability in Continuous Lyapunov Models. SIAM J. Matrix Anal. Appl. Philipp Dettling, Roser Homs, Carlos Améndola, Mathias Drton, Niels Richard Hansen
2023	—	`conf`	Interaction Models and Generalized Score Matching for Compositional Data. LoG Shiqing Yu, Mathias Drton, Ali Shojaie
2023	J	`jnl`	Learning Linear Gaussian Polytree Models With Interventions. IEEE J. Sel. Areas Inf. Theory Daniele Tramontano, L. Waldmann, Mathias Drton, Eliana Duarte
2023	J	`jnl`	Learning Linear Gaussian Polytree Models with Interventions. CoRR Daniele Tramontano, L. Waldmann, Mathias Drton, Eliana Duarte
2023	J	`jnl`	Partial Homoscedasticity in Causal Discovery With Linear Models. IEEE J. Sel. Areas Inf. Theory Jun Wu, Mathias Drton
2023	A	`conf`	Rank-Based Causal Discovery for Post-Nonlinear Models. AISTATS Grigor Keropyan, David Strieder, Mathias Drton
2023	J	`jnl`	Rank-Based Causal Discovery for Post-Nonlinear Models. CoRR Grigor Keropyan, David Strieder, Mathias Drton
2023	A*	`conf`	Unpaired Multi-Domain Causal Representation Learning. NeurIPS Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler
2023	J	`jnl`	Unpaired Multi-Domain Causal Representation Learning. CoRR Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler
2023	J	`jnl`	causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. CoRR Konstantin Göbler, Tobias Windisch, Tim Pychynski, Steffen Sonntag, Martin Roth, Mathias Drton
2022	J	`jnl`	Fine-grained network traffic prediction from coarse data. CoRR Krzysztof Rusek, Mathias Drton
2022	—	`conf`	Graphical Representations for Algebraic Constraints of Linear Structural Equations Models. PGM Thijs van Ommen, Mathias Drton
2022	J	`jnl`	Graphical Representations for Algebraic Constraints of Linear Structural Equations Models. CoRR Thijs van Ommen, Mathias Drton
2022	J	`jnl`	High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data. CoRR Konstantin Göbler, Anne Miloschewski, Mathias Drton, Sach Mukherjee
2022	J	`jnl`	Learning Linear Non-Gaussian Polytree Models. CoRR Daniele Tramontano, Anthea Monod, Mathias Drton
2022	A	`conf`	Learning linear non-Gaussian polytree models. UAI Daniele Tramontano, Anthea Monod, Mathias Drton
2021	A	`conf`	Confidence in causal discovery with linear causal models. UAI David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton
2021	J	`jnl`	CorDiffViz: an R package for visualizing multi-omics differential correlation networks. BMC Bioinform. Shiqing Yu, Mathias Drton, Daniel E. L. Promislow, Ali Shojaie
2021	J	`jnl`	Definite Non-Ancestral Relations and Structure Learning. CoRR Wenyu Chen, Mathias Drton, Ali Shojaie
2020	—	`conf`	Statistical Significance in High-dimensional Linear Mixed Models. FODS Lina Lin, Mathias Drton, Ali Shojaie
2020	A	`conf`	Structure Learning for Cyclic Linear Causal Models. UAI Carlos Améndola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu
2019	J	`jnl`	Generalized Score Matching for Non-Negative Data. J. Mach. Learn. Res. Shiqing Yu, Mathias Drton, Ali Shojaie
2018	A*	`conf`	Algebraic tests of general Gaussian latent tree models. NeurIPS Dennis Leung, Mathias Drton
2018	J	`jnl`	Generalized Score Matching for Non-Negative Data. CoRR Shiqing Yu, Mathias Drton, Ali Shojaie
2018	A	`conf`	Graphical Models for Non-Negative Data Using Generalized Score Matching. AISTATS Shiqing Yu, Mathias Drton, Ali Shojaie
2016	J	`jnl`	Efficient computation of the Bergsma-Dassios sign covariance. Comput. Stat. Luca Weihs, Mathias Drton, Dennis Leung
2015	—	`conf`	Maximum Likelihood Estimates for Gaussian Mixtures Are Transcendental. MACIS Carlos Améndola, Mathias Drton, Bernd Sturmfels
2014	J	`jnl`	Robust Graphical Modeling with t-Distributions. CoRR Michael Finegold, Mathias Drton
2013	J	`jnl`	PC algorithm for nonparanormal graphical models. J. Mach. Learn. Res. Naftali Harris, Mathias Drton
2012	J	`jnl`	A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence CoRR Mathias Drton, Thomas S. Richardson
2012	J	`jnl`	Iterative Conditional Fitting for Gaussian Ancestral Graph Models CoRR Mathias Drton, Thomas S. Richardson
2012	—	`conf`	Nonparametric Reduced Rank Regression. NIPS Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty
2010	—	`conf`	Extended Bayesian Information Criteria for Gaussian Graphical Models. NIPS Rina Foygel, Mathias Drton
2010	J	`jnl`	On a Parametrization of Positive Semidefinite Matrices with Zeros. SIAM J. Matrix Anal. Appl. Mathias Drton, Josephine Yu
2009	J	`jnl`	Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors. J. Mach. Learn. Res. Mathias Drton, Michael Eichler, Thomas S. Richardson
2009	A	`conf`	Robust Graphical Modeling with t-Distributions. UAI Michael Finegold, Mathias Drton
2008	J	`jnl`	Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models. J. Mach. Learn. Res. Mathias Drton, Thomas S. Richardson
2006	J	`jnl`	Computing all roots of the likelihood equations of seemingly unrelated regressions. J. Symb. Comput. Mathias Drton
2004	A	`conf`	Iterative Conditional Fitting for Gaussian Ancestral Graph Models. UAI Mathias Drton, Thomas S. Richardson
2003	A	`conf`	A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence. UAI Mathias Drton, Thomas S. Richardson