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Invited Talk (Posner Lecture)
Benign Overfitting
Peter Bartlett

Wed Dec 08 03:00 PM -- 04:30 PM (PST) @ None

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 surveys work on methods that predict accurately in probabilistic settings despite fitting too well to training data. We present a characterization of linear regression problems for which the minimum norm interpolating prediction rule has near-optimal prediction accuracy. The characterization shows that overparameterization is essential for benign overfitting in this setting: the number of directions in parameter space that are unimportant for prediction must significantly exceed the sample size. We discuss implications for robustness to adversarial examples, and we describe extensions to ridge regression and barriers to analyzing benign overfitting via model-dependent generalization bounds.

Author Information

Peter Bartlett (UC Berkeley)
Peter Bartlett

Peter Bartlett is professor of Computer Science and Statistics at the University of California at Berkeley, Associate Director of the Simons Institute for the Theory of Computing, and Director of the Foundations of Data Science Institute. He has previously held positions at the Queensland University of Technology, the Australian National University and the University of Queensland. His research interests include machine learning and statistical learning theory, and he is the co-author of the book Neural Network Learning: Theoretical Foundations. He has been Institute of Mathematical Statistics Medallion Lecturer, winner of the Malcolm McIntosh Prize for Physical Scientist of the Year, and Australian Laureate Fellow, and he is a Fellow of the IMS, Fellow of the ACM, and Fellow of the Australian Academy of Science.

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