Efficient Stochastic Gradient Descent for Distributionally Robust Learning

05/22/2018

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by   Soumyadip Ghosh, et al.

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We consider a new stochastic gradient descent algorithm for efficiently solving general min-max optimization problems that arise naturally in distributionally robust learning. By focusing on the entire dataset, current approaches do not scale well. We address this issue by initially focusing on a subset of the data and progressively increasing this support to statistically cover the entire dataset.