Hyperparameter optimization: Difference between revisions

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| bibcode = 2012arXiv1208.3719T
| arxiv = 1208.3719
}}</ref><ref name="turner-neurips21">{{cite journal
| last1 = Turner
| first1 = Ryan
| last2 = Eriksson
| first2 = David
| last3 = McCourt
| first3 = Michael
| last4 = Kiili
| first4 = Juha
| last5 = Laaksonen
| first5 = Eero
| last6 = Xu
| first6 = Zhen
| last7 = Guyon
| first7 = Isabelle
| title = Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
| journal = Proceedings of the NeurIPS 2020 Competition and Demonstration Track
| volume = 133
| pages = 3-26
| year = 2021
| publisher = PMLR,
| url = https://proceedings.mlr.press/v133/turner21a.html
}}</ref> to obtain better results in fewer evaluations compared to grid search and random search, due to the ability to reason about the quality of experiments before they are run.