From: NeurIPS.cc/2022/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: This paper proposes a method that uses unlabeled data to learn a generative model of the Bayesian posterior distribution and shows that it can accurately model epistemic uncertainty for out-of-distribution data. This is a relevant topic and contribution to the workshop and thus is recommended for acceptance.