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In many real-world problems, there is a limited set of training data, but anabundance of unlabeled data. We propose a new method, Generative PosteriorNetworks (GPNs), that uses unlabeled data to estimate epistemic uncertainty inhigh-dimensional problems. A GPN is a generative model that, given a priordistribution over functions, approximates the posterior distribution directlyby regularizing the network towards samples from the prior. We provetheoretically that our method indeed approximates the Bayesian posterior andshow empirically that it improves epistemic uncertainty estimation andscalability over competing methods.

Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation