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Bayesian Optimization (BO) is a framework for black-box optimization that is especially suitable for expensive cost functions.
1908
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1997
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C. Rasmussen and C. Williams, Gaussian Processes for Machine Learning , 1st ed. Cambridge, MA, USA: MIT Press, 2006
2006
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2010
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N. Srinivas, A. Krause, S. Kakade, and M. Seeger, “Gaussian process optimization in the bandit setting: No regret and experimental design,” in Proceedings of the 27th International Conference on Machine Learning , no. CONF. Omnipress, 2010
2010
Cited alongside, same era.
M. D. Hoffman, E. Brochu, and N. de Freitas, “Portfolio allocation for bayesian optimization.” in UAI . Citeseer, 2011, pp. 327–336
2011
Cited alongside, same era.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research , vol. 12, pp. 2825–2830, 2011
2011
Cited alongside, same era.
J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian optimization of machine learning algorithms,” in Advances in Neural Information Processing Systems 25 (NIPS) . Lake Tahoe, Nevada, USA: NIPS Foundation, 2012, pp. 2951–2959
J. González, M. Osborne, and N. D. Lawrence, “Glasses: Relieving the myopia of bayesian optimisation,” 2016
2016
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L. Kotthoff, C. Thornton, H. H. Hoos, F. Hutter, and K. Leyton-Brown, “Auto-weka 2.0: Automatic model selection and hyperparameter optimization in weka,” The Journal of Machine Learning Research , vol. 18, no. 1, pp. 826–830, 2017
2017
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K. Chatzilygeroudis, R. Rama, R. Kaushik, D. Goepp, V. Vassiliades, and J.-B. Mouret, “Black-box data-efficient policy search for robotics,” in Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on . IEEE, 2017, pp. 51–58
2017
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P. Bangalore, S. Letzgus, D. Karlsson, and M. Patriksson, “An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox,” Wind Energy , vol. 20, no. 8, pp. 1421–1438, 2017. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/we.2102
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2012
Cited alongside, same era.
B. Shahriari, Z. Wang, M. W. Hoffman, A. Bouchard-Côté, and N. de Freitas, “An entropy search portfolio for bayesian optimization,” Proc. NIPS Workshop Bayesian Optim. , 2014
2014
Cited alongside, same era.
W. Shen, J. Wang, Y.-G. Jiang, and H. Zha, “Portfolio choices with orthogonal bandit learning,” in Twenty-Fourth International Joint Conference on Artificial Intelligence , 2015
2015
Cited alongside, same era.
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, “Taking the human out of the loop: A review of bayesian optimization,” Proceedings of the IEEE , vol. 104, no. 1, pp. 148–175, 2015
2015
Cited alongside, same era.
2016
Cited alongside, same era.
R. Calandra, A. Seyfarth, J. Peters, and M. P. Deisenroth, “Bayesian optimization for learning gaits under uncertainty,” Annals of Mathematics and Artificial Intelligence , vol. 76, no. 1-2, pp. 5–23, 2016
2016
Cited alongside, same era.
The GPyOpt authors, “GPyOpt: A bayesian optimization framework in python,” http://github.com/SheffieldML/GPyOpt
2016
Cited alongside, same era.
2017
Later among the works it cites.
S. Falkner, A. Klein, and F. Hutter, “Bohb: Robust and efficient hyperparameter optimization at scale,” in International Conference on Machine Learning , 2018, pp. 1436–1445
2018
Later among the works it cites.
W. Lyu, F. Yang, C. Yan, D. Zhou, and X. Zeng, “Batch Bayesian optimization via multi-objective acquisition ensemble for automated analog circuit design,” in International Conference on Machine Learning , 2018, pp. 3312–3320
2018
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H. M. Torun, M. Swaminathan, A. K. Davis, and M. L. F. Bellaredj, “A global bayesian optimization algorithm and its application to integrated system design,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems , vol. 26, no. 4, pp. 792–802, 2018
2018
Later among the works it cites.
M. Feurer and F. Hutter, “Hyperparameter optimization,” in Automated Machine Learning . Springer, 2019, pp. 3–33
2019
Closest in time.
D. A. R. M. A. Souza, T. P. Vasconcelos, C. L. C. Mattos, and J. P. P. Gomes, “Evaluation of data based normal behavior models for fault detection in wind turbines,” in Brazilian Conference on Intelligent Systems , 2019, p. to appear
2019
Closest in time.