首个可融合多目标先验知识的超参优化算法，提升深度学习调参效率。
Multi-objective Hyperparameter Optimization in the Age of Deep Learning
- 首次支持多目标用户先验信息的超参优化方法
- 在8个深度学习基准上表现超越现有方法
- 适合需要兼顾多个指标的深度学习实践者
尽管深度学习专家通常对高效超参数设置有先验知识，但现有的超参数优化（HPO）算法大多无法利用此类知识，且没有任何方法能处理多目标先验。由于深度学习实践者常需同时优化多个目标，这成为当前HPO算法的盲点。为此，我们提出PriMO，首个可集成多目标用户信念的HPO算法。实验表明，PriMO在8个深度学习基准上的单目标与多目标设置中均达到领先性能，显著优于现有方法，确立其作为深度学习从业者首选HPO工具的地位。
原文摘要 · Abstract (English)
While Deep Learning (DL) experts often have prior knowledge about which hyperparameter settings yield strong performance, only few Hyperparameter Optimization (HPO) algorithms can leverage such prior knowledge and none incorporate priors over multiple objectives. As DL practitioners often need to optimize not just one but many objectives, this is a blind spot in the algorithmic landscape of HPO. To address this shortcoming, we introduce PriMO, the first HPO algorithm that can integrate multi-objective user beliefs. We show PriMO achieves state-of-the-art performance across 8 DL benchmarks in the multi-objective and single-objective setting, clearly positioning itself as the new go-to HPO algorithm for DL practitioners.
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