In this paper we develop a dynamic form of Bayesian optimization for machine learning models with the goal of rapidly finding good hyperparameter settings. Our method uses the partial information gained during the training of a machine learning model in order to decide whether to pause training and start a new model, or resume the training of a previously-considered model. We specifically tailor our method to machine learning problems by developing a novel positive-definite covariance kernel to capture a variety of training curves. Furthermore, we develop a Gaussian process prior that scales gracefully with additional temporal observations. Finally, we provide an information-theoretic framework to automate the decision process. Experiments on several common machine learning models show that our approach is extremely effective in practice.

本文介绍一种基于贝叶斯优化的机器学习超参数搜索方法，在训练过程中根据部分信息决定是否暂停、新建模型，以及恢复之前考虑过的模型。该方法特别适合机器学习问题，通过开发一种新的正定协方差核来捕捉多种训练曲线，并开发了一个能够与额外时间观察优雅扩展的高斯过程先验。此外，我们提供了一个信息理论的框架来自动化决策过程。在多种常见的机器学习模型上的实验表明，我们的方法在实践中非常有效。

冻融贝叶斯优化