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Bayesian optimization has proven to be a highly effective methodology for the global optimization of unknown, expensive and multimodal functions.
A new method for locating the maximum point of an arbitrary multipeak curve in the presence of noise
H. J. Kushner · 1964
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Nonparametric estimation of nonstationary spatial covariance structure
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Unconstrained parameterizations for variance-covariance matrices
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D. Higdon, J. Swall, and J. Kern · 1998
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A taxonomy of global optimization methods based on response surfaces
Donald R. Jones · 2001
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Bayesian inference for nonstationary spatial covariance structures via spatial deformations
Alexandra M. Schmidt and Anthony O’Hagan · 2003
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Edward Snelson, Carl Edward Rasmussen, and Zoubin Ghahramani · 2003
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Gaussian Processes for Machine Learning
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Estimating deformations of isotropic gaussian random fields on the plane
Ethan B. Anderes and Michael L. Stein · 2008
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Gaussian process product models for nonparametric nonstationarity
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Practical Bayesian Optimization
Dan Lizotte · 2008
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Convergence rates of efficient global optimization algorithms
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Computationally efficient convolved multiple output gaussian processes
Mauricio A Alvarez and Neil D Lawrence · 2011
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Portfolio allocation for Bayesian optimization
Matthew Hoffman, Eric Brochu, and Nando de Freitas · 2011
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Contextual Gaussian process bandit optimization
Andreas Krause and Cheng Soon Ong · 2011
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Practical Bayesian optimization of machine learning algorithms
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Entropy search for information-efficient global optimization
Philipp Hennig and Christian J. Schuler · 2012
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Bayesian Gaussian Processes for Sequential Prediction, Optimisation and Quadrature
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Visualizing and understanding convolutional neural networks
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