论文标题
基于从数据到超参数的映射发现的自动超参数优化
Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters
论文作者
论文摘要
机器学习算法在人工智能领域取得了显着的成就。但是，大多数机器学习算法对超参数敏感。手动优化超参数是一种常见的高参数调整方法。但是，这是昂贵且经验依赖的。自动高参数优化（AUTOHPO）由于其有效性而受到青睐。但是，当前的AutoHPO方法通常仅对某种类型的问题有效，并且时间成本很高。在本文中，我们提出了一种有效的自动参数优化方法，该方法基于从数据到相应的超参数的映射。为了描述这样的映射，我们提出了一个复杂的网络结构。为了获得此类映射，我们开发有效的网络约束算法。我们还设计策略，以在应用映射期间优化结果。广泛的实验结果表明，所提出的方法的表现明显优于最先进的适应性。
Machine learning algorithms have made remarkable achievements in the field of artificial intelligence. However, most machine learning algorithms are sensitive to the hyper-parameters. Manually optimizing the hyper-parameters is a common method of hyper-parameter tuning. However, it is costly and empirically dependent. Automatic hyper-parameter optimization (autoHPO) is favored due to its effectiveness. However, current autoHPO methods are usually only effective for a certain type of problems, and the time cost is high. In this paper, we propose an efficient automatic parameter optimization approach, which is based on the mapping from data to the corresponding hyper-parameters. To describe such mapping, we propose a sophisticated network structure. To obtain such mapping, we develop effective network constrution algorithms. We also design strategy to optimize the result futher during the application of the mapping. Extensive experimental results demonstrate that the proposed approaches outperform the state-of-the-art apporaches significantly.
