A distributional framework for optimizing Lipschitz risk functionals in offline contextual bandits yields data-dependent suboptimality bounds of Õ(1/√n) that match risk-neutral rates and are minimax optimal.
Risk-sensitive reinforcement learning: near-optimal risk-sample tradeoff in regret
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Pessimistic Risk-Aware Policy Learning in Contextual Bandits
A distributional framework for optimizing Lipschitz risk functionals in offline contextual bandits yields data-dependent suboptimality bounds of Õ(1/√n) that match risk-neutral rates and are minimax optimal.