Minimax Statistical Learning with Wasserstein Distances

NeurIPS 2018 Jaeho LeeMaxim Raginsky

As opposed to standard empirical risk minimization (ERM), distributionally robust optimization aims to minimize the worst-case risk over a larger ambiguity set containing the original empirical distribution of the training data. In this work, we describe a minimax framework for statistical learning with ambiguity sets given by balls in Wasserstein space... (read more)

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