A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning

Understand

Statistical machine learning theory often tries to give generalization guarantees of machine learning models.

• Those models naturally underlie some fluctuation, as they are based on a data sample.
• If we were unlucky, and gathered a sample that is not representative of the underlying distribution, one cannot expect to construct a reliable machine learning model.
• Following that, statements made about the performance of machine learning models have to take the sampling process into account.