Model Theory and Machine Learning
Logic
2019-10-30 v1
Abstract
About 25 years ago, it came to light that a single combinatorial property determines both an important dividing line in model theory (NIP) and machine learning (PAC-learnability). The following years saw a fruitful exchange of ideas between PAC learning and the model theory of NIP structures. In this article, we point out a new and similar connection between model theory and machine learning, this time developing a correspondence between \emph{stability} and learnability in various settings of \emph{online learning.} In particular, this gives many new examples of mathematically interesting classes which are learnable in the online setting.
Keywords
Cite
@article{arxiv.1801.06566,
title = {Model Theory and Machine Learning},
author = {Hunter Chase and James Freitag},
journal= {arXiv preprint arXiv:1801.06566},
year = {2019}
}
Comments
13 pages