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This paper studies the sample complexity of robust average-reward Markov decision processes, deriving minimax-optimal learning rates via plug-in reductions under total-variation uncertainty sets.
This paper introduces a scenario-weighted adversarially robust posture optimization engine for military asset allocation, proposing CEV and RobustCEV optimizers that outperform greedy baselines under adversarial threat uncertainty.
This paper proposes F2CTO, the first distributed first-order constrained trilevel optimization method for robust coreset selection over distributed networks, with a non-asymptotic convergence guarantee of O(ε^(-3/2)).
This paper proposes a robust variant of smart predict-then-optimize that accounts for feature perturbations, providing a convex surrogate with theoretical guarantees and demonstrating superior performance over standard methods.
This paper studies robust peak-cost constrained reinforcement learning, addressing limitations of standard CMDPs by controlling the maximum cost along a trajectory and considering dynamics uncertainty. The authors show zero duality gap may not hold and propose a surrogate optimization framework with robust value estimation.
This paper introduces Maximally Robust Satisficing Bayesian Optimization (MRSBO), a method that efficiently finds solutions meeting a quality threshold while being robust to input perturbations after deployment, outperforming previous approaches.
This paper presents an algorithm for group distributionally robust least squares regression using block Lewis weights, achieving improved complexity over interior point methods. It also provides interpolating algorithms between average and robust losses.
Introduces PROWL, a prioritized regret-driven optimization framework that uses an adversarial curriculum to improve diffusion-based world model robustness by focusing on high-error trajectories, achieving better performance on out-of-distribution scenarios in MineRL.
Introduces ODRPO, a framework that decomposes discrete rewards into ordinal binary indicators to improve robustness of policy optimization in RLAIF for LLMs, achieving up to 14.8% relative improvement with minimal overhead.
This paper introduces RQIQN, a robust quantile-based method for distributional reinforcement learning that uses Wasserstein geometry regularization to prevent distribution degeneration and improve performance in risk-sensitive tasks.