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bayesian methodsgaussian processesgaussiansoptimizationoptimization problems

Citation

MLA
Wang, Z., et al. “Pre-trained Gaussian Processes for Bayesian Optimization”. Journal of Machine Learning Research, vol. 25, no. 212, 2024, pp. 1–3, https://www.jmlr.org/papers/v25/23-0269.html.
APA
Wang, Z., Dahl, G. E., Swersky, K., Lee, C., Nado, Z., Gilmer, J., Snoek, J., & Ghahramani, Z. (2024). Pre-trained Gaussian Processes for Bayesian Optimization. Journal of Machine Learning Research, 25(212), 1–83. https://www.jmlr.org/papers/v25/23-0269.html
Chicago
Wang, Z., G. E. Dahl, K. Swersky, et al. 2024. “Pre-trained Gaussian Processes for Bayesian Optimization”. Journal of Machine Learning Research 25 (212): 1–83. https://www.jmlr.org/papers/v25/23-0269.html.
Harvard
Wang, Z. et al. (2024) “Pre-trained Gaussian Processes for Bayesian Optimization”, Journal of Machine Learning Research, 25(212), pp. 1–83. Available at: https://www.jmlr.org/papers/v25/23-0269.html.
Vancouver
1. Wang Z, Dahl GE, Swersky K, Lee C, Nado Z, Gilmer J, Snoek J, Ghahramani Z (2024) Pre-trained Gaussian Processes for Bayesian Optimization. Journal of Machine Learning Research 25:1–83

BibTeX

@article{JMLR:v25:23-0269,
  author  = {Zi Wang and George E. Dahl and Kevin Swersky and Chansoo Lee and Zachary Nado and Justin Gilmer and Jasper Snoek and Zoubin Ghahramani},
  title   = {Pre-trained Gaussian Processes for Bayesian Optimization},
  journal = {Journal of Machine Learning Research},
  year    = {2024},
  volume  = {25},
  number  = {212},
  pages   = {1--83},
  url     = {http://jmlr.org/papers/v25/23-0269.html}
}
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