Worst-Case Regret Bounds for Combinatorial Thompson Sampling in Sleeping Semi-Bandits
We revisit combinatorial Thompson sampling (CTS) for semi-bandits with sleeping arms, where arm availability varies over time and actions must satisfy combinatorial constraints, as in wireless mesh routing with fluctuating link availability. Despite its practical relevance, CTS has been hindered by several long-standing problems: (i) the absence of worst-case regret guarantees in the semi-bandit setting even without sleeping arms, (ii) the lack of theory under adversarially varying availability, and (iii) the consistently weak empirical performance of CTS with Gaussian priors (CTS-G). This paper resolves these long-standing issues by providing the first worst-case regret analysis of CTS-G...
| Authors | Zhiming Huang, Bingshan Hu, Jianping Pan |
|---|---|
| Primary category | cs.LG |
| Published | 10 May 2026 |
| Last updated at source | 12 May 2026 |
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