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This research establishes that no single rank-ordered adversary strategy is globally optimal for the prediction with expert advice problem when there are six or more experts, refining theoretical bounds in online learning. By deriving a leading order correction for scenarios where one expert significantly outperforms others, the study bridges gaps between strategies for different numbers of experts and leverages existing optimality sets for smaller groups. These findings advance the mathematical foundations of adversarial prediction algorithms, offering critical insights for optimizing performance in multi-agent machine learning systems.
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On prediction from expert advice with more than five experts

arXiv1 min read

arXiv:2610.05186v1 Announce Type: cross Abstract: We prove that no single rank ordered adversary strategy is globally optimal for the prediction with expert advice problem with six or more experts, in both the geometric stopping and finite time horizon settings. The proof is based on establishing a leading order correction when one expert moves far ahead of the others. This allows us to connect optimal strategies between $n$ and $j<n$ experts and utilize recent results on the exact optimality set for the five expert problem.

Abstract:We prove that no single rank ordered adversary strategy is globally optimal for the prediction with expert advice problem with six or more experts, in both the geometric stopping and finite time horizon settings. The proof is based on establishing a leading order correction when one expert moves far ahead of the others. This allows us to connect optimal strategies between $n$ and $j<n$ experts and utilize recent results on the exact optimality set for the five expert problem.

42 pages. Lean formalization: this https URL

Analysis of PDEs (math.AP); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2610.05186 [math.AP]

(or arXiv:2610.05186v1 [math.AP] for this version)

https://doi.org/10.48550/arXiv.2610.05186 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 194 words

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