Convex repeated games and Fenchel duality

Shai Shalev-Shwartz*, Yoram Singer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

57 Scopus citations

Abstract

We describe an algorithmic framework for an abstract game which we term a convex repeated game. We show that various online learning and boosting algorithms can be all derived as special cases of our algorithmic framework. This unified view explains the properties of existing algorithms and also enables us to derive several new interesting algorithms. Our algorithmic framework stems from a connection that we build between the notions of regret in game theory and weak duality in convex optimization.

Original languageAmerican English
Title of host publicationAdvances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
Pages1265-1272
Number of pages8
StatePublished - 2007
Event20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada
Duration: 4 Dec 20067 Dec 2006

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference20th Annual Conference on Neural Information Processing Systems, NIPS 2006
Country/TerritoryCanada
CityVancouver, BC
Period4/12/067/12/06

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