Wasserstein Distributionally Robust Optimization and Variation Regularization

Abstract

This paper builds a bridge between two area in optimization and machine learning by establishing a general connection between Wasserstein distributional robustness and variation regularization. It helps to demystify the empirical success of Wasserstein distributionally robust optimization and devise new regularization schemes for machine learning.

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