Learning algorithms versus automatability of Frege systems

We connect learning algorithms and algorithms automating proof search in propositional proof systems: for every sufficiently strong, well-behaved propositional proof system [Formula: see text], we prove that the following statements are equivalent, (1) Provable learning. [Formula: see text] proves efficiently that p-size circuits are learnable by subexponential-size circuits over the uniform distribution with membership queries. (2) Provable automatability. [Formula: see text] proves efficiently that [Formula: see text] is automatable by non-uniform circuits on propositional formulas expressing p-size circuit lower bounds. Here, [Formula: see text] is sufficiently strong and well-behaved if I.–III. hold: I. [Formula: see text] p-simulates Jeřábek’s system [Formula: see text] (which strengthens the Extended Frege system [Formula: see text] by a surjective weak pigeonhole principle); II. [Formula: see text] satisfies some basic properties of standard proof systems which p-simulate [Formula: see text]; III. [Formula: see text] proves efficiently for some Boolean function [Formula: see text] that [Formula: see text] is hard on average for circuits of subexponential size. For example, if III. holds for [Formula: see text], then Items 1 and 2 are equivalent for [Formula: see text]. We use the following modified notion of automatability in Item 2, the automating circuits output a [Formula: see text]-proof of a given formula (expressing a p-size circuit lower bound for a function [Formula: see text]) in non-uniform p-time in the length of a shortest [Formula: see text]-proof of a closely related but different formula (expressing an average-case subexponential-size circuit lower bound for the same function [Formula: see text]). If there is a function [Formula: see text] which is hard on average for circuits of size [Formula: see text], for each sufficiently big [Formula: see text], then there is an explicit propositional proof system [Formula: see text] satisfying properties I.-III., i.e. the equivalence of Items 1 and 2 holds for [Formula: see text].

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Publication Details

Journal
Journal of Mathematical Logic
Published
2024-11-15
DOI
https://doi.org/10.1142/s0219061325500023
Primary Topic
Machine Learning and Algorithms
Type
article
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article

Learning algorithms versus automatability of Frege systems

Ján Pich, Rahul Santhanam
Journal of Mathematical Logic
Machine Learning and Algorithms
article

Learning algorithms versus automatability of Frege systems

Ján Pich, Rahul Santhanam
article en

Abstract

We connect learning algorithms and algorithms automating proof search in propositional proof systems: for every sufficiently strong, well-behaved propositional proof system [Formula: see text], we prove that the following statements are equivalent, (1) Provable learning. [Formula: see text] proves efficiently that p-size circuits are learnable by subexponential-size circuits over the uniform distribution with membership queries. (2) Provable automatability. [Formula: see text] proves efficiently that [Formula: see text] is automatable by non-uniform circuits on propositional formulas expressing p-size circuit lower bounds. Here, [Formula: see text] is sufficiently strong and well-behaved if I.–III. hold: I. [Formula: see text] p-simulates Jeřábek’s system [Formula: see text] (which strengthens the Extended Frege system [Formula: see text] by a surjective weak pigeonhole principle); II. [Formula: see text] satisfies some basic properties of standard proof systems which p-simulate [Formula: see text]; III. [Formula: see text] proves efficiently for some Boolean function [Formula: see text] that [Formula: see text] is hard on average for circuits of subexponential size. For example, if III. holds for [Formula: see text], then Items 1 and 2 are equivalent for [Formula: see text]. We use the following modified notion of automatability in Item 2, the automating circuits output a [Formula: see text]-proof of a given formula (expressing a p-size circuit lower bound for a function [Formula: see text]) in non-uniform p-time in the length of a shortest [Formula: see text]-proof of a closely related but different formula (expressing an average-case subexponential-size circuit lower bound for the same function [Formula: see text]). If there is a function [Formula: see text] which is hard on average for circuits of size [Formula: see text], for each sufficiently big [Formula: see text], then there is an explicit propositional proof system [Formula: see text] satisfying properties I.-III., i.e. the equivalence of Items 1 and 2 holds for [Formula: see text].

Journal of Mathematical Logic
University of Oxford (GB)
Openalex Percentile: Top 100%
Machine Learning and Algorithms
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