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Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results.
pysot and poap: An event-driven asynchronous framework for surrogate optimization
D. Eriksson, D. Bindel, and C. Shoemaker · 1908
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Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown · 2011
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Cox · 2013
Cited alongside, same era.
Maximum projection designs for computer experiments
V. Roshan Joseph, Evren Gul, and Shan Ba · 2015
Cited alongside, same era.
Scalable bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat Prabhat, and Ryan P. Adams · 2015
Cited alongside, same era.
Bayesian optimization for policy search via online-offline experimentation
Benjamin Letham and Eytan Bakshy · 2019
Cited alongside, same era.
URL https://bbochallenge.com
Black-box optimization challenge
Cited in the paper.
Hebo: Heteroscedastic evolutionary bayesian optimisation
Alexander I. Cowen-Rivers, Wenlong Lyu, Zhi Wang, Rasul Tutunov, Hao Jianye, Jun Wang, and Haitham Bou Ammar
Cited in the paper.
Scalable global optimization via local bayesian optimization
David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, and Matthias Poloczek
Cited in the paper.
Gpu accelerated exhaustive search for optimal ensemble of black-box optimization algorithms
Jiwei Liu, Bojan Tunguz, and Gilberto Titericz
Cited in the paper.
Higher performance for automl: The benefit of various ensemble bayesian optimization strategy
Jiajun Wu, Manliang Cao, Manliang Cao, and Qing Yang
Cited in the paper.
Sample-efficient neural architecture search by learning action space
Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, and Yuandong Tian · 2019
Later among the works it cites.
Bayesian optimization for adaptive experimental design: A review
Stewart Greenhill, Santu Rana, Sunil Gupta, Pratibha Vellanki, and Svetha Venkatesh · 2020
Closest in time.
Team optuna developers’ method for black-box optimization challenge 2020
Masashi Shibata, Toshihiko Yanase, Hideaki Imamura, Masahiro Nomura, Takeru Ohta, Shotaro Sano, and Hiroyuki Vincent Yamazaki · 2020
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Learning search space partition for black-box optimization using monte carlo tree search
Linnan Wang, Rodrigo Fonseca, and Yuandong Tian · 2020
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