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Algorithm Selection as a Bandit Problem with Unbounded Losses · Study

WorkAlgorithm Selection as a Bandit Problem with Unbounded Losses

Matteo Gagliolo2010

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Workgreen2010

Algorithm Selection as a Bandit Problem with Unbounded Losses

Matteo Gagliolo · Jürgen Schmidhuber

40 citations·0/0 files stored·Lecture notes in computer science

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Advanced Bandit Algorithms Research Constraint Satisfaction and Optimization Optimization and Search Problems

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References

2002The Nonstochastic Multiarmed Bandit Problem 1952Some aspects of the sequential design of experiments 2009Data fusion 2002Gambling in a rigged casino: The adversarial multi-armed bandit problem 2006Improved second-order bounds for prediction with expert advice 2005The max K-armed bandit: a new model of exploration applied to search heuristic selection 2007Learning dynamic algorithm portfolios 2006An asymptotically optimal algorithm for the max k-armed bandit problem

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Details

Year2010

Typeother

Published2010-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations40

Files0 public / 0

ShelfAdvanced Bandit Algorithms Research

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doi10.1007/978-3-642-13800-3_7

openalex_workW2126405658

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