Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL). Practitioners are often faced with the trade-off between multiple criteria, such as accuracy and latency. Given the high computational needs of DL and the growing demand for efficient HPO, the acceleration of multi-objective (MO) optimization becomes ever more important. Despite the significant body of work on meta-learning for HPO, existing methods are inapplicable to MO tree-structured Parzen estimator (MO-TPE), a simple yet powerful MO-HPO algorithm. In this paper, we extend TPE's acquisition function to the meta-learning setting using a task similarity defined by the overlap of top domains between tasks. We also theoretically analyze and address the limitations of our task similarity. In the experiments, we demonstrate that our method speeds up MO-TPE on tabular HPO benchmarks and attains state-of-the-art performance. Our method was also validated externally by winning the AutoML 2022 competition on "Multiobjective Hyperparameter Optimization for Transformers".

翻译：超参数优化（HPO）是提升深度学习（DL）性能的关键步骤。从业者常需在多个标准（如准确率和延迟）之间进行权衡。鉴于深度学习的高计算需求以及对高效HPO日益增长的需求，加速多目标（MO）优化变得愈发重要。尽管关于HPO元学习的研究已有大量成果，现有方法却无法适用于多目标树结构Parzen估计器（MO-TPE）——一种简单而强大的MO-HPO算法。本文通过基于任务间顶级域重叠定义的任务相似性，将TPE的采集函数扩展至元学习场景，并从理论上分析并解决了该任务相似性的局限性。实验表明，我们的方法在表格型HPO基准测试中加速了MO-TPE，并达到了最先进性能。该方法还通过赢得2022年AutoML竞赛“多目标超参数优化在Transformer中的应用”获得了外部验证。