Global Sensitivity Analysis for MAP Inference in Graphical Models

Jasper De Bock, Cassio P. de Campos, Alessandro Antonucci

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

15 Citations (Scopus)

Abstract

We study the sensitivity of a MAP configuration of a discrete probabilistic graphical model with respect to perturbations of its parameters. These perturbations are global, in the sense that simultaneous perturbations of all the parameters (or any chosen subset of them) are allowed. Our main contribution is an exact algorithm that can check whether the MAP configuration is robust with respect to given perturbations. Its complexity is essentially the same as that of obtaining the MAP configuration itself, so it can be promptly used with minimal effort. We use our algorithm to identify the largest global perturbation that does not induce a change in the MAP configuration, and we successfully apply this robustness measure in two practical scenarios: the prediction of facial action units with posed images and the classification of multiple real public data sets. A strong correlation between the proposed robustness measure and accuracy is verified in both scenarios.
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.
Pages2690-2698
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
Country/TerritoryCanada
CityMontreal
Period08/12/201413/12/2014

Bibliographical note

(acc. rate 24%, double-blind peer reviewed by >3 reviewers)

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