Hyperparameter optimization plays a pivotal role in enhancing the predictive performance and generalization capabilities of ML models. However, in many applications, we do not only care about predictive performance but also about objectives such as inference time, memory, or energy consumption. In such MOO scenarios, determining the importance of hyperparameters poses a significant challenge due to the complex interplay between the conflicting objectives. In this paper, we propose the first method for assessing the importance of hyperparameters in the context of multi-objective hyperparameter optimization. Our approach leverages surrogate-based hyperparameter importance (HPI) measures, i.e. fANOVA and ablation paths, to provide insights into the impact of hyperparameters on the optimization objectives. Specifically, we compute the a-priori scalarization of the objectives and determine the importance of the hyperparameters for different objective tradeoffs. Through extensive empirical evaluations on diverse benchmark datasets with three different objectives paired with accuracy, namely time, demographic parity, and energy consumption, we demonstrate the effectiveness and robustness of our proposed method. Our findings not only offer valuable guidance for hyperparameter tuning in MOO tasks but also contribute to advancing the understanding of HPI in complex optimization scenarios.

我们提出了第一种方法，用于评估多目标超参数优化背景下超参数的重要性。我们的方法利用基于代理模型的超参数重要性度量，即fANOVA和消融路径，以便提供关于超参数对优化目标的影响的见解。通过在不同目标与准确性相配对的广泛实证评估，即时间、人口统计学平衡和能量消耗，在多样化的基准数据集上展示了我们提出的方法的有效性和鲁棒性。我们的发现不仅为MOO任务中的超参数调整提供了有价值的指导，也有助于推进对复杂优化情景中超参数重要性的理解。

多目标 AutoML 的超参数重要性分析