Explore the phenomenon of benign overfitting in machine learning through this 42-minute lecture by Peter Bartlett from UC Berkeley, presented at the Alan Turing Institute. Delve into the intersection of statistics and computer science, examining how modern machine learning algorithms can overfit training data yet still generalize well. Investigate topics such as overfitting in deep networks, statistical wisdom, regularization, interpolating linear regression, and characterizations of benign overfitting. Learn about notions of effective rank, proof ideas, and the implications for deep learning and adversarial examples. Gain insights into the future directions of benign overfitting research and its application in linear regression, providing a comprehensive overview of this important concept in the era of Big Data and high-dimensional statistical models.
Overview
Syllabus
Intro
Overfitting in Deep Networks
Statistical Wisdom and Overhitting
Progress on Overfitting Prediction Rules
Outline
Definitions
From regularization to overfitting
Interpolating Linear Regression
Benign Overfitting: A Characterization
Notions of Effective Rank
Benign Overfitting: Proof Ideas
What kinds of eigenvalues?
Extensions
Implications for deep learning
Implications for adversarial examples
Benign averfitting: Future directions
Benign Overfitting in Linear Regression
Taught by
Alan Turing Institute
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