| A survey on Bayesian network structure learning from data M Scanagatta, A Salmerón, F Stella Progress in Artificial Intelligence 8 (4), 425-439, 2019 | 239 | 2019 |
| Learning Bayesian networks with thousands of variables M Scanagatta, CP de Campos, G Corani, M Zaffalon Advances in neural information processing systems 28, 2015 | 139 | 2015 |
| Air pollution prediction via multi-label classification G Corani, M Scanagatta Environmental modelling & software 80, 259-264, 2016 | 84 | 2016 |
| A gamification platform to analyze and influence citizens’ daily transportation choices R Kazhamiakin, E Loria, A Marconi, M Scanagatta IEEE Transactions on Intelligent Transportation Systems 22 (4), 2153-2167, 2021 | 53 | 2021 |
| Learning treewidth-bounded Bayesian networks with thousands of variables M Scanagatta, G Corani, CP De Campos, M Zaffalon Advances in neural information processing systems 29, 2016 | 52 | 2016 |
| Approximate structure learning for large Bayesian networks M Scanagatta, G Corani, CP De Campos, M Zaffalon Machine Learning 107, 1209-1227, 2018 | 47 | 2018 |
| Entropy-based pruning for learning Bayesian networks using BIC CP de Campos, M Scanagatta, G Corani, M Zaffalon Artificial Intelligence 260, 42-50, 2018 | 47 | 2018 |
| Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets M Scanagatta, G Corani, M Zaffalon, J Yoo, U Kang International Journal of Approximate Reasoning 95, 152-166, 2018 | 41 | 2018 |
| Improved local search in Bayesian networks structure learning M Scanagatta, G Corani, M Zaffalon Advanced methodologies for Bayesian networks, 45-56, 2017 | 26 | 2017 |
| Learning extended tree augmented naive structures CP de Campos, G Corani, M Scanagatta, M Cuccu, M Zaffalon International Journal of Approximate Reasoning 68, 153-163, 2016 | 24 | 2016 |
| Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference J Yoo, U Kang, M Scanagatta, G Corani, M Zaffalon Proceedings of the 13th International Conference on Web Search and Data …, 2020 | 14 | 2020 |
| Min-BDeu and max-BDeu scores for learning Bayesian networks M Scanagatta, CP de Campos, M Zaffalon Probabilistic Graphical Models: 7th European Workshop, PGM 2014, Utrecht …, 2014 | 12 | 2014 |
| Early classification of time series by hidden markov models with set-valued parameters A Antonucci, M Scanagatta, DD Mauá, CP de Campos Proceedings of the NIPS time series workshop, 1-5, 2015 | 9 | 2015 |
| Calibration of game dynamics for a more even multi-player experience M Scanagatta, M Ferron, G Deppieri, A Marconi Proceedings of the 25th International Conference on Intelligent User …, 2020 | 5 | 2020 |
| Learning Bounded Treewidth Bayesian Networks with Thousands of Variables M Scanagatta, G Corani, CP de Campos, M Zaffalon arXiv preprint arXiv:1605.03392, 2016 | 2 | 2016 |
| Advancements in Bayesian network structure learning M Scanagatta Università della Svizzera Italiana, 2018 | 1 | 2018 |
| Automatic Calibration for a Mutual Insurance System in a Multi-Player Serious Game M Scanagatta, A Marconi European Conference on Games Based Learning 16 (1), 508-517, 2022 | | 2022 |
