解决两类分布同时漂移下的分类迁移问题，避免负迁移。
Neyman-Pearson Classification under Both Null and Alternative Distributions Shift
- 提出自适应方法，同时处理正负样本分布漂移
- 在源数据有信息时提升两类错误率，无信息时自动规避负迁移
- 兼具统计保证与高效计算，适合实际迁移场景
我们研究奈曼-皮尔逊分类中的迁移学习问题，目标是在约束μ₀分布上的误差不超过阈值的前提下，最小化μ₁分布上的误差。尽管传统分类的迁移学习已广泛研究，但对不平衡分类如奈曼-皮尔逊分类的迁移学习关注较少。该问题的独特挑战在于需同时控制两类错误。现有工作仅考虑μ₁分布漂移，而实际中μ₀和μ₁可能同时漂移。本文提出一种自适应方法，在源数据有信息时能有效降低一型与二型错误，且在源数据无信息时自动适应，避免负迁移。此外，该方法具有良好的统计保障与计算效率。
原文摘要 · Abstract (English)
We consider the problem of transfer learning in Neyman-Pearson classification, where the objective is to minimize the error w.r.t. a distribution $μ_1$, subject to the constraint that the error w.r.t. a distribution $μ_0$ remains below a prescribed threshold. While transfer learning has been extensively studied in traditional classification, transfer learning in imbalanced classification such as Neyman-Pearson classification has received much less attention. This setting poses unique challenges, as both types of errors must be simultaneously controlled. Existing works address only the case of distribution shift in $μ_1$, whereas in many practical scenarios shifts may occur in both $μ_0$ and $μ_1$. We derive an adaptive procedure that not only guarantees improved Type-I and Type-II errors when the source is informative, but also automatically adapt to situations where the source is uninformative, thereby avoiding negative transfer. In addition to such statistical guarantees, the procedures is efficient, as shown via complementary computational guarantees.
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