Advances in Learning Bayesian Networks of Bounded Treewidth

Siqi Nie, Denis D. Maua, Cassio P. de Campos, Qiang Ji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 27: 28th Annual Conference on Neural Information Processing Systems 2014
EditorsZ. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger
Place of PublicationNew York
PublisherCurran Associates, Inc.
Pages2285-2293
Number of pages9
Volume3
Publication statusPublished - Jan 2014
Event28th Annual Conference on Neural Information Processing Systems 2014 - Montreal, Canada
Duration: 08 Dec 201413 Dec 2014

Conference

Conference28th Annual Conference on Neural Information Processing Systems 2014
CountryCanada
CityMontreal
Period08/12/201413/12/2014

Bibliographical note

(top 4%, spotlight presentation, double-blind peer reviewed by >3 reviewers)

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