Learning near-optimal robust solutions in pricing and beyond
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We develop a method for producing robust decisions in predict-optimize tasks. In particular, we introduce two notions of robustness: (1) decisions which minimize maximum cost with respect to noise/uncertainty in the objective, (2) producing stable decisions which do not change significantly under perturbation to the training data.