Hyperparameter optimization (HPO) is important to leverage the full potential of machine learning (ML). In practice, users are often interested in multi-objective (MO) problems, i.e., optimizing potentially conflicting objectives, like accuracy and energy consumption. To tackle this, the vast majority of MO-ML algorithms return a Pareto front of non-dominated machine learning models to the user. Optimizing the hyperparameters of such algorithms is non-trivial as evaluating a hyperparameter configuration entails evaluating the quality of the resulting Pareto front. In literature, there are known indicators that assess the quality of a Pareto front (e.g., hypervolume, R2) by quantifying different properties (e.g., volume, proximity to a reference point). However, choosing the indicator that leads to the desired Pareto front might be a hard task for a user. In this paper, we propose a human-centered interactive HPO approach tailored towards multi-objective ML leveraging preference learning to extract desiderata from users that guide the optimization. Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator. Concretely, we leverage pairwise comparisons of distinct Pareto fronts to learn such an appropriate quality indicator. Then, we optimize the hyperparameters of the underlying MO-ML algorithm towards this learned indicator using a state-of-the-art HPO approach. In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick.

翻译：超参数优化对于充分释放机器学习潜力至关重要。在实践中，用户通常关注多目标问题，即优化可能存在冲突的目标，例如准确性和能耗。为解决这一问题，绝大多数多目标机器学习算法会向用户返回一个由非支配机器学习模型构成的帕累托前沿。优化此类算法的超参数并非易事，因为评估某一超参数配置需要衡量其所生成帕累托前沿的质量。现有文献中，已有多种指标通过量化不同属性（如超体积、R2指标）来评估帕累托前沿质量（例如体积、与参考点的接近程度）。然而，用户可能难以选择能导向理想帕累托前沿的指标。本文提出一种以人为中心的交互式超参数优化方法，专为多目标机器学习设计，利用偏好学习从用户需求中提取优化导向的期望特征。与传统方法依赖用户猜测最适配指标不同，本方法可自动学习合适指标。具体而言，我们通过对不同帕累托前沿进行成对比较来学习此类质量指标，进而采用最先进的超参数优化方法，基于该学习到的指标优化底层多目标机器学习算法的超参数。在面向机器学习环境影响的实验研究中，我们证明：与用户预先错误选择指标进行优化相比，本方法能生成显著更优的帕累托前沿；即使面对具备指标选择专业知识的高级用户，其优化效果也相当。