DRO regularizers are worst-case sensitivities of expected cost, supplying a robustness measure that guides uncertainty-set selection and traces performance-robustness frontiers.
Distributionally robust stochastic optimization with Wasserstein distance.Mathematics of Operations Research, 48(2):603–655, 2023
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Develops robust SGLD with non-asymptotic convergence bounds for non-convex DRO and applies it to neural network regression under adversarial corruption.
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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.
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Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems
Develops robust SGLD with non-asymptotic convergence bounds for non-convex DRO and applies it to neural network regression under adversarial corruption.