Variational Rejection Sampling
Abstract
Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the gradient estimates. We propose a novel rejection sampling step that discards samples from the variational posterior which are assigned low likelihoods by the model. Our approach provides an arbitrarily accurate approximation of the true posterior at the expense of extra computation. Using a new gradient estimator for the resulting unnormalized proposal distribution, we achieve average improvements of 3.71 nats and 0.21 nats over state-of-the-art single-sample and multi-sample alternatives respectively for estimating marginal log-likelihoods using sigmoid belief networks on the MNIST dataset.
Cite
@article{arxiv.1804.01712,
title = {Variational Rejection Sampling},
author = {Aditya Grover and Ramki Gummadi and Miguel Lazaro-Gredilla and Dale Schuurmans and Stefano Ermon},
journal= {arXiv preprint arXiv:1804.01712},
year = {2018}
}
Comments
AISTATS 2018