Robust Generalization despite Distribution Shift via Minimum Discriminating InformationDownload PDF

21 May 2021, 20:47 (modified: 17 Dec 2021, 17:26)NeurIPS 2021 PosterReaders: Everyone
Keywords: Distributionally robust optimization, distribution shift, stochastic programming, large deviations
TL;DR: Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Abstract: Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to training data, we have partial structural knowledge of the shifted test distribution. We employ the principle of minimum discriminating information to embed the available prior knowledge, and use distributionally robust optimization to account for uncertainty due to the limited samples. By leveraging large deviation results, we obtain explicit generalization bounds with respect to the unknown shifted distribution. Lastly, we demonstrate the versatility of our framework by demonstrating it on two rather distinct applications: (1) training classifiers on systematically biased data and (2) off-policy evaluation in Markov Decision Processes.
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