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Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate model for the objective.
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A O’Hagan, · 1992
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“Derivative observations in Gaussian process models of dynamic systems,”
E Solak, S. R Murray, W. E Leithead, D. J Leith, and C. E Rasmussen, · 2003
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“Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach,”
A Krause and C Guestrin, · 2007
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“Nonparametric elicitation for heavy-tailed prior distributions,”
J. P Gosling, J. E Oakley, and A O’Hagan, · 2007
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“Gaussian processes with monotonicity information.,”
J. Riihimäki and A Vehtari, · 2010
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“Gaussian process optimization in the bandit setting: No regret and experimental design,”
N Srinivas, A Krause, S. M Kakade, and M Seeger, · 2010
Cited alongside, same era.
Engineering statistics handbook
C Carroll, T Paul, and Z Chelli, · 2013
Cited alongside, same era.
“Taking the human out of the loop: A review of Bayesian optimization,”
B Shahriari, K Swersky, Z Wang, R. P Adams, and N de Freitas, · 2016
Cited alongside, same era.
“Unbounded Bayesian optimization via regularization,”
B Shahriari, A Bouchard-Côté, and N de Freitas, · 2016
Cited alongside, same era.
“GLASSES: relieving the myopia of Bayesian optimisation,”
J González, M. A Osborne, and N. D Lawrence, · 2016
Cited alongside, same era.
“Minimum energy path calculations with Gaussian process regression,”
O.-P Koistinen, E Maras, A Vehtari, and H Jónsson, · 2016
Later among the works it cites.
“Estimating shape constrained functions using Gaussian processes,”
X Wang and J. O Berger, · 2016
Later among the works it cites.
“A stratified analysis of Bayesian optimization methods,”
I Dewancker, M McCourt, S Clark, P Hayes, A Johnson, and G Ke, · 2016
Later among the works it cites.
“Bayesian optimization with gradients,”
J Wu, M Poloczek, A. G Wilson, and P. I Frazier, · 2017
Closest in time.
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