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CORR
2008
Springer
136views Education» more  CORR 2008»
13 years 4 months ago
Multi-Armed Bandits in Metric Spaces
In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of n trials so as to maximize the total payoff of the chosen strategies. While ...
Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal
CORR
2010
Springer
171views Education» more  CORR 2010»
12 years 11 months ago
Online Learning in Opportunistic Spectrum Access: A Restless Bandit Approach
We consider an opportunistic spectrum access (OSA) problem where the time-varying condition of each channel (e.g., as a result of random fading or certain primary users' activ...
Cem Tekin, Mingyan Liu
FOCS
2007
IEEE
13 years 10 months ago
Approximation Algorithms for Partial-Information Based Stochastic Control with Markovian Rewards
We consider a variant of the classic multi-armed bandit problem (MAB), which we call FEEDBACK MAB, where the reward obtained by playing each of n independent arms varies according...
Sudipto Guha, Kamesh Munagala
GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
13 years 9 months ago
Toward comparison-based adaptive operator selection
Adaptive Operator Selection (AOS) turns the impacts of the applications of variation operators into Operator Selection through a Credit Assignment mechanism. However, most Credit ...
Álvaro Fialho, Marc Schoenauer, Michè...
CORR
2011
Springer
198views Education» more  CORR 2011»
12 years 8 months ago
Decentralized Online Learning Algorithms for Opportunistic Spectrum Access
—The fundamental problem of multiple secondary users contending for opportunistic spectrum access over multiple channels in cognitive radio networks has been formulated recently ...
Yi Gai, Bhaskar Krishnamachari