We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions. We develop a reduction from robust improper optimization to Bayesian optimization: given an oracle that returns \u03b1approximate solutions for distributions over objectives, we compute a distribution over solutions that is \u03b1-approximate in the worst case. We show that derandomizing this solution is NP-hard in general, but can be done for a broad class of statistical learning tasks. We apply our results to robust neural network training and submodular optimization. We evaluate our approach experimentally on corrupted character classi\ufb01cation, and robust in\ufb02uence maximization in networks.<\/p>\n","protected":false},"excerpt":{"rendered":"
We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions. We develop a reduction from robust improper optimization to Bayesian optimization: given an oracle that returns \u03b1approximate solutions for distributions over objectives, we compute a distribution over solutions that is \u03b1-approximate in the worst 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