For several machine learning methods such as neural networks, good generalisation performance has been reported in the overparametrized regime. In view of the classical bias-variance trade-off, this behaviour is highly counterintuitive. The talk summarizes recent theoretical results on overparametrization and the bias-variance trade-off. This is joint work with Alexis Derumigny.
Underlying paper: https://arxiv.org/pdf/2006.00278.pdf
Personal website of Johannes Schmidt-Hieber