Near-Optimal Regret Bounds for Monotone Stochastic Optimization under Semi-Bandit Feedback
Stochastic optimization models sequential decision-making under uncertainty, typically assuming known input distributions. When these distributions are unknown, \citet{agarwal2026semibandit} give an online-to-offline reduction for monotone stochastic optimization under semi-bandit feedback, where only probed variables are observed. Its expected regret relative to the offline guarantee is $\widetilde O(n\sqrt T)$, where $n$ is the number of variables and $T$ the horizon. For optimal stopping and Pandora's box, \citet{liu2025improved} achieve $\widetilde O(\sqrt{nT})$ using problem-specific algorithms. \emph{Can a general online-to-offline reduction achieve this improved rate?} We give such...
| Authors | Tao Hu, Rohan Ghuge |
|---|---|
| Primary category | cs.LG |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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