[1] Ahmad Ajalloeian and Sebastian U Stich. On the convergence of SGD with biased gradients. arXiv preprint arXiv:2008.00051, 2020.
[2] Sébastien Bubeck et al. Convex optimization: Algorithms and complexity. Foundations and Trends® in Machine Learning, 8(3-4):231-357, 2015.
[3] Adam D. Bull. Convergence rates of efficient global optimization algorithms. Journal of Machine Learning Research, 12(88):2879-2904, 2011.
[4] Ioannis Chatzigeorgiou. Bounds on the Lambert function and their application to the outage analysis of user cooperation. IEEE Communications Letters, 17(8):1505-1508, 2013.
[5] Oleg Davydov and Robert Schaback. Error bounds for kernel-based numerical differentiation. Numerische Mathematik, 132:243-269, 2016.
[6] Nando De Freitas, Alex Smola, and Masour Zoghi. Exponential regret bounds for Gaussian process bandits with deterministic observations. In Proceedings of the 29th International Conference on Machine Learning, 2012.
[7] Aryan Deshwal and Jana Doppa. Combining latent space and structured kernels for Bayesian optimization over combinatorial spaces. In Advances in Neural Information Processing Systems, volume 34, pages 8185-8200, 2021.
[8] Luc Devroye and Gábor Lugosi. Combinatorial Methods in Density Estimation. Springer Science & Business Media, 2001.
[9] David Eriksson and Matthias Poloczek. Scalable constrained Bayesian optimization. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130, pages 730-738. PMLR, 2021.
[10] 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, volume 32, 2019.
[11] Roman Garnett. Bayesian Optimization. Cambridge University Press, 2023.
[12] Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, et al. High-dimensional Bayesian optimisation with variational autoencoders and deep metric learning. arXiv preprint arXiv:2106.03609, 2021.
[13] Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik. Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 4(2):268-276, 2018.
[14] Gautam Kamath. Bounds on the expectation of the maximum of samples from a Gaussian. 2015. URL http://www.gautamkamath.com/writings/gaussian_max.pdf.
[15] Kirthevasan Kandasamy, Jeff Schneider, and Barnabas Poczos. High dimensional Bayesian optimisation and bandits via additive models. In Proceedings of the 32nd International Conference on Machine Learning, volume 37, pages 295-304, 2015.
[16] Kenji Kawaguchi, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. Bayesian optimization with exponential convergence. In Advances in Neural Information Processing Systems, volume 28, 2015.
[17] Natalie Maus, Haydn Jones, Juston Moore, Matt J Kusner, John Bradshaw, and Jacob Gardner. Local latent space Bayesian optimization over structured inputs. In Advances in Neural Information Processing Systems, volume 35, pages 34505-34518, 2022.