新的Robbins混合算法为有界数据投注提供O(ln n)遗憾

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-14 04:00

一篇新的arXiv论文介绍了一种新颖的Robbins混合算法，该算法专为在二元观测序列上进行投注而设计。该算法旨在与最佳固定投注相比，实现O(ln n)的最坏情况遗憾，同时在特定条件下满足几乎确定的ln ln n的更严格遗憾界限。研究将这些发现与之前关于无界数据的工作进行了对比，强调了对冲策略在实现最佳增长率和遗憾界限方面的优势。 AI

影响在顺序预测算法方面引入了理论进展，可能应用于需要不确定性下稳健决策的领域。

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arXiv stat.ML TIER_1 English(EN) · Shubhada Agrawal, Aaditya Ramdas · 2026-10-14 04:00

Cover meets Robbins while Betting on Bounded Data: $\ln n$ Regret and Almost Sure $\ln\ln n$ Regret

arXiv:2604.20172v3 Announce Type: replace-cross Abstract: Consider betting against a sequence of $[0,1]$-valued observations using bets that are fair if the data have a conditional mean $m_0 \in (0,1)$. Cover's universal portfolio algorithm achieves a worst-case regret of $O(\ln …

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Cover meets Robbins while Betting on Bounded Data: $\ln n$ Regret and Almost Sure $\ln\ln n$ Regret

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