| A survey on multi‐output regression H Borchani, G Varando, C Bielza, P Larranaga Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5 (5 …, 2015 | 551 | 2015 |
| Decision boundary for discrete Bayesian network classifiers G Varando, MC Bielza Lozoya, PM Larrañaga Múgica Journal of Machine Learning Research, 2725-2749, 2015 | 26 | 2015 |
| The R package stagedtrees for structural learning of stratified staged trees F Carli, M Leonelli, E Riccomagno, G Varando Journal of Statistical Software 102 (6), 1-30, 2022 | 21 | 2022 |
| Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values S Weichwald, ME Jakobsen, PB Mogensen, L Petersen, N Thams, ... https://arxiv.org/abs/2002.09573, 2020 | 17 | 2020 |
| Decision functions for chain classifiers based on Bayesian networks for multi-label classification G Varando, C Bielza, P Larrañaga International Journal of Approximate Reasoning 68, 164-178, 2016 | 15 | 2016 |
| Graphical continuous Lyapunov models G Varando, NR Hansen Conference on Uncertainty in Artificial Intelligence, 989-998, 2020 | 10 | 2020 |
| Expressive power of binary relevance and chain classifiers based on Bayesian networks for multi-label classification G Varando, C Bielza, P Larranaga Probabilistic Graphical Models: 7th European Workshop, PGM 2014, Utrecht …, 2014 | 10 | 2014 |
| MultiMap: a tool to automatically extract and analyse spatial microscopic data from large stacks of confocal microscopy images G Varando, R Benavides-Piccione, A Muñoz, A Kastanauskaite, C Bielza, ... Frontiers in Neuroanatomy 12, 37, 2018 | 9 | 2018 |
| On generating random Gaussian graphical models I Córdoba, G Varando, C Bielza, P Larrañaga International Journal of Approximate Reasoning 125, 240-250, 2020 | 8 | 2020 |
| Learning DAGs without imposing acyclicity G Varando arXiv preprint arXiv:2006.03005, 2020 | 8 | 2020 |
| Context-specific causal discovery for categorical data using staged trees M Leonelli, G Varando International Conference on Artificial Intelligence and Statistics, 8871-8888, 2023 | 7 | 2023 |
| Structural learning of simple staged trees M Leonelli, G Varando arXiv preprint arXiv:2203.04390, 2022 | 5 | 2022 |
| Staged trees and asymmetry-labeled DAGs G Varando, F Carli, M Leonelli arXiv preprint arXiv:2108.01994, 2021 | 5 | 2021 |
| A fast Metropolis-Hastings method for generating random correlation matrices I Córdoba, G Varando, C Bielza, P Larrañaga Intelligent Data Engineering and Automated Learning–IDEAL 2018: 19th …, 2018 | 5 | 2018 |
| Conditional density approximations with mixtures of polynomials G Varando, PL López‐Cruz, TD Nielsen, P Larranaga, C Bielza International Journal of Intelligent Systems 30 (3), 236-264, 2015 | 5 | 2015 |
| Highly efficient structural learning of sparse staged trees M Leonelli, G Varando International Conference on Probabilistic Graphical Models, 193-204, 2022 | 4 | 2022 |
| Sparse Cholesky covariance parametrization for recovering latent structure in ordered data I Córdoba, C Bielza, P Larrañaga, G Varando IEEE Access 8, 154614-154624, 2020 | 4 | 2020 |
| A new class of generative classifiers based on staged tree models F Carli, M Leonelli, G Varando Knowledge-Based Systems 268, 110488, 2023 | 3 | 2023 |
| A partial orthogonalization method for simulating covariance and concentration graph matrices I Córdoba, G Varando, C Bielza, P Larrañaga International Conference on Probabilistic Graphical Models, 61-72, 2018 | 3 | 2018 |
| Learning Granger Causal Feature Representations G Varando, MA Fernández-Torres, G Camps-Valls | 2 | 2021 |
Pedro LarrañagaProfessor of Artificial Intelligence - Technical University of MadridVerified email at fi.upm.es
