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Dynamic Algorithm Configuration (DAC) aims to dynamically control a target algorithm's hyperparameters in order to improve its performance.
Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)
Nikolaus Hansen, Sibylle D. Müller, and Petros Koumoutsakos · 2003
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The fast downward planning system
M. Helmert · 2006
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A gender-based genetic algorithm for the automatic configuration of algorithms
C. Ansótegui, M. Sellmann, and K. Tierney · 2009
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ParamILS: An automatic algorithm configuration framework
F. Hutter, H. Hoos, K. Leyton-Brown, and T. Stützle · 2009
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Sequential model-based optimization for general algorithm configuration
F. Hutter, H. Hoos, and K. Leyton-Brown · 2011
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Cumulative step-size adaptation on linear functions
A. Chotard, A. Auger, and N. Hansen · 2012
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Hyflex: A benchmark framework for cross-domain heuristic search
G. Ochoa, M. Hyde, T. Curtois, J. Rodríguez, J. Walker, M. Gendreau, G. Kendall, B. McCollum, A. Parkes, S. Petrovic, and E. Burke · 2012
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Towards an empirical foundation for assessing Bayesian optimization of hyperparameters
K. Eggensperger, M. Feurer, F. Hutter, J. Bergstra, J. Snoek, H. Hoos, and K. Leyton-Brown · 2013
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An empirical study of learning rates in deep neural networks for speech recognition
A. Senior, G. Heigold, M. Ranzato, and K. Yang · 2013
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AClib: a benchmark library for algorithm configuration
F. Hutter, M. López-Ibánez, C. Fawcett, M. Lindauer, H. Hoos, K. Leyton-Brown, and T. Stützle · 2014
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Towards objective measures of algorithm performance across instance space
K. Smith-Miles, D. Baatar, B. Wreford, and R. Lewis · 2014
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Contextual markov decision processes
A. Hallak, D. Di Castro, and S. Mannor · 2015
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Performance of selection hyper-heuristics on the extended hyflex domains
A. Almutairi, E. Özcan, A. Kheiri, and W. Jackson · 2016
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Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. G. Colmenarejo, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas · 2016
Cited alongside, same era.
Maxsat by improved instance-specific algorithm configuration
C. Ansótegui, J. Gabàs, Y. Malitsky, and M. Sellmann · 2016
Cited alongside, same era.
ASlib: A benchmark library for algorithm selection
B. Bischl, P. Kerschke, L. Kotthoff, M. Lindauer, Y. Malitsky, A. Frechétte, H. Hoos, F. Hutter, K. Leyton-Brown, K. Tierney, and J. Vanschoren · 2016
Cited alongside, same era.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Cited alongside, same era.
Learning step size controllers for robust neural network training
C. Daniel, J. Taylor, and S. Nowozin · 2016
Cited alongside, same era.
Efficient benchmarking of algorithm configurators via model-based surrogates
K. Eggensperger, M. Lindauer, H. H. Hoos, F. Hutter, and K. Leyton-Brown · 2018
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Meta-reinforcement learning of structured exploration strategies
A. Gupta, R. Mendonca, Y. Liu, P. Abbeel, and S. Levine · 2018
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Evolved policy gradients
R. Houthooft, Y. Chen, P. Isola, B. Stadie, F. Wolski, J. Ho, and Pieter Abbeel · 2018
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Automated Machine Learning: Methods, Systems, Challenges
F. Hutter, L. Kotthoff, and J. Vanschoren, editors · 2019
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Online selection of CMA-ES variants
D. Vermetten, S. van Rijn, T. Bäck, and C. Doerr · 2019
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Nas-bench-101: Towards reproducible neural architecture search
C. Ying, A. Klein, E. Christiansen, E. Real, K. Murphy, and F. Hutter · 2019
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M. López-Ibáñez, J. Dubois-Lacoste, L. Perez Caceres, M. Birattari, and T. Stützle · 2016
Cited alongside, same era.
Taking the human out of the loop: A review of Bayesian optimization
B. Shahriari, K. Swersky, Z. Wang, R. Adams, and N. de Freitas · 2016
Cited alongside, same era.
Learning to learn without gradient descent by gradient descent
Y. Chen, M. Hoffman, S. Colmenarejo, M. Denil, T. Lillicrap, M. Botvinick, and N. de Freitas · 2017
Cited alongside, same era.
The configurable SAT solver challenge (CSSC)
F. Hutter, M. Lindauer, A. Balint, S. Bayless, H. Hoos, and K. Leyton-Brown · 2017
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
Cited alongside, same era.
Optimal static and self-adjusting parameter choices for the (1+(
B. Doerr and C. Doerr · 2018
Cited alongside, same era.
Iohprofiler: A benchmarking and profiling tool for iterative optimization heuristics
C. Doerr, H. Wang, F. Ye, S. van Rijn, and T. Bäck · 2018
Cited alongside, same era.
Later among the works it cites.
Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework
A. Biedenkapp, H. F. Bozkurt, T. Eimer, F. Hutter, and M. Lindauer · 2020
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COCO: A platform for comparing continuous optimizers in a black-box setting
N. Hansen, A. Auger, R. Ros, O. Mersmann, T. Tušar, and D. Brockhoff · 2020
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Learning step-size adaptation in CMA-ES
G. Shala, A. Biedenkapp, N. Awad, S. Adriaensen, M. Lindauer, and F. Hutter · 2020
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NAS-Bench-301 and the case for surrogate benchmarks for neural architecture search
J. Siems, L. Zimmer, A. Zela, J. Lukasik, M. Keuper, and F. Hutter · 2020
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Learning heuristic selection with dynamic algorithm configuration
D. Speck, A. Biedenkapp, F. Hutter, R. Mattmüller, and M. Lindauer · 2020
Later among the works it cites.
Learning heuristic selection with dynamic algorithm configuration
D. Speck, A. Biedenkapp, F. Hutter, R. Mattmüller, and M. Lindauer · 2021
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