State augmentation converts static risk measures on total cost into dynamic programs, yielding sample-complexity bounds for risk-averse MDPs and stochastic optimal control under φ-divergence robustness.
Quantifying distributional model risk via optimal transport
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
math.OC 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Develops robust SGLD with non-asymptotic convergence bounds for non-convex DRO and applies it to neural network regression under adversarial corruption.
citing papers explorer
-
Sample Complexity for Markov Decision Processes and Stochastic Optimal Control with Static Risk Measures
State augmentation converts static risk measures on total cost into dynamic programs, yielding sample-complexity bounds for risk-averse MDPs and stochastic optimal control under φ-divergence robustness.
-
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.