提出ShakyPrepend方法，提升多组学习的样本效率与泛化能力。
ShakyPrepend: A Multi-Group Learner with Improved Sample Complexity
- 基于差分隐私思想设计多组学习算法
- 实验证明可适应组结构与空间异质性
- 适合需要公平性保障的现实场景应用
多组学习旨在控制预测器在特定子群体上的条件损失。我们提出ShakyPrepend方法，借鉴差分隐私工具，获得优于现有方法的理论保证。通过数值实验，证明ShakyPrepend能适应组结构与空间异质性。研究为多组学习算法在真实场景中的部署提供了实用指导。
原文摘要 · Abstract (English)
Multi-group learning is a learning task that focuses on controlling predictors' conditional losses over specified subgroups. We propose ShakyPrepend, a method that leverages tools inspired by differential privacy to obtain improved theoretical guarantees over existing approaches. Through numerical experiments, we demonstrate that ShakyPrepend adapts to both group structure and spatial heterogeneity. We provide practical guidance for deploying multi-group learning algorithms in real-world settings.
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