Modeling the Second Player in Distributionally Robust Optimization
international conference on learning representations, 2021.
We use generative neural models to define the uncertainty set in distributionally robust optimization, and show that this helps train more robust classifiers
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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