Bayesian optimization

Bayesian optimization is a sequential design strategy for global optimization of black-box functions,[1][2][3] that does not assume any functional forms. It is usually employed to optimize expensive-to-evaluate functions. With the rise of artificial intelligence innovation in the 21st century, Bayesian optimizations have found prominent use in machine learning problems, for optimizing hyperparameter values.[4][5]

  1. ^ Cite error: The named reference Mockus1989 was invoked but never defined (see the help page).
  2. ^ Garnett, Roman (2023). Bayesian Optimization. Cambridge University Press. ISBN 978-1-108-42578-0.
  3. ^ Hennig, P.; Osborne, M. A.; Kersting, H. P. (2022). Probabilistic Numerics (PDF). Cambridge University Press. pp. 243–278. ISBN 978-1107163447.
  4. ^ Snoek, Jasper (2012). "Practical Bayesian Optimization of Machine Learning Algorithms". Advances in Neural Information Processing Systems 25 (NIPS 2012).
  5. ^ Klein, Aaron (2017). "Fast bayesian optimization of machine learning hyperparameters on large datasets". Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR: 528–536.