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New research tackles scientific discovery complexity with PAC learning

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the sample complexity of scientific discovery through the lens of PAC learning, focusing on compositional function trees. The study proves that the generalization quantity, Rademacher complexity, is controlled by the depth of the tree and the Lipschitz constants of its operators, rather than growing exponentially. The authors developed a codebase to train differentiable operator trees and empirically demonstrated that the generalization gap correlates with their predicted complexity. AI

IMPACT Provides theoretical grounding for symbolic regression and compositional function learning, potentially improving AI's ability to perform scientific discovery.

RANK_REASON The cluster contains an academic paper detailing theoretical research on machine learning.

Read on arXiv stat.ML →

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

New research tackles scientific discovery complexity with PAC learning

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · \c{S}uayp Talha Kocabay, Talha R\"uzgar Akku\c{s}, Kerem Yal\c{c}{\i}n ·

    Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees

    arXiv:2606.29331v1 Announce Type: cross Abstract: Scientific discovery via symbolic regression is often viewed as statistically and computationally intractable because the hypothesis space of expressions grows combinatorially with depth. This paper revisits the statistical side t…

  2. arXiv stat.ML TIER_1 English(EN) · Kerem Yalçın ·

    Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees

    Scientific discovery via symbolic regression is often viewed as statistically and computationally intractable because the hypothesis space of expressions grows combinatorially with depth. This paper revisits the statistical side through the lens of PAC learning, focusing on compo…

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