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.

为手头的任务优化机器学习管道需要仔细配置各种超参数，通常由 AutoML 系统支持，该系统针对给定的训练数据集优化超参数。然而，根据 AutoML 系统自身的二阶元配置，AutoML 流程的性能可能会有很大差异。当前的 AutoML 系统无法自动调整自己的配置以适应特定的用例。此外，他们无法编译用户定义的应用程序对管道及其生成的有效性和效率的约束。在本文中，我们提出了Caml，它使用元学习来自动调整自己的 AutoML 参数，例如搜索策略、验证策略和搜索空间，以适应手头的任务。Caml的动态AutoML策略考虑了用户定义的约束，并获得具有高预测性能的满足约束的管道。