Thomas Borsani

Operator-Theoretic Generalization Bounds for Multitask Deep Learning

Operator-theoretic generalization bounds for deep multi-output function classes are developed by representing network layers as Koopman composition operators on vector-valued reproducing kernel Hilbert spaces and derive Rademacher complexity bounds for invertible and width-expanding injective architectures.