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Algorithm selection is typically based on models of algorithm performance, learned during a separate offline training sequence, which can be prohibitively expensive.
Some aspects of the sequential design of experiments
H. Robbins · 1952
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The algorithm selection problem
J. R. Rice · 1976
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The weighted majority algorithm
Nick Littlestone and Manfred K. Warmuth · 1994
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Gambling in a rigged casino: the adversarial multi-armed bandit problem
Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire · 1995
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An economic approach to hard computational problems
B. A. Huberman, R. M. Lukose, and T. Hogg · 1997
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Reinforcement learning: An introduction
R. Sutton and A. Barto · 1998
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Heavy-tailed phenomena in satisfiability and constraint satisfaction problems
Carla P. Gomes, Bart Selman, Nuno Crato, and Henry Kautz · 2000
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SATLIB: An Online Resource for Research on SAT
H. H. Hoos and T. Stützle · 2000
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Local search algorithms for SAT: An empirical evaluation
Holger H. Hoos and Thomas Stützle · 2000
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Algorithm selection using reinforcement learning
Michail G. Lagoudakis and Michael L. Littman · 2000
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On-line bibliography on meta-learning, 2001
Johannes Fürnkranz · 2001
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Algorithm portfolios
Carla P. Gomes and Bart Selman · 2001
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Optimal schedules for parallelizing anytime algorithms: the case of independent processes
Lev Finkelstein, Shaul Markovitch, and Ehud Rivlin · 2002
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Dynamic restart policies
Henry A. Kautz, Eric Horvitz, Yongshao Ruan, Carla P. Gomes, and Bart Selman · 2002
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Learning the empirical hardness of optimization problems: The case of combinatorial auctions
Kevin Leyton-Brown, Eugene Nudelman, and Yoav Shoham · 2002
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A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
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The nonstochastic multiarmed bandit problem
Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire · 2003
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Optimal schedules for parallelizing anytime algorithms: The case of shared resources
Lev Finkelstein, Shaul Markovitch, and Ehud Rivlin · 2003
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Simple rules for low-knowledge algorithm selection
Christopher J. Beck and Eugene C. Freuder · 2004
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A neural network model for inter-problem adaptive online time allocation
Matteo Gagliolo and Jürgen Schmidhuber · 2005
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Parameter adjustment based on performance prediction: Towards an instance-aware problem solver
Frank Hutter and Youssef Hamadi · 2005
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Diversification and determinism in local search for satisfiability
Chu Min Li and Wenqi Huang · 2005
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Statistically optimal combination of algorithms
Marek Petrik · 2005
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Hannan consistency in on-line learning in case of unbounded losses under partial monitoring
Chamy Allenberg, Peter Auer, László Györfi, and György Ottucsák · 2006
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Learning dynamic algorithm portfolios
Matteo Gagliolo and Jürgen Schmidhuber · 2006
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Adaptive online time allocation to search algorithms
M. Gagliolo, V. Zhumatiy, and J. Schmidhuber · 2004
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Introduction to the special issue on meta-learning
Christophe Giraud-Carrier, Ricardo Vilalta, and Pavel Brazdil · 2004
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Understanding random sat: Beyond the clauses-to-variables ratio
Eugene Nudelman, Kevin Leyton-Brown, Holger H. Hoos, Alex Devkar, and Yoav Shoham · 2004
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Applying machine learning to low knowledge control of optimization algorithms
T. Carchrae and J. C. Beck · 2005
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Improved second-order bounds for prediction with expert advice
Nicolò Cesa-Bianchi, Yishay Mansour, and Gilles Stoltz · 2005
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The max k-armed bandit: A new model of exploration applied to search heuristic selection
Vincent A. Cicirello and Stephen F. Smith · 2005
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Learning static parallel portfolios of algorithms
Marek Petrik and Shlomo Zilberstein · 2006
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An asymptotically optimal algorithm for the max k-armed bandit problem
Matthew J. Streeter and Stephen F. Smith · 2006
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Improved second-order bounds for prediction with expert advice
Nicolò Cesa-Bianchi, Yishay Mansour, and Gilles Stoltz · 2007
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Learning restart strategies
Matteo Gagliolo and Jürgen Schmidhuber · 2007
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Personal communication, 2008
Nicolò Cesa-Bianchi · 2008
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Towards distributed algorithm portfolios
Matteo Gagliolo and Jürgen Schmidhuber · 2008
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