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This note tightens a theoretical bound on list replicability, a measure of how consistently a learning algorithm can produce the same outputs across runs on fresh samples, when it may return a short list of candidates. A polylogarithmic upper bound would be a notable result, since it would show the quantity grows slowly with the number of concepts and domain points. The paper is a proof-simplification of a recent preprint not yet independently established, so readers should treat the result as a theoretical claim pending scrutiny rather than a practical guarantee.
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A polylogarithmic bound for list replicability

arXiv1 min read

arXiv:2610.20657v1 Announce Type: cross Abstract: Zhang [Zha26] recently posted a manuscript announcing that the list-replicability number of every class of $M\ge 2$ binary concepts on $N\ge 2$ domain points is $O(\log^2 M\log N)$, which would be the first polylogarithmic upper bound on this quantity. In this note, we present a short, self-contained proof of this bound, following the high-level approach of the manuscript but with considerably simpler arguments.

Abstract:Zhang [Zha26] recently posted a manuscript announcing that the list-replicability number of every class of $M\ge 2$ binary concepts on $N\ge 2$ domain points is $O(\log^2 M\log N)$, which would be the first polylogarithmic upper bound on this quantity. In this note, we present a short, self-contained proof of this bound, following the high-level approach of the manuscript but with considerably simpler arguments.

Machine Learning (stat.ML); Machine Learning (cs.LG); Combinatorics (math.CO)

arXiv:2610.20657 [stat.ML]

(or arXiv:2610.20657v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.20657 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 160 words

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