The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Bayesian Optimization
Abstract
In numerical analysis, this problem is typically called (global) optimization and has been the subject of decades of study. We draw a distinction between global optimization, where we seek the absolute optimum in X , and local optimization, where we seek to nd a local optimum in the neighborhood of a given initial point x0. A common approach to optimization problems is to make some assumptions about f . For example, when the objective function f is known to be convex and the domain X is also convex, the problem is known as convex optimization and has been widely studied. Convex optimization is a common tool used across machine learning.
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Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
On the limited memory BFGS method for large scale optimization
Liu, D. C., & Nocedal, J. (1989) · Mathematical Programming · doi:10.1007/bf01589116 · Cited by 8,560
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · Journal of Global Optimization · doi:10.1023/a:1008306431147 · Cited by 8,070
Statistical Decision Theory and Bayesian Analysis
Berger, J. O. (1985) · Springer Series in Statistics · doi:10.1007/978-1-4757-4286-2 · Cited by 7,722
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & de Freitas, N. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2494218 · Cited by 6,049
Cited by
Recent Advances in Bayesian Optimization
Wang, X., Jin, Y., Schmitt, S., & Olhofer, M. (2023) · ACM Computing Surveys · doi:10.1145/3582078 · Cited by 477
Two-step machine learning enables optimized nanoparticle synthesis
Mekki-Berrada, F., Ren, Z., Huang, T., Wong, W. K., Zheng, F., Xie, J., Tian, I. P. S., Jayavelu, S., Mahfoud, Z., Bash, D., et al. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00520-w · Cited by 267
Training of quantum circuits on a hybrid quantum computer
Zhu, D., Linke, N. M., Benedetti, M., Landsman, K. A., Nguyen, N. H., Alderete, C. H., Perdomo-Ortiz, A., Korda, N., Garfoot, A., Brecque, C., et al. (2019) · Science Advances · doi:10.1126/sciadv.aaw9918 · Cited by 215
Wang, Q., Qin, Y., Xie, J., Kong, Y., Sun, Q., Wei, Z., & Zhao, S. (2025) · Advanced Materials · doi:10.1002/adma.202420173 · Cited by 61
Accessory pathway analysis using a multimodal deep learning model
Nishimori, M., Kiuchi, K., Nishimura, K., Kusano, K., Yoshida, A., Adachi, K., Hirayama, Y., Miyazaki, Y., Fujiwara, R., Sommer, P., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-87631-y · Cited by 36
Metadata
- Authors
- Frazier, P. I.
- Venue
- Recent Advances in Optimization and Modeling of Contemporary Problems
- Publisher
- INFORMS
- Published
- 2018-10
- Pages
- 255-278
- OpenAlex
- W4238746485
- Cited by
- 826
- References
- 60
- FWCI
- 112.89
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Frazier, P. I. (2018). Bayesian Optimization. Recent Advances in Optimization and Modeling of Contemporary Problems. https://doi.org/10.1287/educ.2018.0188
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
