Gherardo Varando

46 papers A 4Journal 34Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Estimating Staged Event Tree Models via Hierarchical Clustering on the Simplex. CoRR Muhammad Shoaib, Eva Riccomagno, Manuele Leonelli, Gherardo Varando
2025	J	`jnl`	Large language models for causal hypothesis generation in science. Mach. Learn. Sci. Technol. Kai-Hendrik Cohrs, Emiliano Diaz, Vasileios Sitokonstantinou, Gherardo Varando, Gustau Camps-Valls
2025	A	`conf`	Learning Causal Response Representations through Direct Effect Analysis. UAI Homer Durand, Gherardo Varando, Gustau Camps-Valls
2025	J	`jnl`	Learning Causal Response Representations through Direct Effect Analysis. CoRR Homer Durand, Gherardo Varando, Gustau Camps-Valls
2025	A	`conf`	Out-of-distribution robustness for multivariate analysis via causal regularisation. AISTATS Homer Durand, Gherardo Varando, Nathan Mankovich, Gustau Camps-Valls
2024	J	`jnl`	Causal hybrid modeling with double machine learning - applications in carbon flux modeling. Mach. Learn. Sci. Technol. Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, Gustau Camps-Valls
2024	—	`conf`	Context-Specific Refinements of Bayesian Network Classifiers. PGM Manuele Leonelli, Gherardo Varando
2024	J	`jnl`	Context-Specific Refinements of Bayesian Network Classifiers. CoRR Manuele Leonelli, Gherardo Varando
2024	J	`jnl`	Double machine learning for causal hybrid modeling - applications in the Earth sciences. CoRR Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, Gustau Camps-Valls
2024	J	`jnl`	Improving generalisation via anchor multivariate analysis. CoRR Homer Durand, Gherardo Varando, Nathan Mankovich, Gustau Camps-Valls
2024	J	`jnl`	Large Language Models for Constrained-Based Causal Discovery. CoRR Kai-Hendrik Cohrs, Gherardo Varando, Emiliano Diaz, Vasileios Sitokonstantinou, Gustau Camps-Valls
2024	—	`conf`	Learning Staged Trees from Incomplete Data. PGM Jack Storror Carter, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
2024	J	`jnl`	Learning Staged Trees from Incomplete Data. CoRR Jack Storror Carter, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
2024	J	`jnl`	Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear. Appl. Intell. Manuele Leonelli, Gherardo Varando
2024	J	`jnl`	Pairwise causal discovery with support measure machines. Appl. Soft Comput. Gherardo Varando, Salvador Catsis, Emiliano Diaz, Gustau Camps-Valls
2024	J	`jnl`	Recovering Latent Confounders from High-dimensional Proxy Variables. CoRR Nathan Mankovich, Homer Durand, Emiliano Diaz, Gherardo Varando, Gustau Camps-Valls
2024	J	`jnl`	Structural learning of simple staged trees. Data Min. Knowl. Discov. Manuele Leonelli, Gherardo Varando
2023	J	`jnl`	A new class of generative classifiers based on staged tree models. Knowl. Based Syst. Federico Carli, Manuele Leonelli, Gherardo Varando
2023	A	`conf`	Context-Specific Causal Discovery for Categorical Data Using Staged Trees. AISTATS Manuele Leonelli, Gherardo Varando
2023	J	`jnl`	Discovering Causal Relations and Equations from Data. CoRR Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
2023	J	`jnl`	Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear. CoRR Manuele Leonelli, Gherardo Varando
2023	J	`jnl`	Learning latent functions for causal discovery. Mach. Learn. Sci. Technol. Emiliano Diaz, Gherardo Varando, Juan Emmanuel Johnson, Gustau Camps-Valls
2022	—	`conf`	Highly Efficient Structural Learning of Sparse Staged Trees. PGM Manuele Leonelli, Gherardo Varando
2022	J	`jnl`	Highly Efficient Structural Learning of Sparse Staged Trees. CoRR Manuele Leonelli, Gherardo Varando
2022	J	`jnl`	Structural Learning of Simple Staged Trees. CoRR Manuele Leonelli, Gherardo Varando
2022	J	`jnl`	The J. Stat. Softw. Federico Carli, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
2021	J	`jnl`	Context-Specific Causal Discovery for Categorical Data Using Staged Trees. CoRR Manuele Leonelli, Gherardo Varando
2021	J	`jnl`	Staged trees and asymmetry-labeled DAGs. CoRR Gherardo Varando, Federico Carli, Manuele Leonelli
2020	J	`jnl`	A new class of generative classifiers based on staged tree models. CoRR Federico Carli, Manuele Leonelli, Gherardo Varando
2020	J	`jnl`	Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values. CoRR Sebastian Weichwald, Martin Emil Jakobsen, Phillip B. Mogensen, Lasse Petersen, Nikolaj Thams, Gherardo Varando
2020	A	`conf`	Graphical continuous Lyapunov models. UAI Gherardo Varando, Niels Richard Hansen
2020	J	`jnl`	Graphical continuous Lyapunov models. CoRR Gherardo Varando, Niels Richard Hansen
2020	J	`jnl`	Learning DAGs without imposing acyclicity. CoRR Gherardo Varando
2020	J	`jnl`	On generating random Gaussian graphical models. Int. J. Approx. Reason. Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga
2020	J	`jnl`	Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data. IEEE Access Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando
2020	J	`jnl`	Sparse Cholesky covariance parametrization for recovering latent structure in ordered data. CoRR Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando
2019	—	`conf`	Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values. NeurIPS (Competition and Demos) Sebastian Weichwald, Martin Emil Jakobsen, Phillip B. Mogensen, Lasse Petersen, Nikolaj Thams, Gherardo Varando
2018	—	`conf`	A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. IDEAL (1) Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga
2018	—	`conf`	A partial orthogonalization method for simulating covariance and concentration graph matrices. PGM Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga
2018	J	`jnl`	Markov Property in Generative Classifiers. CoRR Gherardo Varando, Concha Bielza, Pedro Larrañaga, Eva Riccomagno
2018	—	`—`	Theoretical studies on Bayesian network classifiers. Gherardo Varando
2016	J	`jnl`	Decision functions for chain classifiers based on Bayesian networks for multi-label classification. Int. J. Approx. Reason. Gherardo Varando, Concha Bielza, Pedro Larrañaga
2015	J	`jnl`	A survey on multi-output regression. WIREs Data Mining Knowl. Discov. Hanen Borchani, Gherardo Varando, Concha Bielza, Pedro Larrañaga
2015	J	`jnl`	Conditional Density Approximations with Mixtures of Polynomials. Int. J. Intell. Syst. Gherardo Varando, Pedro L. López-Cruz, Thomas D. Nielsen, Pedro Larrañaga, Concha Bielza
2015	J	`jnl`	Decision boundary for discrete Bayesian network classifiers. J. Mach. Learn. Res. Gherardo Varando, Concha Bielza, Pedro Larrañaga
2014	—	`conf`	Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification. Probabilistic Graphical Models Gherardo Varando, Concha Bielza, Pedro Larrañaga