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Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions.
The application of Bayesian methods for seeking the extremum
Jonas Mockus, Vytautas Tiesis, and Antanas Zilinskas · 1978
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A single series from the Gibbs sampler provides a false sense of security
Andrew Gelman and Donald R. Rubin · 1992
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John Geweke · 1992
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A. Shapiro, D. Dentcheva, and A. Ruszczynski · 2009
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UCI machine learning repository, 2010
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Exponential regret bounds for Gaussian process bandits with deterministic observations
Nando de Freitas, Alex Smola, and Masrour Zoghi · 2012
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Entropy search for information-efficient global optimization
Philipp Hennig and Christian J. Schuler · 2012
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Adaptive MCMC with Bayesian optimization
Nimalan Mahendran, Ziyu Wang, Firas Hamze, and Nando de Freitas · 2012
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Practical Bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P. Adams · 2012
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Josip Djolonga, Andreas Krause, and Volkan Cevher · 2013
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Algorithms for hyper-parameter optimization
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Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown · 2011
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Contextual Gaussian Process bandit optimization
Andreas Krause and Cheng Soon Ong · 2011
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A tutorial on Bayesian optimization of expensive cost functions, 2010b
Eric Brochu, Vlad M. Cora, and Nando de Freitas
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Bayesian optimization in high dimensions via random embeddings
Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, and Nando de Freitas · 2013
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Active learning for multi-objective optimization
Marcela Zuluaga, Andreas Krause, Guillaume Sergent, and Markus Püschel · 2013
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