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New algorithm learns constant-depth circuits under locally sampleable graphical models

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new algorithm for learning constant-depth circuits under graphical models that can be locally sampled. This work extends previous findings by Chandrasekaran, Gaitonde, Moitra, and Vasilyan (arXiv 2026), which were limited to models with strong spatial mixing and polynomial growth. The new method utilizes a novel low-degree approximation of Gibbs distributions, achieved by simulating and truncating Glauber dynamics. This approach enables learning for systems like the hard-core and Ising models on various bounded-degree graphs, even near their sampling thresholds. AI

IMPACT Advances theoretical understanding of learning algorithms for graphical models, potentially impacting future AI research in related areas.

RANK_REASON The cluster describes a new research paper published on arXiv detailing a novel algorithm for learning constant-depth circuits.

Read on arXiv cs.LG →

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

New algorithm learns constant-depth circuits under locally sampleable graphical models

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COVERAGE [2]

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

    Learning $\mathsf{AC}^0$ under Locally Sampleable Graphical Models

    arXiv:2607.08303v1 Announce Type: new Abstract: The problem of learning constant-depth circuits holds profound implications for computational learning theory. In a seminal result, by introducing the low-degree algorithm, Linial, Mansour, and Nisan (J. ACM 1993) presented a quasip…

  2. arXiv cs.LG TIER_1 English(EN) · Yiyao Zhang ·

    Learning $\mathsf{AC}^0$ under Locally Sampleable Graphical Models

    The problem of learning constant-depth circuits holds profound implications for computational learning theory. In a seminal result, by introducing the low-degree algorithm, Linial, Mansour, and Nisan (J. ACM 1993) presented a quasipolynomial-time learner for $\mathsf{AC}^0$ under…

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