  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.
