Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification. - Gherardo Varando; Concha Bielza; Pedro Larrañaga (Probabilistic Graphical Models, 2014)

Conditional Density Approximations with Mixtures of Polynomials. - Gherardo Varando; Pedro L. López-Cruz; Thomas D. Nielsen; Pedro Larrañaga; Concha Bielza (Int. J. Intell. Syst., 2015)

Decision boundary for discrete Bayesian network classifiers. - Gherardo Varando; Concha Bielza; Pedro Larrañaga (J. Mach. Learn. Res., 2015)

A survey on multi-output regression. - Hanen Borchani; Gherardo Varando; Concha Bielza; Pedro Larrañaga (WIREs Data Mining Knowl. Discov., 2015)

Decision functions for chain classifiers based on Bayesian networks for multi-label classification. - Gherardo Varando; Concha Bielza; Pedro Larrañaga (Int. J. Approx. Reason., 2016)

A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. - Irene Córdoba; Gherardo Varando; Concha Bielza; Pedro Larrañaga (IDEAL (1), 2018)

A partial orthogonalization method for simulating covariance and concentration graph matrices. - Irene Córdoba; Gherardo Varando; Concha Bielza; Pedro Larrañaga (PGM, 2018)

Markov Property in Generative Classifiers. - Gherardo Varando; Concha Bielza; Pedro Larrañaga; Eva Riccomagno (CoRR, 2018)

Theoretical studies on Bayesian network classifiers. - Gherardo Varando (2018)

Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values. - Sebastian Weichwald; Martin Emil Jakobsen; Phillip B. Mogensen; Lasse Petersen; Nikolaj Thams; Gherardo Varando (NeurIPS (Competition and Demos), 2019)

Graphical continuous Lyapunov models. - Gherardo Varando; Niels Richard Hansen (UAI, 2020)

Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data. - Irene Córdoba; Concha Bielza; Pedro Larrañaga; Gherardo Varando (IEEE Access, 2020)

Learning DAGs without imposing acyclicity. - Gherardo Varando (CoRR, 2020)

On generating random Gaussian graphical models. - Irene Córdoba; Gherardo Varando; Concha Bielza; Pedro Larrañaga (Int. J. Approx. Reason., 2020)

Staged trees and asymmetry-labeled DAGs. - Gherardo Varando; Federico Carli; Manuele Leonelli (CoRR, 2021)

Highly Efficient Structural Learning of Sparse Staged Trees. - Manuele Leonelli; Gherardo Varando (PGM, 2022)

The R Package stagedtrees for Structural Learning of Stratified Staged Trees. - Federico Carli; Manuele Leonelli; Eva Riccomagno; Gherardo Varando (J. Stat. Softw., 2022)

Context-Specific Causal Discovery for Categorical Data Using Staged Trees. - Manuele Leonelli; Gherardo Varando (AISTATS, 2023)

Discovering Causal Relations and Equations from Data. - Gustau Camps-Valls; Andreas Gerhardus; Urmi Ninad; Gherardo Varando; Georg Martius; Emili Balaguer-Ballester; Ricardo Vinuesa; Emiliano Diaz; Laure Zanna; Jakob Runge (CoRR, 2023)

A new class of generative classifiers based on staged tree models. - Federico Carli; Manuele Leonelli; Gherardo Varando (Knowl. Based Syst., 2023)

Learning latent functions for causal discovery. - Emiliano Diaz; Gherardo Varando; Juan Emmanuel Johnson; Gustau Camps-Valls (Mach. Learn. Sci. Technol., 2023)

Learning Staged Trees from Incomplete Data. - Jack Storror Carter; Manuele Leonelli; Eva Riccomagno; Gherardo Varando (PGM, 2024)

Context-Specific Refinements of Bayesian Network Classifiers. - Manuele Leonelli; Gherardo Varando (PGM, 2024)

Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear. - Manuele Leonelli; Gherardo Varando (Appl. Intell., 2024)

Double machine learning for causal hybrid modeling - applications in the Earth sciences. - Kai-Hendrik Cohrs; Gherardo Varando; Nuno Carvalhais; Markus Reichstein; Gustau Camps-Valls (CoRR, 2024)

Improving generalisation via anchor multivariate analysis. - Homer Durand; Gherardo Varando; Nathan Mankovich; Gustau Camps-Valls (CoRR, 2024)

Recovering Latent Confounders from High-dimensional Proxy Variables. - Nathan Mankovich; Homer Durand; Emiliano Diaz; Gherardo Varando; Gustau Camps-Valls (CoRR, 2024)

Large Language Models for Constrained-Based Causal Discovery. - Kai-Hendrik Cohrs; Gherardo Varando; Emiliano Diaz; Vasileios Sitokonstantinou; Gustau Camps-Valls (CoRR, 2024)

Causal hybrid modeling with double machine learning - applications in carbon flux modeling. - Kai-Hendrik Cohrs; Gherardo Varando; Nuno Carvalhais; Markus Reichstein; Gustau Camps-Valls (Mach. Learn. Sci. Technol., 2024)

Pairwise causal discovery with support measure machines. - Gherardo Varando; Salvador Catsis; Emiliano Diaz; Gustau Camps-Valls (Appl. Soft Comput., 2024)

Structural learning of simple staged trees. - Manuele Leonelli; Gherardo Varando (Data Min. Knowl. Discov., 2024)

Out-of-distribution robustness for multivariate analysis via causal regularisation. - Homer Durand; Gherardo Varando; Nathan Mankovich; Gustau Camps-Valls (AISTATS, 2025)

Learning Causal Response Representations through Direct Effect Analysis. - Homer Durand; Gherardo Varando; Gustau Camps-Valls (UAI, 2025)

Large language models for causal hypothesis generation in science. - Kai-Hendrik Cohrs; Emiliano Diaz; Vasileios Sitokonstantinou; Gherardo Varando; Gustau Camps-Valls (Mach. Learn. Sci. Technol., 2025)