[33] Sonja Surjanovic and Derek Bringham, 2013. URL https://www.sfu.ca/~ssurjano/optimization.html.
[34] Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of machine learning algorithms. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 25. Curran Associates, Inc., 2012. URL https://proceedings.neurips.cc/paper/2012/file/05311655a15b75fab86956663e1819cd-Paper.pdf.
[35] The GPyOpt authors. GPyOpt: A bayesian optimization framework in python. http://github.com/SheffieldML/GPyOpt, 2016.
[36] Jian Wu and Peter Frazier. The parallel knowledge gradient method for batch bayesian optimization. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc., 2016. URL https://proceedings.neurips.cc/paper_files/paper/2016/file/18d10dc6e666eab6de9215ae5b3d54df-Paper.pdf.
[37] 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.
[38] Andrei Paleyes, Mark Pullin, Maren Mahsereci, Cliff McCollum, Neil Lawrence, and Javier González. Emulation of physical processes with emukit. In Second Workshop on Machine Learning and the Physical Sciences, NeurIPS, 2019.
[39] Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, and Eric P. Xing. Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly. Journal of Machine Learning Research, 21(81):1-27, 2020. URL http://jmlr.org/papers/v21/18-223.html.
[40] Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider, and Eric Xing. Probo: Versatile bayesian optimization using any probabilistic programming language, 2019.
[41] Nicolas Knudde, Joachim van der Herten, Tom Dhaene, and Ivo Couckuyt. GPflowOpt: A Bayesian Optimization Library using TensorFlow. arXiv preprint - arXiv:1711.03845, 2017. URL https://arxiv.org/abs/1711.03845.
[42] Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, and Eytan Bakshy. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. In Advances in Neural Information Processing Systems 33, 2020. URL http://arxiv.org/abs/1910.06403.
[43] Jacob R Gardner, Geoff Pleiss, David Bindel, Kilian Q Weinberger, and Andrew Gordon Wilson. Gpytorch: Blackbox matrix-matrix gaussian process inference withgpu acceleration. In Advances in Neural Information Processing Systems, 2018.
[44] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. URL http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.
[45] Tea Tušar, Dimo Brockhoff, and Nikolaus Hansen. Mixed-integer benchmark problems for single- and bi-objective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '19, page 718–726, New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450361118. doi: 10.1145/3321707.3321868. URL https://doi.org/10.1145/3321707.3321868.