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Bayesian optimization (BO) is a class of global optimization algorithms, suitable for minimizing an expensive objective function in as few function evaluations as possible.
The application of Bayesian methods for seeking the extremum
Mockus, J., Tiesis, V., and Zilinskas, A · 1978
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Large sample properties of simulations using latin hypercube sampling
Stein, M · 1987
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Low-discrepancy and low-dispersion sequences
Niederreiter, H · 1988
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Convex optimization
Boyd, S. and Vandenberghe, L · 2004
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Warped gaussian processes
Snelson, E., Ghahramani, Z., and Rasmussen, C. E · 2004
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Gaussian Processes for Machine Learning
Rasmussen, C. E. and Williams, C. K · 2006
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Multi-fidelity optimization via surrogate modelling
Forrester, A. I., Sóbester, A., and Keane, A. J · 2007
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Modern experimental design
Ryan, T. P. and Morgan, J · 2007
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Predicting execution time of computer programs using sparse polynomial regression
Huang, L., Jia, J., Yu, B., Chun, B.-G., Maniatis, P., and Naik, M · 2010
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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
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Predicting execution time of machine learning tasks using metalearning
Priya, R., de Souza, B. F., Rossi, A. L., and de Carvalho, A. C · 2011
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Experimental design
Kirk, R. E · 2012
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Performance modeling for dense linear algebra
Peise, E. and Bientinesi, P · 2012
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Design of computer experiments: space filling and beyond
Pronzato, L. and Müller, W. G · 2012
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Practical Bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P · 2012
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Numerical studies of space-filling designs: optimization of latin hypercube samples and subprojection properties
Damblin, G., Couplet, M., and Iooss, B · 2013
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Adaptive algorithm for minimizing cloud task length with prediction errors
Di, S., Wang, C.-L., and Cappello, F · 2013
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Multi-task bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P · 2013
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Designing nanostructures for phonon transport via bayesian optimization
Ju, S., Shiga, T., Feng, L., Hou, Z., Tsuda, K., and Shiomi, J · 2017
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Multi-fidelity bayesian optimisation with continuous approximations
Kandasamy, K., Dasarathy, G., Schneider, J., and Poczos, B · 2017
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Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Klein, A., Falkner, S., Bartels, S., Hennig, P., and Hutter, F · 2017
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A · 2017
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Multi-information source optimization
Poloczek, M., Wang, J., and Frazier, P · 2017
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Minimax and maximin space-filling designs: some properties and methods for construction
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Predictive entropy search for efficient global optimization of black-box functions
Hernández-Lobato, J. M., Hoffman, M. W., and Ghahramani, Z · 2014
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Algorithm runtime prediction: Methods & evaluation
Hutter, F., Xu, L., Hoos, H. H., and Leyton-Brown, K · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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http://github.com/SheffieldML/GPyOpt
GPyOpt: A Bayesian optimization framework in Python · 2016
Cited alongside, same era.
Fast bayesian optimization of machine learning hyperparameters on large datasets
Klein, A., Falkner, S., Bartels, S., Hennig, P., and Hutter, F · 2016
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Taking the human out of the loop: A review of Bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and de Freitas, N · 2016
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Pronzato, L · 2017
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Max-value entropy search for efficient Bayesian optimization
Wang, Z. and Jegelka, S · 2017
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Bohb: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F · 2018
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A tutorial on bayesian optimization
Frazier, P. I · 2018
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Maximizing acquisition functions for bayesian optimization
Wilson, J., Hutter, F., and Deisenroth, M · 2018
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Oboe: Collaborative filtering for automl initialization
Yang, C., Akimoto, Y., Kim, D. W., and Udell, M · 2018
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Cost-aware multi-objective bayesian optimisation
Abdolshah, M., Shilton, A., Rana, S., Gupta, S., and Venkatesh, S · 2019
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Practical multi-fidelity bayesian optimization for hyperparameter tuning
Wu, J., Toscano-Palmerin, S., Frazier, P. I., and Wilson, A. G · 2019
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