Gherardo Varando

According to our database, Gherardo Varando authored at least 31 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Structural learning of simple staged trees.

Manuele Leonelli, Gherardo Varando

Data Min. Knowl. Discov., May, 2024

Pairwise causal discovery with support measure machines.

Gherardo Varando, Salvador Catsis, Emiliano Diaz, Gustau Camps-Valls

Appl. Soft Comput., January, 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

Large Language Models for Constrained-Based Causal Discovery.

Kai-Hendrik Cohrs, Gherardo Varando, Emiliano Diaz, Vasileios Sitokonstantinou, Gustau Camps-Valls

CoRR, 2024

Learning Staged Trees from Incomplete Data.

Jack Storror Carter, Manuele Leonelli, Eva Riccomagno, Gherardo Varando

CoRR, 2024

Context-Specific Refinements of Bayesian Network Classifiers.

Manuele Leonelli, Gherardo Varando

CoRR, 2024

Recovering Latent Confounders from High-dimensional Proxy Variables.

Nathan Mankovich, Homer Durand, Emiliano Diaz, Gherardo Varando, Gustau Camps-Valls

CoRR, 2024

Improving generalisation via anchor multivariate analysis.

Homer Durand, Gherardo Varando, Nathan Mankovich, Gustau Camps-Valls

CoRR, 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

Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear.

Manuele Leonelli, Gherardo Varando

Appl. Intell., 2024

2023

Learning latent functions for causal discovery.

Emiliano Diaz, Gherardo Varando, Juan Emmanuel Johnson, Gustau Camps-Valls

Mach. Learn. Sci. Technol., September, 2023

A new class of generative classifiers based on staged tree models.

Federico Carli, Manuele Leonelli, Gherardo Varando

Knowl. Based Syst., 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

Context-Specific Causal Discovery for Categorical Data Using Staged Trees.

Manuele Leonelli, Gherardo Varando

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

The <i>R</i> Package stagedtrees for Structural Learning of Stratified Staged Trees.

Federico Carli, Manuele Leonelli, Eva Riccomagno, Gherardo Varando

J. Stat. Softw., 2022

Highly Efficient Structural Learning of Sparse Staged Trees.

Manuele Leonelli, Gherardo Varando

Proceedings of the International Conference on Probabilistic Graphical Models, 2022

2021

Staged trees and asymmetry-labeled DAGs.

Gherardo Varando, Federico Carli, Manuele Leonelli

CoRR, 2021

2020

On generating random Gaussian graphical models.

Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga

Int. J. Approx. Reason., 2020

Learning DAGs without imposing acyclicity.

Gherardo Varando

CoRR, 2020

Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data.

Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando

IEEE Access, 2020

Graphical continuous Lyapunov models.

Gherardo Varando, Niels Richard Hansen

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

2019

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

Proceedings of the NeurIPS 2019 Competition and Demonstration Track, 2019

2018

Theoretical studies on Bayesian network classifiers.

Gherardo Varando

PhD thesis, 2018

Markov Property in Generative Classifiers.

Gherardo Varando, Concha Bielza, Pedro Larrañaga, Eva Riccomagno

CoRR, 2018

A partial orthogonalization method for simulating covariance and concentration graph matrices.

Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga

Proceedings of the International Conference on Probabilistic Graphical Models, 2018

A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices.

Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2018, 2018

2016

Decision functions for chain classifiers based on Bayesian networks for multi-label classification.

Gherardo Varando, Concha Bielza, Pedro Larrañaga

Int. J. Approx. Reason., 2016

2015

A survey on multi-output regression.

Hanen Borchani, Gherardo Varando, Concha Bielza, Pedro Larrañaga

WIREs Data Mining Knowl. Discov., 2015

Decision boundary for discrete Bayesian network classifiers.

Gherardo Varando, Concha Bielza, Pedro Larrañaga

J. Mach. Learn. Res., 2015

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

2014

Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification.

Gherardo Varando, Concha Bielza, Pedro Larrañaga

Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014