Adversarially-learned inference via an ensemble of discrete undirected graphical models

Abstract

Undirected graphical models are compact representations of joint probability distributions over random variables. To carry out an inference task of interest, graphical models of arbitrary topology can be trained using empirical risk minimization. However, when faced with new tasks, these models (EGMs) often need to be re-trained. Instead, we propose an inference-agnostic adversarial training framework for producing an ensemble of graphical models (AGMs). The ensemble is optimized to generate data, and inference is learned as a by-product of this endeavor. AGMs perform comparably with EGMs on inference tasks that the latter were specifically optimized for. Most importantly, AGMs show significantly better generalization capabilities across inference tasks. AGMs are also on par with GibbsNet, a state-of-the-art deep neural architecture, which like AGMs, allows conditioning on any subset of random variables. Finally, AGMs allow fast data sampling, competitive with Gibbs sampling from EGMs.