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DTSTAMP:20260415T115919Z
DESCRIPTION:Title: Generalization bounds via regret analysis”\n\nAbstract: 
 Understanding the generalization ability of learning algorithms has been a
  key driving force behind statistical learning theory.\n\nIn this talk\, w
 e present a novel framework for deriving bounds on the generalization erro
 r of statistical learning algorithms from the perspective of online learni
 ng. Specifically\, we construct an online learning game called the “genera
 lization game”\, where an online learner competes with a fixed statistical
  learning algorithm in predicting the sequence of generalization gaps on a
  training set of i.i.d. data points. We establish a connection between the
  online and statistical learning setting by showing that the existence of 
 an online learning algorithm with bounded regret in this game implies a bo
 und on the generalization error of the statistical learning algorithm. Thi
 s technique allows us to recover several standard generalization bounds\, 
 including a range of PAC-Bayesian and information-theoretic guarantees.\n
DTSTART:20250922T190000Z
DTEND:20250922T190000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Gabor Lugosi (Universitat Pompeu Fabra)
URL:https://www.mcgill.ca/mathstat/channels/event/gabor-lugosi-universitat-
 pompeu-fabra-367890
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