延迟反馈赌博机的最优双世界算法：对过度延迟具有鲁棒性
A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays
AI总结：
本文提出一种无需延迟先验知识的最优双世界算法，通过隐式探索、分布漂移控制和自适应跳过过度延迟观测，实现对任意延迟的鲁棒性，并揭示问题复杂性由缺失信息量而非延迟时间决定。
摘要：
我们提出了一种新的最优双世界算法，用于具有可变延迟反馈的赌博机问题。与先前需要预先知道最大延迟 $d_{\mathrm{max}}$ 且遗憾值与其呈线性关系的工作不同，我们的算法可以容忍高达 $T$ 阶的任意过度延迟（其中 $T$ 是时间范围）。该算法基于三项技术革新，每项都可能具有独立的研究价值：（1）我们引入了首个在最优双世界设置中工作的隐式探索方案。（2）我们引入了首个不依赖于延迟有界性的分布漂移控制方法。该控制基于隐式探索方案和对具有过度延迟的观测的自适应跳过。（3）我们引入了一种将标准遗憾与漂移遗憾相关联的程序，该程序不依赖于延迟的有界性。在概念层面上，我们证明了具有延迟反馈的最优双世界赌博机问题的复杂性由决策时缺失的信息量（以未完成观测的数量衡量）决定，而非信息缺失的时间（以延迟衡量）。
英文摘要：
We propose a new best-of-both-worlds algorithm for bandits with variably delayed feedback. In contrast to prior work, which required prior knowledge of the maximal delay $d_{\mathrm{max}}$ and had a linear dependence of the regret on it, our algorithm can tolerate arbitrary excessive delays up to order $T$ (where $T$ is the time horizon). The algorithm is based on three technical innovations, which may all be of independent interest: (1) We introduce the first implicit exploration scheme that works in best-of-both-worlds setting. (2) We introduce the first control of distribution drift that does not rely on boundedness of delays. The control is based on the implicit exploration scheme and adaptive skipping of observations with excessive delays. (3) We introduce a procedure relating standard regret with drifted regret that does not rely on boundedness of delays. At the conceptual level, we demonstrate that complexity of best-of-both-worlds bandits with delayed feedback is characterized by the amount of information missing at the time of decision making (measured by the number of outstanding observations) rather than the time that the information is missing (measured by the delays).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
