Modeling the Second Player in Distributionally Robust Optimization

international conference on learning representations, 2021.

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We use generative neural models to define the uncertainty set in distributionally robust optimization, and show that this helps train more robust classifiers

Abstract:

Distributionally robust optimization (DRO) provides a framework for training machine learning models that are able to perform well on a collection of related data distributions (the \"uncertainty set\"). This is done by solving a min-max game: the model is trained to minimize its maximum expected loss among all distributions in the uncert...More

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