论文标题
通过学习当地贝叶斯优化的学习搜索空间分区来解决黑盒优化挑战
Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization
论文作者
论文摘要
黑框优化是机器学习中的重要任务之一，因为它近似于现实世界的条件，因为我们并不总是知道给定系统的所有属性，只能知道结果几乎什么都不知道。本文介绍了我们通过学习搜索空间分区的当地贝叶斯优化来解决2020年神经盒优化挑战的方法。我们描述了挑战的任务以及我们命名为\ texttt {spbopt}的低预算优化的算法。我们使用多任务贝叶斯优化前两个评估设置的结果来优化算法的超参数为竞争决赛。我们的方法在决赛中排名第三。
Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results. This paper describes our approach to solving the black-box optimization challenge at NeurIPS 2020 through learning search space partition for local Bayesian optimization. We describe the task of the challenge as well as our algorithm for low budget optimization that we named \texttt{SPBOpt}. We optimize the hyper-parameters of our algorithm for the competition finals using multi-task Bayesian optimization on results from the first two evaluation settings. Our approach has ranked third in the competition finals.
