TY - GEN
T1 - Convex repeated games and Fenchel duality
AU - Shalev-Shwartz, Shai
AU - Singer, Yoram
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84864036264&partnerID=8YFLogxK
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AN - SCOPUS:84864036264
SN - 9780262195683
T3 - Advances in Neural Information Processing Systems
SP - 1265
EP - 1272
BT - Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
T2 - 20th Annual Conference on Neural Information Processing Systems, NIPS 2006
Y2 - 4 December 2006 through 7 December 2006
ER -