Near-Optimal Regret for Semi-Bandit Learning of Monotone Stochastic Problems
We consider the task of learning an (approximately) optimal policy for stochastic optimization problems where the true distributions are unknown. In each period, the algorithm plays a policy for the stochastic problem and observes semi-bandit feedback, i.e., it only receives new samples from the random variables that were selected by the policy. We obtain an algorithm that works for a large class of ``monotone'' stochastic problems, including prophet inequality, Pandora's box, series testing, stochastic knapsack and stochastic matching. We obtain $\tilde{O}(\sqrt{nkT})$ regret for ``weakly'' monotone problems and $\tilde{O}(\sqrt{nT})$ regret for ``strongly'' monotone problems, where $T$ ...
| Authors | Zhengjia Zhuo, Viswanath Nagarajan |
|---|---|
| Primary category | cs.LG |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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