Decision-Making with Auto-Encoding Variational Bayes

TL;DR

This paper investigates how different approximate posterior distributions, beyond the standard variational distribution, can improve decision-making in models fitted with auto-encoding variational Bayes, especially in complex applications like single-cell RNA sequencing.

Contribution

It introduces a method for learning multiple approximate proposals and combining them via importance sampling to enhance decision quality in variational Bayes models.

Findings

Using alternative objective functions improves decision accuracy.

Multiple importance sampling outperforms single proposals in complex tasks.

Proposed approach surpasses state-of-the-art in single-cell RNA sequencing analysis.

Abstract

To make decisions based on a model fit with auto-encoding variational Bayes (AEVB), practitioners often let the variational distribution serve as a surrogate for the posterior distribution. This approach yields biased estimates of the expected risk, and therefore leads to poor decisions for two reasons. First, the model fit with AEVB may not equal the underlying data distribution. Second, the variational distribution may not equal the posterior distribution under the fitted model. We explore how fitting the variational distribution based on several objective functions other than the ELBO, while continuing to fit the generative model based on the ELBO, affects the quality of downstream decisions. For the probabilistic principal component analysis model, we investigate how importance sampling error, as well as the bias of the model parameter estimates, varies across several approximate posteriors when used as proposal distributions. Our theoretical results suggest that a posterior approximation distinct from the variational distribution should be used for making decisions. Motivated by these theoretical results, we propose learning several approximate proposals for the best model and combining them using multiple importance sampling for decision-making. In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing. In this challenging instance of multiple hypothesis testing, our proposed approach surpasses the current state of the art.