Optimizing a machine learning pipeline for a task at hand requires careful configuration of various hyperparameters, typically supported by an AutoML system that optimizes the hyperparameters for the given training dataset. Yet, depending on the AutoML system's own second-order meta-configuration, the performance of the AutoML process can vary significantly. Current AutoML systems cannot automatically adapt their own configuration to a specific use case. Further, they cannot compile user-defined application constraints on the effectiveness and efficiency of the pipeline and its generation. In this paper, we propose CAML, which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand. The dynamic AutoML strategy of CAML takes user-defined constraints into account and obtains constraint-satisfying pipelines with high predictive performance.

翻译：针对特定任务优化机器学习流程需要精细配置各种超参数，这通常由自动机器学习系统通过优化给定训练数据集的超参数来实现。然而，自动机器学习系统的性能会因其自身的二阶元配置差异而产生显著波动。现有自动机器学习系统无法自动调整其配置以适应特定应用场景，也无法将用户关于流程有效性及生成效率的约束条件纳入系统编译。本文提出CAML系统，采用元学习技术自动调整其自动机器学习参数（包括搜索策略、验证策略及搜索空间）以适应目标任务。CAML的动态自动机器学习策略可整合用户自定义约束条件，生成满足约束条件且具有高预测性能的流程。