Learning Bayesian Networks with Thousands of Variables

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

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

25 Citations (Scopus)


We present a method for learning Bayesian networks from data sets containing thousands of variables without the need for structure constraints. Our approach is made of two parts. The first is a novel algorithm that effectively explores the space of possible parent sets of a node. It guides the exploration towards the most promising parent sets on the basis of an approximated score function that is computed in constant time. The second part is an improvement of an existing ordering-based algorithm for structure optimization. The new algorithm provably achieves a higher score compared to its original formulation. Our novel approach consistently outperforms the state of the art on very large data sets.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 28 (NIPS 2015)
EditorsC. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett
PublisherNIPS Foundation, Inc
Number of pages9
Publication statusPublished - Dec 2015
EventNIPS 2015 - Montreal, Canada
Duration: 07 Dec 201512 Dec 2015


ConferenceNIPS 2015

Bibliographical note

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

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    Scanagatta, M., de Campos, C. P., Corani, G., & Zaffalon, M. (2015). Learning Bayesian Networks with Thousands of Variables. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 1855-1863). NIPS Foundation, Inc. http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables.pdf