Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces

Anastasis Kratsios, A. Martina Neuman, Gudmund Pammer

Machine learning models with inputs in a Euclidean space ${\mathbb{R}}^d$, when implemented on digital computers, generalize, and their generalization gap converges to $0$ at a rate…