Related papers: Annealed Stein Variational Gradient Descent

Annealed Stein Variational Gradient Descent

URL: http://arxiv.org/abs/2101.09815v2
Date: Mon, 8 Feb 2021 10:19:25 GMT
Title: Annealed Stein Variational Gradient Descent
Authors: Francesco D'Angelo, Vincent Fortuin
Abstract summary: Stein variational gradient descent has gained attention in the approximate literature inference for its flexibility and accuracy. We empirically explore the ability of this method to sample from multi-modal distributions and focus on two important issues: (i) the inability of the particles to escape from local modes and (ii) the inefficacy in reproducing the density of the different regions.
Score: 4.020523898765405
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Particle based optimization algorithms have recently been developed as sampling methods that iteratively update a set of particles to approximate a target distribution. In particular Stein variational gradient descent has gained attention in the approximate inference literature for its flexibility and accuracy. We empirically explore the ability of this method to sample from multi-modal distributions and focus on two important issues: (i) the inability of the particles to escape from local modes and (ii) the inefficacy in reproducing the density of the different regions. We propose an annealing schedule to solve these issues and show, through various experiments, how this simple solution leads to significant improvements in mode coverage, without invalidating any theoretical properties of the original algorithm.

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