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arXiv · 2107.05197

Density of compressible types and some consequences

Martin Bays·Itay Kaplan·Pierre Simon
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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.

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Martin Bays, Itay Kaplan, Pierre Simon. 2021-07-12. Density of compressible types and some consequences. https://doi.org/10.4171/jems/1423

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