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Generalization Bounds via Online Learning

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  • UserDr Gergely Neu, Universitat Pompeu Fabra World_link
  • ClockWednesday 08 March 2023, 14:00-15:00
  • HouseMR5, CMS Pavilion A.

If you have a question about this talk, please contact Prof. Ramji Venkataramanan.

Bounding the generalization error is one most fundamental problems in statistical learning theory. In this talk, I will present a new framework for deriving generalization bounds from the perspective of online learning. Specifically, we construct an online learning game called the Generalization Game, where an online learner is trying to compete with a fixed statistical learning algorithm in predicting the sequence of generalization gaps on a training set of i.i.d. data points. As I will show, this framework will allow us to recover a range of classic bounds including PAC -Bayes and generalizations thereof. (Based on joint work with Gabor Lugosi.)

This talk is part of the Information Theory Seminar series.

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