Efficient Generalization with Distributionally Robust LearningDownload PDF

Published: 09 Nov 2021, Last Modified: 05 May 2023NeurIPS 2021 PosterReaders: Everyone
Keywords: distributionally robust learning, distributionally robust optimization, generalization, robust learning
TL;DR: We propose and analyze an efficient method for solving DRL, and contrast with recent literature to show where our method is superior
Abstract: Distributionally robust learning (DRL) is increasingly seen as a viable method to train machine learning models for improved model generalization. These min-max formulations, however, are more difficult to solve. We provide a new stochastic gradient descent algorithm to efficiently solve this DRL formulation. Our approach applies gradient descent to the outer minimization formulation and estimates the gradient of the inner maximization based on a sample average approximation. The latter uses a subset of the data sampled without replacement in each iteration, progressively increasing the subset size to ensure convergence. We rigorously establish convergence to a near-optimal solution under standard regularity assumptions and, for strongly convex losses, match the best known $O(\epsilon{ −1})$ rate of convergence up to a known threshold. Empirical results demonstrate the significant benefits of our approach over previous work in improving learning for model generalization.
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