Stochastic Gradient MCMC with Repulsive Forces

Victor Gallego; David Rios Insua

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arXiv · 1812.00071

Stochastic Gradient MCMC with Repulsive Forces

Victor Gallego·David Rios Insua

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Abstract

We propose a unifying view of two different Bayesian inference algorithms, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) and Stein Variational Gradient Descent (SVGD), leading to improved and efficient novel sampling schemes. We show that SVGD combined with a noise term can be framed as a multiple chain SG-MCMC method. Instead of treating each parallel chain independently from others, our proposed algorithm implements a repulsive force between particles, avoiding collapse and facilitating a better exploration of the parameter space. We also show how the addition of this noise term is necessary to obtain a valid SG-MCMC sampler, a significant difference with SVGD. Experiments with both synthetic distributions and real datasets illustrate the benefits of the proposed scheme.

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BibTeX RIS

Victor Gallego, David Rios Insua. 2018-11-30. Stochastic Gradient MCMC with Repulsive Forces. https://arxiv.org/abs/1812.00071

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