Learning Bayesian networks with thousands of variables

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

31 Citations (Scopus)
1 Downloads (Pure)


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 publicationNIPS'15 Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2
Place of PublicationCambridge
PublisherMIT Press
Number of pages9
Publication statusPublished - 2015
Externally publishedYes
Event29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada
Duration: 7 Dec 201512 Dec 2015

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference29th Annual Conference on Neural Information Processing Systems, NIPS 2015

Bibliographical note

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


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