A Wasserstein DRO model for appointment scheduling with random service times and no-shows that converges to the true stochastic optimum and admits polynomial-sized LP reformulations under mild conditions.
Models for minimax stochastic linear optimization problems with risk aversion.Mathematics of Operations Research, 35(3):580–602, 2010
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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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Data-Driven Distributionally Robust Appointment Scheduling over Wasserstein Balls
A Wasserstein DRO model for appointment scheduling with random service times and no-shows that converges to the true stochastic optimum and admits polynomial-sized LP reformulations under mild conditions.
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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.