MOFA：用于超参数优化的模块析因设计
MOFA: Modular Factorial Design for Hyperparameter Optimization
摘要：
提出了一种新的轻量级超参数优化方法--模块因子设计法。MOFA进行了几轮HPO，每一轮在通过因子设计探索超参数空间和通过因子分析利用评估结果之间交替进行。每一轮首先通过构建一组低差异的超参数来探索配置空间，这些超参数在去相关超参数的同时很好地覆盖了这个空间，然后通过因子分析来利用评估结果，该因子分析确定哪些超参数应该进一步探索，哪些应该在下一轮中固定下来。我们证明了MOFA的推理比其他抽样方案具有更高的置信度。每一轮都是高度并行的，因此与基于模型的方法相比，效率有了很大的提高。实验结果表明，MOFA实现了更好的效果和效率相比，国家的最先进的方法。
英文摘要：
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.
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