Graph partition strategies for generalized mean field inference

1 Graph partition strategies for generalized Eric P. Xing Computer Science Division University of California Berkeley, CA 94720 Abstract Michael I. Jordan Computer Science and Statistics University of California Berkeley, CA 94720 An autonomous variational inference algorithm for arbitrary graphical models requires the ability to optimize variational approximations over the space of model parameters as well as over the choice of tractable families used for the variational approximation. In this paper, we present a novel combination of graph partitioning algorithms with a generalized mean field (GMF) inference algorithm. Thiscombination optimizes over disjoint clustering of variables and performs inference using those clusters.We provide a formal analysis of the relationship between the graph cut and the GMF approximation, and explore several graph partition strategies empirically. Ourempirical results provide rather clear support for a weighted version of MinCut as a useful clustering algorithm for GMF inference, which is consistent with the implications from the formal analysis. Introduction What are the prospects for fully autonomous algorithms for variational inference in graphical models? Recentyears have seen an increasingly systematic treatment of an increasingly flexible range of algorithms for variational inference.