報 告 人：劉慧航 博士

報告題目：Trans-MA: Sufficiency-principled Transfer Learning via Model Averaging

報告時間：2025年03月05日（周三）下午3：00

報告地點：靜遠樓1506學術報告廳

主辦單位：數學與統計學院、數學研究院、科學技術研究院

報告人簡介：

       劉慧航博士是中國科學技術大學國際金融研究院博士后. 2023年畢業于中國科學技術大學. 研究方向為模型平均與遷移學習. 主要的工作內容包括針對有向和無向高斯圖模型, 遷移學習, 非對稱損失的回歸模型進行參數的模型平均.論文發表于 Biometrics 和 Journal of Business & Economic Statistics 等期刊.

報告摘要：

       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.