In many business settings, task-specific labeled data are scarce or costly to obtain, which limits supervised learning on a specific task. To address this challenge, we study sample sharing in the case of ridge regression: leveraging an auxiliary data set while explicitly protecting against negative transfer. We introduce a principled, data-driven rule that decides how many samples from an auxiliary dataset to add to the target training set. The rule is based on an estimate of the transfer gain i.e. the marginal reduction in the predictive error. Building on this estimator, we derive finite-sample guaranties: under standard conditions, the procedure borrows when it improves parameter estimation and abstains otherwise. In the Gaussian feature setting, we analyze which data set properties ensure that borrowing samples reduces the predictive error. We validate the approach in synthetic and real datasets, observing consistent gains over strong baselines and single-task training while avoiding negative transfer.

翻译：在许多商业场景中，任务特定的标注数据稀缺或获取成本高昂，这限制了针对特定任务的监督学习。为应对这一挑战，我们研究了岭回归情形下的样本共享问题：在利用辅助数据集的同时，明确防范负迁移。我们提出了一种基于原则的数据驱动规则，用于确定从辅助数据集中向目标训练集添加多少样本。该规则基于对迁移增益（即预测误差的边际减少量）的估计。基于此估计量，我们推导出有限样本保证：在标准条件下，该方法仅在能改进参数估计时进行样本借用，否则保持克制。在高斯特征设定下，我们分析了哪些数据集特性能够确保借用样本可降低预测误差。我们在合成数据集和真实数据集上验证了该方法，观察到相较于强基线和单任务训练，该方法能获得稳定增益，同时避免了负迁移。