Graphical Models for Complex Multivariate Data

Informazioni relative al progetto

GRAPHMODE

ID dell’accordo di sovvenzione: 883818

DOI

10.3030/883818

Data della firma CE 27 Aprile 2020

Data di avvio 1 Ottobre 2020

Data di completamento 30 Settembre 2026

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale

€ 1 971 296,00

Contributo UE

€ 1 971 296,00

Coordinato da

TECHNISCHE UNIVERSITAET MUENCHEN
Germany

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Pubblicazioni

Other (15)

Faithlessness in Gaussian graphical models

Autori: Mathias Drton, Leonard Henckel, Benjamin Hollering, Pratik Misra
Pubblicato in: 2024
Editore: ArXiv
DOI: 10.48550/arxiv.2404.05306

causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery

Autori: Konstantin Göbler, Tobias Windisch, Tim Pychynski, Steffen Sonntag, Martin Roth, Mathias Drton
Pubblicato in: 2023
Editore: arXiv
DOI: 10.48550/arxiv.2306.10816

Unpaired Multi-Domain Causal Representation Learning

Autori: Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler
Pubblicato in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023), Numero to appear, 2023
Editore: arXiv
DOI: 10.48550/arxiv.2302.00993

Confidence Sets for Causal Orderings

Autori: Y. Samuel Wang, Mladen Kolar, Mathias Drton
Pubblicato in: 2023
Editore: arXiv
DOI: 10.48550/arxiv.2305.14506

Existence of Direct Density Ratio Estimators

Autori: Erika Banzato, Mathias Drton, Kian Saraf-Poor, Hongjian Shi
Pubblicato in: 2025
Editore: ArXiv

Matching Criterion for Identifiability in Sparse Factor Analysis

Autori: Nils Sturma, Miriam Kranzlmueller, Irem Portakal, Mathias Drton
Pubblicato in: 2025
Editore: ArXiv

On the Lasso for Graphical Continuous Lyapunov Models

Autori: Philipp Dettling, Mathias Drton, Mladen Kolar
Pubblicato in: 2022
Editore: arXiv
DOI: 10.48550/arxiv.2208.13572

On universal inference in Gaussian mixture models

Autori: Hongjian Shi, Mathias Drton
Pubblicato in: 2024
Editore: ArXiv
DOI: 10.48550/arxiv.2407.19361

Identifiability of Homoscedastic Linear Structural Equation Models using Algebraic Matroids

Autori: Mathias Drton, Benjamin Hollering, Jun Wu
Pubblicato in: 2023
Editore: arXiv
DOI: 10.48550/arxiv.2308.01821

Goodness-of-Fit Tests for Linear Non-Gaussian Structural Equation Models

Autori: Daniela Schkoda, Mathias Drton
Pubblicato in: 2023
Editore: arXiv
DOI: 10.48550/arxiv.2311.04585

Testing Many and Possibly Singular Polynomial Constraints

Autori: Nils Sturma, Mathias Drton, Dennis Leung
Pubblicato in: 2022
Editore: arXiv
DOI: 10.48550/arxiv.2208.11756

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

Autori: Daniela Schkoda, Elina Robeva, Mathias Drton
Pubblicato in: 2024
Editore: ArXiv
DOI: 10.48550/arxiv.2408.04907

Colored Gaussian DAG models

Autori: Tobias Boege, Kaie Kubjas, Pratik Misra, Liam Solus
Pubblicato in: 2024
Editore: ArXiv
DOI: 10.48550/arxiv.2404.04024

Identifiability in Continuous Lyapunov Models

Autori: Philipp Dettling, Roser Homs, Carlos Améndola, Mathias Drton, Niels Richard Hansen
Pubblicato in: 2022
Editore: arXiv
DOI: 10.48550/arxiv.2209.03835

Parametric and nonparametric symmetries in graphical models for extremes

Autori: Röttger, Frank; Coons, Jane Ivy; Grosdos, Alexandros
Pubblicato in: Numero 1, 2023
Editore: ArXiv
DOI: 10.48550/arxiv.2306.00703

Conference proceedings (12)

Directed Graphical Models and Causal Discovery for Zero-Inflated Data

Autori: Shiqing Yu, Mathias Drton, Ali Shojaie
Pubblicato in: Proceedings of the Second Conference on Causal Learning and Reasoning, Numero 213, 2023, Pagina/e 27-67
Editore: PMLR

Causal Effect Identification in LiNGAM Models with Latent Confounders

Autori: Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash
Pubblicato in: Proceedings of the 41st International Conference on Machine Learning, Numero 235, 2024
Editore: Proceedings of Machine Learning Research (PMLR)

Interaction Models and Generalized Score Matching for Compositional Data

Autori: Shiqing Yu, Mathias Drton, and Ali Shojaie
Pubblicato in: Proceedings of the Second Learning on Graphs Conference, Numero 231, 2024
Editore: Proceedings of Machine Learning Research (PMLR)

Identifying Total Causal Effects in Linear Models under Partial Homoscedasticity

Autori: David Strieder, Mathias Drton
Pubblicato in: Proceedings of the 12th International Conference on Probabilistic Graphical Models, Numero 246, 2024
Editore: Proceedings of Machine Learning Research (PMLR)

Rank-Based Causal Discovery for Post-Nonlinear Models

Autori: Grigor Keropyan, David Strieder, Mathias Drton
Pubblicato in: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 2023, Pagina/e 7849-7870
Editore: PMLR Proceedings of Machine Learning Research

Definite Non-Ancestral Relations and Structure Learning

Autori: Wenyu Chen, Mathias Drton, Ali Shojaie
Pubblicato in: 8th Causal Inference Workshop at UAI, 2021, Pagina/e "Paper #2"
Editore: causalUAI2021

Robust Score Matching

Autori: Richard Schwank, Andrew McCormack, Mathias Drton
Pubblicato in: Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Editore: Proceedings of Machine Learning Research (PMLR)

Learning Linear Non-Gaussian Polytree Models

Autori: Daniele Tramontano, Anthea Monod, Mathias Drton
Pubblicato in: Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, Numero Proceedings of Machine Learning Research 180, 2022, Pagina/e 1960-1969
Editore: PMLR
DOI: 10.48550/arxiv.2208.06701

Confidence in Causal Discovery with Linear Causal Models

Autori: David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton
Pubblicato in: Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, Numero PMLR 161, 2021, Pagina/e 1217-1226
Editore: Proceedings of Machine Learning Research

Dual Likelihood for Causal Inference under Structure Uncertainty

Autori: David Strieder, Mathias Drton
Pubblicato in: Proceedings of the 3rd Conference on Causal Learning and Reasoning, Numero 236, 2024
Editore: Proceedings of Machine Learning Research (PMLR)

Center-Outward Sign- and Rank-Based Quadrant, Spearman, and Kendall Tests for Multivariate Independence

Autori: Hongjian Shi, Mathias Drton, Marc Hallin, Fang Han
Pubblicato in: OT-SDM 2022: The 1st International Workshop on Optimal Transport and Structured Data Modeling, 2022
Editore: AAAI 2022

Graphical Representations for Algebraic Constraints of Linear Structural Equations Models

Autori: Thijs van Ommen, Mathias Drton
Pubblicato in: Proceedings of The 11th International Conference on Probabilistic Graphical Models, Numero 186, 2022, Pagina/e 409-420
Editore: Proceedings of Machine Learning Research (PMLR)
DOI: 10.48550/arxiv.2208.00926

Peer reviewed articles (18)

Rational maximum likelihood estimators of Kronecker covariance matrices

Autori: Mathias Drton, Alexandros Grosdos, Andrew McCormack
Pubblicato in: Algebraic Statistics, Numero 15, 2024, Pagina/e 145-164, ISSN 2693-3004
Editore: Mathematical Sciences Publishers
DOI: 10.2140/astat.2024.15.145

Partial Homoscedasticity in Causal Discovery with Linear Models

Autori: Jun Wu, Mathias Drton
Pubblicato in: IEEE Journal on Selected Areas in Information Theory, 2023, ISSN 2641-8770
Editore: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/jsait.2023.3328476

Learning Linear Gaussian Polytree Models With Interventions

Autori: Daniele Tramontano, Leonard Waldmann, Mathias Drton, Eliana Duarte
Pubblicato in: IEEE Journal on Selected Areas in Information Theory, 2023, ISSN 2641-8770
Editore: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/jsait.2023.3328429

Half-Trek Criterion for Identifiability of Latent Variable Models

Autori: Rina Foygel Barber, Mathias Drton, Nils Sturma, Luca Weihs
Pubblicato in: The Annals of Statistics, Numero 50, 2022, Pagina/e 3174-3196, ISSN 0090-5364
Editore: Institute of Mathematical Statistics
DOI: 10.1214/22-aos2221

On the choice of the splitting ratio for the split likelihood ratio test

Autori: David Strieder, Mathias Drton
Pubblicato in: Electronic Journal of Statistics, Numero 16, 2022, Pagina/e 6631-6650, ISSN 1935-7524
Editore: Institute of Mathematical Statistics
DOI: 10.1214/22-ejs2099

Causal Structural Learning via Local Graphs

Autori: Wenyu Chen, Mathias Drton, Ali Shojaie
Pubblicato in: SIAM Journal on Mathematics of Data Science, Numero 5(2), 2023, Pagina/e 280-305, ISSN 2577-0187
Editore: Society for Industrial and Applied Mathematics
DOI: 10.1137/20m1362796

"Discussion of ""A note on universal inference"" by Timmy Tse and Anthony Davison"

Autori: Mathias Drton, Hongjian Shi, David Strieder
Pubblicato in: Stat, Numero 12(1), 2023, Pagina/e e574, ISSN 2049-1573
Editore: Wiley
DOI: 10.1002/sta4.572

On Azadkia–Chatterjee’s conditional dependence coefficient

Autori: Hongjian Shi, Mathias Drton, Fang Han
Pubblicato in: Bernoulli, Numero 30, 2024, ISSN 1350-7265
Editore: Chapman & Hall
DOI: 10.3150/22-bej1529

On the power of Chatterjee’s rank correlation

Autori: Hongjian Shi, Mathias Drton, Fang Han
Pubblicato in: Biometrika, 2021, ISSN 0006-3444
Editore: Oxford University Press
DOI: 10.1093/biomet/asab028

Algebraic Sparse Factor Analysis

Autori: Mathias Drton, Alexandros Grosdos, Irem Portakal, Nils Sturma
Pubblicato in: SIAM Journal on Applied Algebra and Geometry, Numero 9, 2025, Pagina/e 279-309, ISSN 2470-6566
Editore: Society for Industrial & Applied Mathematics (SIAM)
DOI: 10.1137/23m1626517

Homaloidal polynomials and Gaussian models of maximum likelihood degree 1

Autori: Shelby Cox, Pratik Misra, Pardis Semnani
Pubblicato in: Algebraic Statistics, Numero 15, 2024, Pagina/e 167-198, ISSN 2693-3004
Editore: Mathematical Sciences Publishers
DOI: 10.2140/astat.2024.15.167

Third-Order Moment Varieties of Linear Non-Gaussian Graphical Models

Autori: Carlos Améndola, Mathias Drton, Alexandros Grosdos, Roser Homs, Elina Robeva
Pubblicato in: Information and Inference. A Journal of the IMA, Numero 12(3), 2023, Pagina/e iaad007, ISSN 2049-8764
Editore: Oxford University Press
DOI: 10.1093/imaiai/iaad007

Confidence in Causal Inference under Structure Uncertainty in Linear Causal Models with Equal Variances

Autori: David Strieder, Mathias Drton
Pubblicato in: Journal of Causal Inference, Numero to appear, 2023, ISSN 2193-3685
Editore: De Gruyter

On universally consistent and fully distribution-free rank tests of vector independence

Autori: Hongjian Shi, Marc Hallin, Mathias Drton, Fang Han
Pubblicato in: The Annals of Statistics, Numero 50, 2022, Pagina/e 1933-1959, ISSN 0090-5364
Editore: Institute of Mathematical Statistics
DOI: 10.1214/21-aos2151

High-dimensional undirected graphical models for arbitrary mixed data

Autori: Konstantin Göbler, Mathias Drton, Sach Mukherjee, Anne Miloschewski
Pubblicato in: Electronic Journal of Statistics, Numero 18, 2024, ISSN 1935-7524
Editore: Institute of Mathematical Statistics
DOI: 10.1214/24-ejs2254

Distribution-free tests of multivariate independence based on center-outward quadrant, Spearman, Kendall, and van der Waerden statistics

Autori: Hongjian Shi, Mathias Drton, Marc Hallin, Fang Han
Pubblicato in: Bernoulli, Numero 31, 2025, Pagina/e 106-129, ISSN 1350-7265
Editore: Chapman & Hall
DOI: 10.3150/24-bej1721

Conditional independence in stationary distributions of diffusions

Autori: Tobias Boege, Mathias Drton, Benjamin Hollering, Sarah Lumpp, Pratik Misra, Daniela Schkoda
Pubblicato in: Stochastic Processes and their Applications, Numero 184, 2025, Pagina/e 104604, ISSN 0304-4149
Editore: Elsevier BV
DOI: 10.1016/j.spa.2025.104604

Causal Discovery with Unobserved Confounding and non-Gaussian Data

Autori: Y. Samuel Wang, Mathias Drton
Pubblicato in: Journal of Machine Learning Research, Numero 271, 2023, Pagina/e 1−61, ISSN 1533-7928
Editore: JMLR, Inc.
DOI: 10.48550/arxiv.2007.11131

Thesis and dissertations (3)

Identifiability and Statistical Inference in Latent Variable Modeling

Autori: Sturma, Nils B.
Pubblicato in: 2024
Editore: online

Homoscedasticity and Feedback Loops in Graphical Models

Autori: Wu, Jun
Pubblicato in: Numero 5, 2023
Editore: Technical University of Munich

Graphical Continuous Lyapunov Models

Autori: Dettling, Philipp Maximilian
Pubblicato in: 2024
Editore: online