Optimal strategies and minimax lower bounds for online convex games
Abernethy, Jacob D., Bartlett, Peter L., Rakhlin, Alexander, & Tewari, Ambuj (2008) Optimal strategies and minimax lower bounds for online convex games. Technical Report, UCB/EECS-2008-19. University of California, Berkeley, Berkeley, California (USA).
A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a prediction x from a convex set, the environment plays a loss function f, and the learner’s long-term goal is to minimize regret. Algorithms have been proposed by Zinkevich, when f is assumed to be convex, and Hazan et al., when f is assumed to be strongly convex, that have provably low regret. We consider these two settings and analyze such games from a minimax perspective, proving minimax strategies and lower bounds in each case. These results prove that the existing algorithms are essentially optimal.
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|Additional Information:||Fulltext freely available see link above|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
|Copyright Owner:||Copyright © 2008, by the author(s).
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|Deposited On:||17 Aug 2011 22:21|
|Last Modified:||28 Jan 2015 03:52|
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