论文标题
MOFA：超参数优化的模块化阶乘设计
MOFA: Modular Factorial Design for Hyperparameter Optimization
论文作者
论文摘要
本文提出了一种新颖而轻的超参数优化（HPO）方法，模块化阶乘设计（MOFA）。 MOFA追求几轮HPO，每一轮通过阶乘设计对超参数空间的探索和通过阶乘分析对评估结果的开发进行交替。每个回合首先通过构造一组低分配的超参数范围，涵盖了该空间，同时可以通过驱动相关的超参数来探索配置空间，然后通过阶乘分析来利用评估结果，该分析确定应进一步探索哪些超牌仪并应在下一轮中固定。我们证明，与其他抽样方案相比，MOFA的推论具有更高的信心。每个单独的一轮都是高度可行的，因此与基于模型的方法相比，效率的重大提高。经验结果表明，与最先进的方法相比，MOFA可以提高有效性和效率。
This paper presents a novel and lightweight hyperparameter optimization (HPO) method, MOdular FActorial Design (MOFA). MOFA pursues several rounds of HPO, where each round alternates between exploration of hyperparameter space by factorial design and exploitation of evaluation results by factorial analysis. Each round first explores the configuration space by constructing a low-discrepancy set of hyperparameters that cover this space well while de-correlating hyperparameters, and then exploits evaluation results through factorial analysis that determines which hyperparameters should be further explored and which should become fixed in the next round. We prove that the inference of MOFA achieves higher confidence than other sampling schemes. Each individual round is highly parallelizable and hence offers major improvements of efficiency compared to model-based methods. Empirical results show that MOFA achieves better effectiveness and efficiency compared with state-of-the-art methods.
