标题： 基于模型平均的合理性迁移学习 显示英文标题

标题： Sufficiency-principled Transfer Learning via Model Averaging

摘要： 当可转移集合不可知时，尽可能传递信息性知识的\textemdash 一个我们称之为\emph{充分性}的原则，对于提高迁移学习效果变得至关重要。 然而，现有的迁移学习方法不仅忽视了充分性原则，还依赖于限制性的单一相似性假设（\eg 个体或组合相似性），导致性能次优。 为了解决这些限制，我们通过统一模型平均算法提出了一种基于充分性原则的迁移学习框架，能够同时适应个体和组合相似性。 理论上，我们建立了多源线性回归模型在参数数量发散情况下的渐近/高概率最优性、增强的收敛速度和渐近正态性，实现了充分性、对负迁移的鲁棒性、隐私保护和可行的统计推断。 广泛的模拟和对北京住房租金数据的经验分析证明了我们的框架相对于传统方法的优越性。 显示英文摘要

摘要： When the transferable set is unknowable, transfering informative knowledge as much as possible\textemdash a principle we refer to as \emph{sufficiency}, becomes crucial for enhancing transfer learning effectiveness. However, existing transfer learning methods not only overlook the sufficiency principle, but also rely on restrictive single-similarity assumptions (\eg individual or combinatorial similarity), leading to suboptimal performance. To address these limitations, we propose a sufficiency-principled transfer learning framework via unified model averaging algorithms, accommodating both individual and combinatorial similarities. Theoretically, we establish the asymptotic/high-probability optimality, enhanced convergence rate and asymptotic normality for multi-source linear regression models with a diverging number of parameters, achieving sufficiency, robustness to negative transfer, privacy protection and feasible statistical inference. Extensive simulations and an empirical data analysis of Beijing housing rental data demonstrate the promising superiority of our framework over conventional alternatives.