研究人员发表了关于老虎机算法的新论文，探索了在不确定性下优化决策的不同方法。一篇论文研究了具有延迟反馈的随机线性老虎机，分析了各种延迟模型如何影响遗憾保证，并将它们与多臂老虎机进行比较。另一项研究侧重于具有有界噪声的随机线性上下文老虎机，提出了一种利用集合成员估计来获得改进遗憾界限的新算法。第三篇论文研究了使用正则化稳定老虎机，推导出精确的遗憾界限和定量中心极限定理，强调了推理有效性与最优遗憾率之间的权衡。 AI
影响 这些论文推进了对老虎机算法的理论理解，有可能在面临不确定性和延迟信息的 AI 系统中实现更有效的决策。
排序理由 多篇 arXiv 论文发表了相关的机器学习研究主题。
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arXiv:2606.16656v1 Announce Type: new Abstract: We study stochastic linear bandits with delayed feedback under several delay models and establish near-optimal regret guarantees. Our results identify when delayed linear bandits exhibit the same qualitative behavior as multi-armed …
We study stochastic linear bandits with delayed feedback under several delay models and establish near-optimal regret guarantees. Our results identify when delayed linear bandits exhibit the same qualitative behavior as multi-armed bandits (MAB), and when the linear structure cre…
arXiv:2606.20022v1 Announce Type: new Abstract: This paper considers stochastic linear contextual bandits (SLCB) with bounded reward noise. Existing works typically assume sub-Gaussian reward noise and bounded expected rewards, under which the optimal regret bound scales as $\til…
arXiv:2603.10184v2 Announce Type: replace Abstract: Statistical inference with bandit data presents fundamental challenges owing to adaptive sampling, which violates the independence assumptions underlying classical asymptotic theory. Recent work has identified stability~\citep{l…
This paper considers stochastic linear contextual bandits (SLCB) with bounded reward noise. Existing works typically assume sub-Gaussian reward noise and bounded expected rewards, under which the optimal regret bound scales as $\tilde{O}(\sqrt{T})$ in terms of horizon $T$. Howeve…