The unstable formula theorem revisited via algorithms

Maryanthe Malliaris; Shay Moran

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arXiv · 2212.05050

The unstable formula theorem revisited via algorithms

Maryanthe Malliaris·Shay Moran

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Abstract

This paper is about the surprising interaction of a foundational result from model theory, about stability of theories, with algorithmic stability in learning. First, in response to gaps in existing learning models, we introduce a new statistical learning model, called ``Probably Eventually Correct'' or PEC. We characterize Littlestone (stable) classes in terms of this model. As a corollary, Littlestone classes have frequent short definitions in a natural statistical sense. In order to obtain a characterization of Littlestone classes in terms of frequent definitions, we build an equivalence theorem highlighting what is common to many existing approximation algorithms, and to the new PEC. This is guided by an analogy to definability of types in model theory, but has its own character. Drawing on these theorems and on other recent work, we present a complete algorithmic analogue of Shelah's celebrated Unstable Formula Theorem, with algorithmic properties taking the place of the infinite.

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Maryanthe Malliaris, Shay Moran. 2025-07-02. The unstable formula theorem revisited via algorithms. https://arxiv.org/abs/2212.05050

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