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ICML
2006
IEEE
10 years 6 months ago
Learning algorithms for online principal-agent problems (and selling goods online)
In a principal-agent problem, a principal seeks to motivate an agent to take a certain action beneficial to the principal, while spending as little as possible on the reward. This...
Vincent Conitzer, Nikesh Garera
CORR
2011
Springer
210views Education» more  CORR 2011»
9 years 23 days 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
CORR
2011
Springer
198views Education» more  CORR 2011»
8 years 9 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
COLT
2008
Springer
9 years 7 months ago
Adapting to a Changing Environment: the Brownian Restless Bandits
In the multi-armed bandit (MAB) problem there are k distributions associated with the rewards of playing each of k strategies (slot machine arms). The reward distributions are ini...
Aleksandrs Slivkins, Eli Upfal
ICML
2001
IEEE
10 years 6 months ago
Expectation Maximization for Weakly Labeled Data
We call data weakly labeled if it has no exact label but rather a numerical indication of correctness of the label "guessed" by the learning algorithm - a situation comm...
Yuri A. Ivanov, Bruce Blumberg, Alex Pentland
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