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Sphere covering theorem yields tight bounds for learning theory reproducibility

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel sphere covering theorem, derived from the Borsuk-Ulam theorem, to establish tight bounds on list replicability in learning theory. This new theorem helps formalize reproducibility by relating list size to accuracy parameters and hypothesis class complexity. The findings yield sharp bounds for VC classes and demonstrate optimal list sizes for large-margin half-spaces, achieving minimal list sizes under specific margin conditions. AI

IMPACT Establishes new theoretical bounds for reproducibility in machine learning, potentially guiding algorithm development.

RANK_REASON The cluster contains an academic paper detailing a new theoretical result in machine learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Sphere covering theorem yields tight bounds for learning theory reproducibility

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ari Blondal, Hamed Hatami, Pooya Hatami, Chavdar Lalov, Sivan Tretiak ·

    Tight list replicability bounds via a novel sphere covering theorem

    arXiv:2606.06148v1 Announce Type: new Abstract: In recent years, list replicability has emerged as a framework for formalizing reproducibility in learning theory. A central question is how the required list size relates to the accuracy parameter and natural complexity measures of…

  2. arXiv cs.LG TIER_1 English(EN) · Sivan Tretiak ·

    Tight list replicability bounds via a novel sphere covering theorem

    In recent years, list replicability has emerged as a framework for formalizing reproducibility in learning theory. A central question is how the required list size relates to the accuracy parameter and natural complexity measures of the hypothesis class. To achieve sharp bounds o…

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