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Research revisits Valiant's algorithm for CNF formula learning

By PulseAugur Editorial · [1 sources] ·

A new research paper revisits Valiant's algorithm for learning CNF formulas, focusing on its sample complexity in the local lemma regime. The study establishes a matching lower bound for the algorithm's performance when the clause size is greater than or equal to a certain threshold. For a specific case, the paper demonstrates that Valiant's algorithm achieves optimal sample complexity, up to logarithmic factors, through an information-theoretic lower bound. AI

RANK_REASON The item is a research paper published on arXiv detailing theoretical computer science concepts. [lever_c_demoted from research: ic=1 ai=0.4]

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Research revisits Valiant's algorithm for CNF formula learning

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Weiming Feng, Yixiao Yu, Yiyao Zhang ·

    Learning CNF Formulas from Uniform Random Solutions: Near-Tight Sample Complexity for Valiant's Algorithm

    arXiv:2609.15268v1 Announce Type: new Abstract: We revisit Valiant's algorithm (Commun. ACM'84) for learning $n$-variable CNF formulas with clause size $k$ and variable degree $d$ from i.i.d. uniform random solutions in the local lemma regime. For fixed $t\geq1$, under $k\gtrsim(…

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