Pessimistic Risk-Aware Policy Learning in Contextual Bandits
We study risk-aware offline policy learning, aiming to learn a decision rule from logged data that is optimal under general risk criteria. This problem is crucial in high-stakes domains where online interaction is infeasible and adverse outcomes must be carefully controlled. However, existing literature on offline contextual bandits either centers on expected-reward criteria or restricts risk considerations to policy evaluation instead of optimization. In this work, we propose a unified distributional framework for optimizing Lipschitz-continuous risk functionals, a broad class of risk measures encompassing mean-variance, entropic risk, and conditional value-at-risk, among others. By deve...
| Authors | Yilong Wan, Yuqiang Li, Xianyi Wu |
|---|---|
| Primary category | stat.ML |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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