报 告 人：刘慧航 博士

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

报告时间：2025年03月05日（周三）下午3：00

报告地点：静远楼1506学术报告厅

主办单位：数学与统计学院、数学研究院、科学技术研究院

报告人简介：

       刘慧航博士是中国科学技术大学国际金融研究院博士后. 2023年毕业于中国科学技术大学. 研究方向为模型平均与迁移学习. 主要的工作内容包括针对有向和无向高斯图模型, 迁移学习, 非对称损失的回归模型进行参数的模型平均.论文发表于 Biometrics 和 Journal of Business & Economic Statistics 等期刊.

报告摘要：

       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.