DRO regularizers are worst-case sensitivities of expected cost, supplying a robustness measure that guides uncertainty-set selection and traces performance-robustness frontiers.
Robust Wasserstein profile inference and applications to machine learning.Journal of Applied Probability, 56(3):830–857
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Robustness Measures in Distributionally Robust Optimization
DRO regularizers are worst-case sensitivities of expected cost, supplying a robustness measure that guides uncertainty-set selection and traces performance-robustness frontiers.