arXiv:2604.20172v1 Announce Type: cross Abstract: Consider betting against a sequence of data in $[0,1]$, where one is allowed to make any bet that is fair if the data have a conditional mean $m_0 \in (0,1)$. Cover's universal portfolio algorithm delivers a worst-case regret of $O(\ln n)$ compared to the best constant bet in hindsight, and this bound is unimprovable against adversarially generated
Cover meets Robbins while Betting on Bounded Data: $\ln n$ Regret and Almost Sure $\ln\ln n$ Regret
Shubhada Agrawal, Aaditya Ramdas·arXiv stat.ML··1 min read
a
Continue reading on arXiv stat.ML
This article was sourced from arXiv stat.ML's RSS feed. Visit the original for the complete story.