Gherardo Varando

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 33

Top Topics

Decision Functions

Structural Learning

Gaussian Graphical Models

Bayesian Network Classifiers

Top Venues

Int. J. Approx. Reason.

Publications

Homer Durand, Gherardo Varando, Nathan Mankovich, Gustau Camps-Valls
Improving generalisation via anchor multivariate analysis. CoRR (2024)
Kai-Hendrik Cohrs, Gherardo Varando, Emiliano Diaz, Vasileios Sitokonstantinou, Gustau Camps-Valls
Large Language Models for Constrained-Based Causal Discovery. CoRR (2024)
Manuele Leonelli, Gherardo Varando
Context-Specific Refinements of Bayesian Network Classifiers. CoRR (2024)
Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, Gustau Camps-Valls
Double machine learning for causal hybrid modeling - applications in the Earth sciences. CoRR (2024)
Manuele Leonelli, Gherardo Varando
Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear. Appl. Intell. 54 (2) (2024)
Manuele Leonelli, Gherardo Varando
Structural learning of simple staged trees. Data Min. Knowl. Discov. 38 (3) (2024)
Gherardo Varando, Salvador Catsis, Emiliano Diaz, Gustau Camps-Valls
Pairwise causal discovery with support measure machines. Appl. Soft Comput. 150 (2024)
Nathan Mankovich, Homer Durand, Emiliano Diaz, Gherardo Varando, Gustau Camps-Valls
Recovering Latent Confounders from High-dimensional Proxy Variables. CoRR (2024)
Jack Storror Carter, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
Learning Staged Trees from Incomplete Data. CoRR (2024)
Manuele Leonelli, Gherardo Varando
Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear. CoRR (2023)
Emiliano Diaz, Gherardo Varando, Juan Emmanuel Johnson, Gustau Camps-Valls
Learning latent functions for causal discovery. Mach. Learn. Sci. Technol. 4 (3) (2023)
Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
Discovering Causal Relations and Equations from Data. CoRR (2023)
Manuele Leonelli, Gherardo Varando
Context-Specific Causal Discovery for Categorical Data Using Staged Trees. AISTATS (2023)
Federico Carli, Manuele Leonelli, Gherardo Varando
A new class of generative classifiers based on staged tree models. Knowl. Based Syst. 268 (2023)
Manuele Leonelli, Gherardo Varando
Structural Learning of Simple Staged Trees. CoRR (2022)
Federico Carli, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
Package stagedtrees for Structural Learning of Stratified Staged Trees. J. Stat. Softw. 102 (6) (2022)
Manuele Leonelli, Gherardo Varando
Highly Efficient Structural Learning of Sparse Staged Trees. CoRR (2022)
Manuele Leonelli, Gherardo Varando
Highly Efficient Structural Learning of Sparse Staged Trees. PGM (2022)
Manuele Leonelli, Gherardo Varando
Context-Specific Causal Discovery for Categorical Data Using Staged Trees. CoRR (2021)
Gherardo Varando, Federico Carli, Manuele Leonelli
Staged trees and asymmetry-labeled DAGs. CoRR (2021)
Federico Carli, Manuele Leonelli, Gherardo Varando
A new class of generative classifiers based on staged tree models. CoRR (2020)
Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando
Sparse Cholesky covariance parametrization for recovering latent structure in ordered data. CoRR (2020)
Gherardo Varando
Learning DAGs without imposing acyclicity. CoRR (2020)
Gherardo Varando, Niels Richard Hansen
Graphical continuous Lyapunov models. UAI (2020)
Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga
On generating random Gaussian graphical models. Int. J. Approx. Reason. 125 (2020)
Sebastian Weichwald, Martin E. Jakobsen, Phillip B. Mogensen, Lasse Petersen, Nikolaj Thams, Gherardo Varando
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values. CoRR (2020)
Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando
Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data. IEEE Access 8 (2020)
Gherardo Varando, Niels Richard Hansen
Graphical continuous Lyapunov models. CoRR (2020)
Sebastian Weichwald, Martin E. Jakobsen, Phillip B. Mogensen, Lasse Petersen, Nikolaj Thams, Gherardo Varando
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values. NeurIPS (Competition and Demos) (2019)
Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga
A partial orthogonalization method for simulating covariance and concentration graph matrices. PGM (2018)
Gherardo Varando, Concha Bielza, Pedro Larrañaga, Eva Riccomagno
Markov Property in Generative Classifiers. CoRR (2018)
Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga
A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. IDEAL (1) (2018)
Gherardo Varando, Concha Bielza, Pedro Larrañaga
Decision functions for chain classifiers based on Bayesian networks for multi-label classification. Int. J. Approx. Reason. 68 (2016)
Gherardo Varando, Concha Bielza, Pedro Larrañaga
Decision boundary for discrete Bayesian network classifiers. J. Mach. Learn. Res. 16 (2015)
Gherardo Varando, Pedro L. López-Cruz, Thomas D. Nielsen, Pedro Larrañaga, Concha Bielza
Conditional Density Approximations with Mixtures of Polynomials. Int. J. Intell. Syst. 30 (3) (2015)
Hanen Borchani, Gherardo Varando, Concha Bielza, Pedro Larrañaga
A survey on multi-output regression. WIREs Data Mining Knowl. Discov. 5 (5) (2015)
Gherardo Varando, Concha Bielza, Pedro Larrañaga
Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification. Probabilistic Graphical Models (2014)