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}}</ref>, 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.<ref>{{cite journal |first=Jasper |last=Snoek |title=Practical Bayesian Optimization of Machine Learning Algorithms |journal=Advances in Neural Information Processing Systems 25 (NIPS 2012) |year=2012 |url=https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html}}</ref><ref>{{cite journal |first=Aaron |last=Klein |title=Fast bayesian optimization of machine learning hyperparameters on large datasets |journal=Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR |year=2017 |pages=528-536 |url=https://proceedings.mlr.press/v54/klein17a.html}}</ref>
==History==
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