Anytime Marginal MAP Inference

D. D. Mauá, C. P. de Campos

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

Abstract

This paper presents a new anytime algorithm for the marginal MAP problem in graphical models of bounded treewidth. We show asymptotic convergence and theoretical error bounds for any fixed step. Experiments show that it compares well to a state-of-the-art systematic search algorithm.
Original languageEnglish
Title of host publicationInternational Conference on Machine Learning (ICML)
Place of PublicationNew York, NY, USA
PublisherOmnipress
Pages1471-1478
Number of pages8
Publication statusPublished - 2012

Bibliographical note

(acc.rate 27%, oral presentation, double-blind peer reviewed by >3 reviewers)

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