New Generalization Bounds for Overparameterized Neural Networks

A new paper on arXiv introduces initialization-dependent complexity bounds for overparameterized shallow neural networks. The research addresses the benign overfitting property, where networks generalize well despite having more parameters than training examples. Existing analyses using Frobenius norm distance from initialization often yield vacuous bounds. The authors propose bounds based on path-norm of distance from initialization, derived via a novel peeling technique for general Lipschitz activation functions. This work aims to provide non-vacuous theoretical guarantees for overparameterized models.