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» The Nonstochastic Multiarmed Bandit Problem
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PKDD
2009
Springer
184views Data Mining» more  PKDD 2009»
14 years 3 days ago
Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm
Abstract. This paper focuses on Active Learning with a limited number of queries; in application domains such as Numerical Engineering, the size of the training set might be limite...
Philippe Rolet, Michèle Sebag, Olivier Teyt...
CIMCA
2008
IEEE
14 years 2 months ago
Tree Exploration for Bayesian RL Exploration
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
Christos Dimitrakakis
ICC
2008
IEEE
169views Communications» more  ICC 2008»
14 years 2 months ago
Optimality of Myopic Sensing in Multi-Channel Opportunistic Access
—We consider opportunistic communications over multiple channels where the state (“good” or “bad”) of each channel evolves as independent and identically distributed Mark...
Tara Javidi, Bhaskar Krishnamachari, Qing Zhao, Mi...
TWC
2008
130views more  TWC 2008»
13 years 7 months ago
On myopic sensing for multi-channel opportunistic access: structure, optimality, and performance
We consider a multi-channel opportunistic communication system where the states of these channels evolve as independent and statistically identical Markov chains (the Gilbert-Elli...
Qing Zhao, Bhaskar Krishnamachari, Keqin Liu
IPSN
2010
Springer
14 years 2 months ago
Online distributed sensor selection
A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to co...
Daniel Golovin, Matthew Faulkner, Andreas Krause