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Bayesian optimization is a popular framework for the optimization of black box functions.
The application of bayesian methods for seeking the extremum
Jonas Mockus, Vytautas Tiesis, and Antanas Zilinskas · 1978
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Latin hypercube sampling as a tool in uncertainty analysis of computer models
Michael D McKay · 1992
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Dynamic programming and optimal control: Volume I
Dimitri Bertsekas · 1995
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Efficient global optimization of expensive black-box functions
Donald R Jones, Matthias Schonlau, and William J Welch · 1998
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Predicting the output from a complex computer code when fast approximations are available
Marc C Kennedy and Anthony O’Hagan · 2000
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Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
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Sequential kriging optimization using multiple-fidelity evaluations
Deng Huang, Theodore T Allen, William I Notz, and R Allen Miller · 2006
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Multi-fidelity optimization via surrogate modelling
Alexander IJ Forrester, András Sóbester, and Andy J Keane · 2007
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Approximate Dynamic Programming: Solving the curses of dimensionality
Warren B Powell · 2007
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Engineering design via surrogate modelling: a practical guide
Alexander Forrester, Andras Sobester, and Andy Keane · 2008
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Gaussian processes for global optimization
Michael A Osborne, Roman Garnett, and Stephen J Roberts · 2009
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger · 2009
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Towards gaussian process-based optimization with finite time horizon
David Ginsbourger and Rodolphe Le Riche · 2010
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Entropy search for information-efficient global optimization
Philipp Hennig and Christian J Schuler · 2012
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Sequential design and analysis of high-accuracy and low-accuracy computer codes
Shifeng Xiong, Peter ZG Qian, and CF Jeff Wu · 2013
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Predictive entropy search for efficient global optimization of black-box functions
Remarks on multi-fidelity surrogates
Chanyoung Park, Raphael T Haftka, and Nam H Kim · 2017
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Multi-information source optimization
Matthias Poloczek, Jialei Wang, and Peter I Frazier · 2017
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Lookahead bayesian optimization with inequality constraints
Remi Lam and Karen Willcox · 2017
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Max-value entropy search for efficient bayesian optimization
Zi Wang and Stefanie Jegelka · 2017
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Survey of multifidelity methods in uncertainty propagation, inference, and optimization
Benjamin Peherstorfer, Karen Willcox, and Max Gunzburger · 2018
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Maximizing acquisition functions for bayesian optimization
James T Wilson, Frank Hutter, and Marc Peter Deisenroth · 2018
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José Miguel Hernández-Lobato, Matthew W Hoffman, and Zoubin Ghahramani · 2014
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Gaussian process optimisation with multi-fidelity evaluations
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Review of multi-fidelity models
M Giselle Fernández-Godino, Chanyoung Park, Nam-Ho Kim, and Raphael T Haftka · 2016
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Bayesian optimization with a finite budget: An approximate dynamic programming approach
Remi Lam, Karen Willcox, and David H Wolpert · 2016
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Glasses: Relieving the myopia of bayesian optimisation
Javier González, Michael Osborne, and Neil Lawrence · 2016
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Information-based multi-fidelity bayesian optimization
Yehong Zhang, Trong Nghia Hoang, Bryan Kian Hsiang Low, and Mohan Kankanhalli · 2017
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Comparison of multi-fidelity approaches for military vehicle design
Philip S Beran, Dean Bryson, Andrew S Thelen, Matteo Diez, and Andrea Serani · 2020
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Multi-fidelity bayesian optimization with max-value entropy search and its parallelization
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, and Masayuki Karasuyama · 2020
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Practical multi-fidelity bayesian optimization for hyperparameter tuning
Jian Wu, Saul Toscano-Palmerin, Peter I Frazier, and Andrew Gordon Wilson · 2020
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Multi-fidelity bayesian optimization via deep neural networks
Shibo Li, Wei Xing, Robert Kirby, and Shandian Zhe · 2020
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Analytical benchmark problems for multifidelity optimization methods
L Mainini, A Serani, MP Rumpfkeil, E Minisci, D Quagliarella, H Pehlivan, S Yildiz, S Ficini, R Pellegrini, F Di Fiore, et al · 2022
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