Efficiently searching for frustrated cycles in MAP inference

David Sontag, Do Kook Choe, Yitao Li

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

Abstract

Dual decomposition provides a tractable framework for designing algorithms for finding the most probable (MAP) configuration in graphical models. However, for many real-world inference problems, the typical decomposition has a large integrality gap, due to frustrated cycles. One way to tighten the relaxation is to introduce additional constraints that explicitly enforce cycle consistency. Earlier work showed that cluster-pursuit algorithms, which iteratively introduce cycle and other higherorder consistency constraints, allows one to exactly solve many hard inference problems. However, these algorithms explicitly enumerate a candidate set of clusters, limiting them to triplets or other short cycles. We solve the search problem for cycle constraints, giving a nearly linear time algorithm for finding the most frustrated cycle of arbitrary length. We show how to use this search algorithm together with the dual decomposition framework and clusterpursuit. The new algorithm exactly solves MAP inference problems arising from relational classification and stereo vision.

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012
Pages795-804
Number of pages10
StatePublished - 2012
Event28th Conference on Uncertainty in Artificial Intelligence, UAI 2012 - Catalina Island, CA, United States
Duration: Aug 15 2012Aug 17 2012

Other

Other28th Conference on Uncertainty in Artificial Intelligence, UAI 2012
CountryUnited States
CityCatalina Island, CA
Period8/15/128/17/12

Fingerprint

Decomposition
Stereo vision

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Sontag, D., Choe, D. K., & Li, Y. (2012). Efficiently searching for frustrated cycles in MAP inference. In Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012 (pp. 795-804)

Efficiently searching for frustrated cycles in MAP inference. / Sontag, David; Choe, Do Kook; Li, Yitao.

Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012. 2012. p. 795-804.

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

Sontag, D, Choe, DK & Li, Y 2012, Efficiently searching for frustrated cycles in MAP inference. in Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012. pp. 795-804, 28th Conference on Uncertainty in Artificial Intelligence, UAI 2012, Catalina Island, CA, United States, 8/15/12.
Sontag D, Choe DK, Li Y. Efficiently searching for frustrated cycles in MAP inference. In Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012. 2012. p. 795-804
Sontag, David ; Choe, Do Kook ; Li, Yitao. / Efficiently searching for frustrated cycles in MAP inference. Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012. 2012. pp. 795-804
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