Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

Mauro Scanagatta, Giorgio Corani, Cassio P de Campos, Marco Zaffalon

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Abstract

We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 29 (NIPS 2016)
PublisherNIPS Foundation, Inc
Publication statusAccepted - 12 Aug 2016
EventAnnual Conference on Neural Information Processing Systems - Centre Convencions Internacional Barcelona, Barcelona, Spain
Duration: 05 Dec 201610 Dec 2016
http://nips.cc

Conference

ConferenceAnnual Conference on Neural Information Processing Systems
Abbreviated titleNIPS 2016
CountrySpain
CityBarcelona
Period05/12/201610/12/2016
Internet address

Bibliographical note

Double blind peer reviewed by multiple reviewers. Acc. rate 22%.

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    Scanagatta, M., Corani, G., de Campos, C. P., & Zaffalon, M. (Accepted/In press). Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables. In Advances in Neural Information Processing Systems 29 (NIPS 2016) NIPS Foundation, Inc.