题目：Trans-MA: Sufficiency-principled Transfer Learning via Model Averaging

报告人：刘慧航 (中国科学技术大学)

摘要：Domain aggregation in multi-source transfer learning faces a critical challenge: effectively integrating knowledge from heterogeneous sources while addressing statistical uncertainties. Existing methods rely on restrictive single-similarity assumptions (i.e., individual or combinatorial similarity) and often neglect practical variability, leading to suboptimal performance. To address these limitations, we propose a sufficiency-principled transfer learning framework that systematically balances model averaging and model selection during domain aggregation with unknown informative knowledge. The framework employs a sufficiency principle for quantifying transferable knowledge to eliminate the challenges of spurious correlation and perturbated evaluation. The proposed model averaging algorithms accommodate both individual and combinatorial similarity regimes, and also has privacy-preserving mechanisms. Theoretically, we establish the asymptotic optimality, estimator convergence and asymptotic normality, for multiple source domain linear regression models with diverging parameters. Especially, compared with existing results, we provide enhanced rate of converge for parameter of interest. Empirical validation through extensive simulations and an analysis of Beijing housing rental data demonstrates the statistical superiority of our framework over conventional domain aggregation methods. The proposed methodology extends beyond regression models, offering a generalizable paradigm for transfer learning in statistical decision theory.

报告人简介：刘慧航博士目前是中国科学技术大学国际金融研究院博士后, 研究领域为模型平均与迁移学习，论文发表在Biometrics, JBES等统计学权威期刊。

报告时间：2025年4月21日（周一）上午10:00-11:00

报告地点：腾讯会议：492-339-201

联系人：胡晓楠