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» Active Learning in Multi-armed Bandits
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121
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CORR
2011
Springer
210views Education» more  CORR 2011»
14 years 5 months ago
Online Learning of Rested and Restless Bandits
In this paper we study the online learning problem involving rested and restless multiarmed bandits with multiple plays. The system consists of a single player/user and a set of K...
Cem Tekin, Mingyan Liu
82
Voted
ALT
2008
Springer
15 years 7 months ago
Active Learning in Multi-armed Bandits
In this paper we consider the problem of actively learning the mean values of distributions associated with a finite number of options (arms). The algorithms can select which opti...
András Antos, Varun Grover, Csaba Szepesv&a...
119
Voted
TSP
2012
13 years 5 months ago
Sensing and Probing Cardinalities for Active Cognitive Radios
—In a cognitive radio network, opportunistic spectrum access (OSA) to the underutilized spectrum involves not only sensing the spectrum occupancy but also probing the channel qua...
Thang Van Nguyen, Hyundong Shin, Tony Q. S. Quek, ...
ICML
2003
IEEE
15 years 11 months ago
Online Choice of Active Learning Algorithms
This paper is concerned with the question of how to online combine an ensemble of active learners so as to expedite the learning progress during a pool-based active learning sessi...
Yoram Baram, Ran El-Yaniv, Kobi Luz
106
Voted
CORR
2010
Springer
171views Education» more  CORR 2010»
14 years 5 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