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» Regret Bounds for Gaussian Process Bandit Problems
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ICASSP
2010
IEEE
14 years 9 months ago
Distributed learning in cognitive radio networks: Multi-armed bandit with distributed multiple players
—We consider a cognitive radio network with distributed multiple secondary users, where each user independently searches for spectrum opportunities in multiple channels without e...
Keqin Liu, Qing Zhao
COLT
2004
Springer
15 years 2 months ago
Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary
We give an algorithm for the bandit version of a very general online optimization problem considered by Kalai and Vempala [1], for the case of an adaptive adversary. In this proble...
H. Brendan McMahan, Avrim Blum
COLT
2010
Springer
14 years 7 months ago
Open Loop Optimistic Planning
We consider the problem of planning in a stochastic and discounted environment with a limited numerical budget. More precisely, we investigate strategies exploring the set of poss...
Sébastien Bubeck, Rémi Munos
COLT
2004
Springer
15 years 1 months ago
Regret Bounds for Hierarchical Classification with Linear-Threshold Functions
We study the problem of classifying data in a given taxonomy when classifications associated with multiple and/or partial paths are allowed. We introduce an incremental algorithm u...
Nicolò Cesa-Bianchi, Alex Conconi, Claudio ...
SIAMCOMP
2002
124views more  SIAMCOMP 2002»
14 years 9 months ago
The Nonstochastic Multiarmed Bandit Problem
Abstract. In the multiarmed bandit problem, a gambler must decide which arm of K nonidentical slot machines to play in a sequence of trials so as to maximize his reward. This class...
Peter Auer, Nicolò Cesa-Bianchi, Yoav Freun...