Develops robust SGLD with non-asymptotic convergence bounds for non-convex DRO and applies it to neural network regression under adversarial corruption.
Quantifying distributional model risk via optimal transport
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State augmentation allows dynamic programming and sample complexity bounds for MDPs and optimal control under static risk measures including CVaR.
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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.
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Sample Complexity for Markov Decision Processes and Stochastic Optimal Control with Static Risk Measures
State augmentation allows dynamic programming and sample complexity bounds for MDPs and optimal control under static risk measures including CVaR.