Learning Bayesian Networks with Thousands of Variables

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

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    Abstract

    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)
    PublisherNIPS Foundation, Inc
    Pages1855-1863
    Number of pages9
    Publication statusPublished - Dec 2015

    Bibliographical note

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

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