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Title: MODEL THEORY AND MACHINE LEARNING
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 stability and learnability in various settings of online learning. In particular, this gives many new examples of mathematically interesting classes which are learnable in the online setting.  more » « less
Award ID(s):
1700095
NSF-PAR ID:
10393851
Author(s) / Creator(s):
;
Date Published:
Journal Name:
The Bulletin of Symbolic Logic
Volume:
25
Issue:
03
ISSN:
1079-8986
Page Range / eLocation ID:
319 to 332
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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