Lifted tree-reweighted variational inference

Hung Hai Bui, Tuyen N. Huynh, David Sontag

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

Abstract

We analyze variational inference for highly symmetric graphical models such as those arising from first-order probabilistic models. We first show that for these graphical models, the treereweighted variational objective lends itself to a compact lifted formulation which can be solved much more efficiently than the standard TRW formulation for the ground graphical model. Compared to earlier work on lifted belief propagation, our formulation leads to a convex optimization problem for lifted marginal inference and provides an upper bound on the partition function. We provide two approaches for improving the lifted TRW upper bound. The first is a method for efficiently computing maximum spanning trees in highly symmetric graphs, which can be used to optimize the TRW edge appearance probabilities. The second is a method for tightening the relaxation of the marginal polytope using lifted cycle inequalities and novel exchangeable cluster consistency constraints.

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014
EditorsNevin L. Zhang, Jin Tian
PublisherAUAI Press
Pages92-101
Number of pages10
ISBN (Electronic)9780974903910
StatePublished - Jan 1 2014
Event30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 - Quebec City, Canada
Duration: Jul 23 2014Jul 27 2014

Publication series

NameUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014

Other

Other30th Conference on Uncertainty in Artificial Intelligence, UAI 2014
CountryCanada
CityQuebec City
Period7/23/147/27/14

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Bui, H. H., Huynh, T. N., & Sontag, D. (2014). Lifted tree-reweighted variational inference. In N. L. Zhang, & J. Tian (Eds.), Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014 (pp. 92-101). (Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014). AUAI Press.