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New ZFC Proof Shows VC Dimension One Doesn't Guarantee PAC Learnability

Researchers have demonstrated that a concept class of Borel sets with a VC dimension of one can exist without guaranteeing PAC learnability, even with a consistent learning rule. This finding, achieved within Zermelo-Fraenkel set theory with the Axiom of Choice (ZFC), removes the necessity of the Continuum Hypothesis previously thought to be required for such a demonstration. The work shows that finite VC dimension and Borel measurability alone are insufficient to ensure PAC learnability for all proper consistent learning rules. AI

IMPACT Challenges foundational assumptions in statistical learning theory, potentially impacting the theoretical guarantees for AI model training.

RANK_REASON Academic paper detailing a theoretical finding in machine learning theory. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ZFC Proof Shows VC Dimension One Doesn't Guarantee PAC Learnability

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Academic paper detailing a theoretical finding in machine learning theory. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mateus Jesus de Arruda Campos, Gabriel Fernandes, Vinicius de Oliveira Rodrigues ·

    A Borel Concept Class of VC Dimension One with a Non-PAC Consistent Learner in ZFC

    arXiv:2608.30246v1 Announce Type: cross Abstract: The fundamental theorem of statistical learning states that, under suitable measurability assumptions, finite Vapnik--Chervonenkis (VC) dimension guarantees that every proper consistent learning rule is probably approximately corr…