Continuous Relaxations for Discrete Hamiltonian Monte Carlo

author: Yichuan Zhang, School of Informatics, University of Edinburgh
published: Jan. 14, 2013,   recorded: December 2012,   views: 2935
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Description

Continuous relaxations play an important role in discrete optimization, but have not seen much use in approximate probabilistic inference. Here we show that a general form of the Gaussian Integral Trick makes it possible to transform a wide class of discrete variable undirected models into fully continuous systems. The continuous representation allows the use of gradient-based Hamiltonian Monte Carlo for inference, results in new ways of estimating normalization constants (partition functions), and in general opens up a number of new avenues for inference in difficult discrete systems. We demonstrate some of these continuous relaxation inference algorithms on a number of illustrative problems.

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Download slides icon Download slides: machine_zhang_continuous_relaxations_01.pdf (2.0 MB)

Download article icon Download article: machine_zhang_continuous_relaxations_01.pdf (399.1 KB)


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