Abstract
We study compressible types in the context of (local and global) NIP. By extending a result in machine learning theory (the existence of a bound on the recursive teaching dimension), we prove density of compressible types. Using this, we obtain explicit uniform honest definitions for NIP formulas (answering a question of Eshel and the second author), and build compressible models in countable NIP theories.
| Original language | English |
|---|---|
| Pages (from-to) | 2705-2749 |
| Number of pages | 45 |
| Journal | Journal of the European Mathematical Society |
| Volume | 27 |
| Issue number | 7 |
| DOIs | |
| State | Published - 2025 |
Bibliographical note
Publisher Copyright:© 2025 European Mathematical Society Publishing House. All rights reserved.
Keywords
- NIP
- compressible types
- distality
- model theory
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