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

TL;DR

This paper introduces a novel mixture betting strategy that achieves near-optimal regret bounds, combining stochastic adaptivity with adversarial robustness, and demonstrates a sharp game-theoretic law of the iterated logarithm.

Contribution

It presents a new mixture betting approach that attains $O( ln n)$ regret on some paths and $O( ln ln n)$ on others, blending stochastic and adversarial guarantees.

Findings

Achieves $O( ln ln n)$ regret on almost all paths under certain conditions.

Demonstrates a sharp game-theoretic upper law of the iterated logarithm.

Contrasts with prior work by providing a best-of-both-worlds betting strategy.

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 data. In this work, we present a novel mixture betting strategy that combines insights from Robbins and Cover, and exhibits a different behavior: it eventually produces a regret of $O(\ln \ln n)$ on almost all paths (a measure-one set of paths if each conditional mean equals $m_0$ and intrinsic variance increases to $\infty$), but has an $O(\log n)$ regret on the complement (a measure zero set of paths). Our paper appears to be the first to point out the value in hedging two very different strategies to achieve a best-of-both-worlds adaptivity to stochastic data and protection against adversarial data. We contrast our results to those in Agrawal and Ramdas [2026] for a sub-Gaussian mixture on unbounded data: their worst-case regret has to be unbounded, but a similar hedging delivers both an optimal betting growth-rate and an almost sure $\ln\ln n$ regret on stochastic data. Finally, our strategy witnesses a sharp game-theoretic upper law of the iterated logarithm, analogous to Shafer and Vovk [2005].