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Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization.
A limited memory algorithm for bound constrained optimization
R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu · 1995
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Information Theory, Inference and Learning Algorithms
D. J. C. Mackay · 2003
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Pattern Recognition and Machine Learning
C. M. Bishop · 2006
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Gaussian Processes for Machine Learning
C. Rasmussen and C. Williams · 2006
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Random features for large-scale kernel machines
A. Rahimi, B. Recht, et al · 2007
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LIBSVM: A library for support vector machines
C.-C. Chang and C.-J. Lin · 2011
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Contextual gaussian process bandit optimization
A. Krause and C. S. Ong · 2011
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Efficient benchmarking of hyperparameter optimizers via surrogates background: hyperparameter optimization
K. Eggensperger, F. Hutter, H. Hoos, and K. Leyton-brown · 2012
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Collaborative hyperparameter tuning
R. Bardenet, M. Brendel, B. Kégl, and M. Sebag · 2013
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Multi-task Bayesian optimization
K. Swersky, J. Snoek, and R. P. Adams · 2013
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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OpenML: networked science in machine learning
J. Vanschoren, J. N. Van Rijn, B. Bischl, and L. Torgo · 2014
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Efficient transfer learning method for automatic hyperparameter tuning
D. Yogatama and G. Mann · 2014
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang · 2015
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Initializing Bayesian hyperparameter optimization via meta-learning
M. Feurer, T. Springenberg, and F. Hutter · 2015
Sparse gaussian processes for Bayesian optimization
M. McIntire, D. Ratner, and S. Ermon · 2016
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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
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Bayesian optimization with robust Bayesian neural networks
J. T. Springenberg, A. Klein, S. Falkner, and F. Hutter · 2016
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Probabilistic matrix factorization for automated machine learning
N. Fusi and H. M. Elibol · 2017
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Google vizier: A service for black-box optimization
D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley · 2017
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Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space
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Scalable Bayesian optimization using deep neural networks
J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. Patwary, M. Prabhat, and R. Adams · 2015
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http://github.com/SheffieldML/GPyOpt
GPyOpt · 2016
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J. M. Hernández-Lobato, J. Requeima, E. O. Pyzer-Knapp, and A. Aspuru-Guzik · 2017
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Bayesian optimization with tree-structured dependencies
R. Jenatton, C. Archambeau, J. Gonzales, and M. Seeger · 2017
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Auto-differentiating linear algebra
M. Seeger, A. Hetzel, Z. Dai, and N. D. Lawrence · 2017
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