[42] Donald R. Jones, Matthias Schonlau, and William J. Welch. Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13:455-492, 1998.
[43] Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, and Eric P. Xing. Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly. J. Mach. Learn. Res., 21(1), jan 2020.
[44] Jungtaek Kim, Seungjin Choi, and Minsu Cho. Combinatorial bayesian optimization with random mapping functions to convex polytopes. In James Cussens and Kun Zhang, editors, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, volume 180 of Proceedings of Machine Learning Research, pages 1001-1011. PMLR, 01-05 Aug 2022.
[45] Diederik P Kingma and Max Welling. Auto-Encoding Variational Bayes. arXiv e-prints, page arXiv:1312.6114, Dec 2013.
[46] A. Klein, S. Falkner, N. Mansur, and F. Hutter. Robo: A flexible and robust bayesian optimization framework in python. In NIPS 2017 Bayesian Optimization Workshop, December 2017.
[47] Dieter Kraft. A software package for sequential quadratic programming. Forschungsbericht-Deutsche Forschungs- und Versuchsanstalt für Luft- und Raumfahr, 1988.
[48] Rémi Lam, Matthias Poloczek, Peter Frazier, and Karen E Willcox. Advances in bayesian optimization with applications in aerospace engineering. In 2018 AIAA Non-Deterministic Approaches Conference, page 1656, 2018.
[49] Robert Langer and David Tirrell. Designing materials for biology and medicine. Nature, 428, 04 2004.
[50] Armin Lederer, Jonas Umlauft, and Sandra Hirche. Posterior variance analysis of gaussian processes with application to average learning curves. arXiv preprint arXiv:1906.01404, 2019.
[51] Eric Hans Lee, Valerio Perrone, Cedric Archambeau, and Matthias Seeger. Cost-aware Bayesian Optimization. arXiv e-prints, page arXiv:2003.10870, March 2020.
[52] Benjamin Letham, Brian Karrer, Guilherme Ottoni, and Eytan Bakshy. Constrained bayesian optimization with noisy experiments. Bayesian Analysis, 14(2):495-519, 06 2019. doi: 10. 1214/18-BA1110.
[53] Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III, and Tonio Buonassisi. Benchmarking the performance of bayesian optimization across multiple experimental materials science domains. npj Computational Materials, 7(1):188, 2021.
[54] Xingtao Liao, Qing Li, Xujing Yang, Weigang Zhang, and Wei Li. Multiobjective optimization for crash safety design of vehicles using stepwise regression model. Structural and Multidisciplinary Optimization, 35:561-569, 06 2008. doi: 10.1007/s00158-007-0163-x.
[55] Wenlong Lyu, Fan Yang, Changhao Yan, Dian Zhou, and Xuan Zeng. Batch Bayesian optimization via multi-objective acquisition ensemble for automated analog circuit design. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 3306-3314. PMLR, 10-15 Jul 2018. URL https://proceedings.mlr.press/v80/lyu18a.html.
[56] Martin Mächler. Accurately computing log (1- exp (-| al)) assessed by the rmpfr package. Technical report, Technical report, 2012.
[57] Mark McLeod, Stephen Roberts, and Michael A. Osborne. Optimization, fast and slow: optimally switching between local and Bayesian optimization. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 3443-3452. PMLR, 10-15 Jul 2018.