**arXiv ID:** 1804.01712 **Authors:** Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon **Published:** 2018-04-05T07:53:41Z **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 likelihoo...
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# Variational Rejection Sampling
**arXiv ID:** 1804.01712
**Authors:** Aditya Grover, Ramki Gummadi, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon
**Published:** 2018-04-05T07:53:41Z
**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.
## Skill Description
This skill is generated from the arXiv paper: Variational Rejection Sampling (1804.01712).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:1804.01712](http://arxiv.org/abs/1804.01712v1)
