Explore the phenomenon of benign overfitting in linear prediction through this 48-minute lecture by Peter Bartlett from UC Berkeley. Delve into the intricacies of overfitting in deep networks, interpolating prediction rules, and linear regression. Examine various notions of effective rank and gain insights into the characterization and proof ideas behind benign overfitting. Investigate the implications for deep learning and adversarial examples, while focusing on the specific case of linear regression. Part of the Frontiers of Deep Learning series at the Simons Institute, this talk provides a comprehensive analysis of a crucial concept in machine learning and its applications.
Overview
Syllabus
Intro
Overfitting in Deep Networks
Interpolating Prediction Rules
Definitions
Interpolating Linear Regression
Notions of Effective Rank
Benign Overfitting: A Characterization
Benign Overfitting: Proof Ideas
What kinds of eigenvalues?
Implications for deep learning
Implications for adversarial examples
Benign Overfitting in Linear Regression
Taught by
Simons Institute
