根据数据自适应选择最优源数据，避免负迁移。
When to Transfer: Adaptive Source Selection for Positive Transfer in Linear Models
- 基于目标数据估计迁移收益，决定是否采纳源数据样本。
- 在真实与合成数据上均优于经典及最新基线方法。
- 适合标签数据稀缺的业务场景，提升线性模型性能。
在许多商业场景中，特定任务的标注数据稀缺或获取成本高，限制了监督学习的应用。传统解决方案是迁移学习（TL）。现有研究多关注如何从相关源任务迁移信息，本文聚焦于线性回归与分类任务中通过样本共享实现有效迁移的时机问题。在多源设置下，我们提出一种贪心策略：基于观测到的目标样本，计算条件化的迁移收益（即预测误差的边际下降），并据此决定从哪些源中选取多少样本加入目标数据集。该方法采用基于数据的接受/拒绝规则，在统计上保证正向迁移的高概率。在标准假设下，进一步分析迁移收益，刻画出迁移有益的条件。在合成与真实数据上的实验表明，本方法持续优于经典及近期强基线，且能有效避免负迁移。
原文摘要 · Abstract (English)
In many business settings, task-specific labeled data are scarce or costly to obtain, limiting supervised learning on a target task. A classical response is transfer learning (TL). Many TL works study how to transfer information from related sources. We study, for linear regression and classification, when to transfer via sample sharing: in a multi-source setting, we greedily decide from which sources and how many samples to incorporate into the target dataset. Our method uses an accept/reject rule based on a data-dependent estimate of the transfer gain, i.e the marginal decrease in target predictive error, computed conditionally on the observed target samples. We analyze our approach and show that how the derived statistical test enforces positive transfer with high probability. Under additional standard conditions, we also study the transfer gain itself and characterize when transfer is beneficial. Experiments on synthetic and real data show consistent gains over classical and recent strong baselines while avoiding negative transfer.
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