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New research analyzes complexity of learning Boolean function formulas

By PulseAugur Editorial · [1 sources] ·

Researchers have analyzed the complexity of fitting and learning propositional formulas built with a specific set of Boolean functions. The study determines the difficulty of various problems, including finding a formula that matches a given labeled sample, identifying a smaller formula, and minimizing misclassifications on unrealizable samples. These findings apply to both tree and circuit representations of formulas and also consider related questions for other propositional fragments. AI

IMPACT Provides theoretical underpinnings for understanding the complexity of learning and fitting Boolean functions, relevant to foundational AI research.

RANK_REASON Academic paper published on arXiv detailing theoretical computer science research. [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 research analyzes complexity of learning Boolean function formulas

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Academic paper published on arXiv detailing theoretical computer science research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Balder ten Cate ·

    Fitting and Learning Basis-Restricted Propositional Formulas

    arXiv:2609.08961v1 Announce Type: cross Abstract: For a finite set $O$ of Boolean functions, we consider the class of propositional formulas built using the functions in $O$ as connectives. We determine, for each possible choice of $O$, the complexity of various fitting and learn…

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