Theoretical analysis uncovers optimal mistake bounds in deterministic online prediction, indicating a complete phase diagram across all expert and round regimes.
In prediction with expert advice under apple tasting feedback, the learner observes the true label only in rounds where it predicts $1$. For deterministic learners and n experts over T rounds, Chase and Mehalel (COLT 2025) determined the optimal realizable mistake bound Θ(√Tlog n) for T≤ n≤ 2T, and stated as an open problem the regime of "slightly more than √ T many experts", namely n=T1/2+ε with ε=o(1). We close this regime: for all √ T≤ n≤ T the optimal deterministic mistake bound is Θ(√Tlog₊(n/√ T)), where log₊ x=max1,ln x. For example, with n=√ Tln T experts the answer is Θ(√Tlnln T). The lower bound is a single self-contained "lowest-load committee" argument which also re-derives, with explicit constants, the known Ω(√Tlog n) bound for n≥ T. We then extend the picture to the k-realizable (agnostic-budget) setting and prove a lower bound of Ω(√T(k+log₊(n/√ T))) throughout √ T≤ n≤ 2T, which is tight in the critical band √ T≤ n≤ T by a tuning of a lemma of Chase and Mehalel; for n≥ T this recovers their known Θ(√T(k+log n)) and contains no new content. Along the way we isolate an expert-budget threshold phenomenon: with two experts M⁽2,k,T)=Θ(k+1) regardless of T, and forcing the √Tk term requires n√T/(k+1) experts. Combining all regimes yields, for every n≥ 2, T≥ 2 and 0≤ k≤ T: M⁽n,k,T)=Θ(minT,\ (n-1)(k+1)+k,\ √T(k+log₊(n/√ T))).
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Guangjian Zhang (2026) studied this question.
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