PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Convexifying the Bethe Free Energy
Ofer Meshi, Ariel Jaimovich, Amir Globerson and Nir Friedman
Proceedings of Uncertainty in Artificial Intelligence (UAI) 2009.

Abstract

The introduction of loopy belief propagation (LBP) revitalized the application of graphical models in many domains. Many recent works present improvements on the basic LBP algorithm in an attempt to overcome convergence and local optima problems. Notable among these are convexified free energy approximations that lead to inference procedures with provable convergence and quality properties. However, empirically LBP still outperforms most of its convex variants in a variety of settings, as we also demonstrate here. Motivated by this fact we seek convexified free energies that directly approximate the Bethe free energy. We show that the proposed approximations compare favorably with state-of-the art convex free energy approximations.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5413
Deposited By:Amir Globerson
Deposited On:23 July 2009