[12] Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan Rhys Griffiths, Alexandre Max Maraval, Hao Jianye, Jun Wang, Jan Peters, and Haitham Bou Ammar. Hebo pushing the limits of sample-efficient hyperparameter optimisation, 2022.
[13] Samuel Daulton, Maximilian Balandat, and Eytan Bakshy. Differentiable expected hypervolume improvement for parallel multi-objective bayesian optimization. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 9851-9864. Curran Associates, Inc., 2020. URL https://proceedings.neurips.cc/paper/2020/file/6fec24eac8f18ed793f5eaad3dd7977c-Paper.pdf.
[14] Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, and Eytan Bakshy. Robust multi-objective Bayesian optimization under input noise. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 4831-4866. PMLR, 17-23 Jul 2022. URL https://proceedings.mlr.press/v162/daulton22a.html.
[15] Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, and Eytan Bakshy. Bayesian optimization over discrete and mixed spaces via probabilistic reparameterization. In Advances in Neural Information Processing Systems 35, 2022.
[16] Filip De Roos, Alexandra Gessner, and Philipp Hennig. High-dimensional gaussian process inference with derivatives. In International Conference on Machine Learning, pages 2535-2545. PMLR, 2021.
[17] Kalyan Deb, L. Thiele, Marco Laumanns, and Eckart Zitzler. Scalable multi-objective optimization test problems. volume 1, pages 825-830, 06 2002. ISBN 0-7803-7282-4. doi: 10.1109/CEC.2002.1007032.
[18] Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, and David Eriksson. Bayesian optimization over high-dimensional combinatorial spaces via dictionary-based embeddings. In Francisco Ruiz, Jennifer Dy, and Jan-Willem van de Meent, editors, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, volume 206 of Proceedings of Machine Learning Research, pages 7021-7039. PMLR, 25-27 Apr 2023.
[19] Ryan M. Dreifuerst, Samuel Daulton, Yuchen Qian, Paul Varkey, Maximilian Balandat, Sanjay Kasturia, Anoop Tomar, Ali Yazdan, Vish Ponnampalam, and Robert W. Heath. Optimizing coverage and capacity in cellular networks using machine learning, 2021.
[20] M. T. M. Emmerich, K. C. Giannakoglou, and B. Naujoks. Single- and multiobjective evolutionary optimization assisted by gaussian random field metamodels. IEEE Transactions on Evolutionary Computation, 10(4):421-439, 2006.
[21] David Eriksson and Martin Jankowiak. High-dimensional Bayesian optimization with sparse axis-aligned subspaces. In Uncertainty in Artificial Intelligence. PMLR, 2021.
[22] David Eriksson and Matthias Poloczek. Scalable constrained Bayesian optimization. In International Conference on Artificial Intelligence and Statistics. PMLR, 2021.
[23] David Eriksson, Kun Dong, Eric Lee, David Bindel, and Andrew G Wilson. Scaling gaussian process regression with derivatives. Advances in neural information processing systems, 31, 2018.
[24] David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, and Matthias Poloczek. Scalable global optimization via local Bayesian optimization. In Advances in Neural Information Processing Systems 32, NeurIPS, 2019.
[25] Peter I Frazier. A tutorial on bayesian optimization. arXiv preprint arXiv:1807.02811, 2018.