1.6k citations · 4.2k across the 71 of their papers we have counts for
3 papers · 2 filters
Reconciling modern machine learning practice and the bias-variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma +1
Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central t…
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
Mikhail Belkin, Daniel Hsu, Partha Mitra
Many modern machine learning models are trained to achieve zero or near-zero training error in order to obtain near-optimal (but non-zero) test error. This phenomenon of strong gen…
Certified Robustness to Adversarial Examples with Differential Privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu +2
Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed…