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Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
Thompson, William R · 1933
Earlier work this paper cites.
Random forests
Breiman, Leo · 2001
Earlier work this paper cites.
The nonstochastic multiarmed bandit problem
Auer, Peter, Cesa-Bianchi, Nicolo, Freund, Yoav, and Schapire, Robert E · 2002
Earlier work this paper cites.
Gaussian processes for machine learning
Rasmussen, C E and Williams, C K I · 2006
Earlier work this paper cites.
Gaussian processes for machine learning
Rasmussen, C E and Williams, C K I · 2006
Earlier work this paper cites.
Brochu, Eric, Cora, Vlad M, and De Freitas, Nando · 2010
Earlier work this paper cites.
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MNIST handwritten digit database
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Earlier work this paper cites.
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Earlier work this paper cites.
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Bergstra, J S, Bardenet, R, Bengio, Y, and Kégl, B · 2011
Earlier work this paper cites.
Sequential model-based optimization for general algorithm configuration
Hutter, Frank, Hoos, Holger H, and Leyton-Brown, Kevin · 2011
Earlier work this paper cites.
Revisiting k-means: New algorithms via Bayesian nonparametrics
Kulis, Brian and Jordan, Michael I · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
Earlier work this paper cites.
Entropy search for information-efficient global optimization
Hennig, Philipp and Schuler, Christian J · 2012
Earlier work this paper cites.
Practical Bayesian optimization of machine learning algorithms
Snoek, Jasper, Larochelle, Hugo, and Adams, Ryan P · 2012
Earlier work this paper cites.
Parallel Gaussian process optimization with upper confidence bound and pure exploration
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Surrogate models for mixed discrete-continuous variables
Swiler, Laura P, Hough, Patricia D, Qian, Peter, Xu, Xu, Storlie, Curtis, and Lee, Herbert · 2014
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Predictive entropy search for Bayesian optimization with unknown constraints
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A tutorial on Bayesian optimization
Frazier, Peter I · 2018
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Algorithmic assurance: An active approach to algorithmic testing using Bayesian optimisation
Gopakumar, Shivapratap, Gupta, Sunil, Rana, Santu, Nguyen, Vu, and Venkatesh, Svetha · 2018
Later among the works it cites.
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Russo, Daniel J, Van Roy, Benjamin, Kazerouni, Abbas, Osband, Ian, Wen, Zheng, et al · 2018
Later among the works it cites.
Asynchronous batch bayesian optimisation with improved local penalisation
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