A seminar by Professor Peter Bartlett from University of Berkeley California
Title: Benign overfitting
Abstract: Deep learning has revealed some major surprises from the perspective of statistical complexity: even without any explicit effort to control model complexity, these methods find prediction rules that give a near-perfect fit to noisy training data and yet exhibit excellent prediction performance in practice. This talk reviews recent work on methods that predict accurately in probabilistic settings despite fitting too well to training data. We see how benign overfitting can occur with sufficient overparameterization in regression and classification problems, but it leads to sensitivity to adversarial examples.
For further information, please contact RSFAS Seminars.
All information collected by the University is governed by the ANU Privacy Policy.