報告人：張新雨研究員

報告時間：11月29日（周二）下午16:10—17:10

報告地點：騰訊會議151-250-933

報告題目：Optimal parameter-transfer learning by semiparametric model averaging

報告摘要：In this article, we focus on the prediction for a target model by transferring the information of source models. To be flexible, we use semiparametric additive frameworks for the target and source models. Inheriting the spirits of parameter-transfer learning, we assume that different models possibly share common knowledge across parametric components that is helpful to the target predictive task. Unlike existing parameter-transfer approaches, which need to construct auxiliary source models by parameter similarity with the target model and then adopt a regularization procedure, we propose a frequentist model averaging strategy with a J-fold cross-validation criterion so that auxiliary parameter information from different models can be adaptively utilized through the data-driven weight assignments. The asymptotic optimality and weight convergence of our proposed method are built under some regularity conditions. Extensive numerical results demonstrate the superiority of the proposed method over competitive methods.

報告人簡介：張新雨，中科院數學與系統科學研究院預測中心研究員，中科院管理、決策與信息系統重點實驗室副主任。主要從事計量經濟學和統計學的理論和應用研究工作，具體研究方向包括模型平均、機器學習和組合預測等，發表論文70余篇，其中多篇論文發表在統計學四大期刊和計量經濟學頂級期刊JoE。擔任SCI期刊《JSSC》領域主編、期刊《系統科學與數學》、《數理統計與管理》等的編委，是雙法學會數據科學分會副理事長、系統工程學會青工委副主任委員、國際統計學會當選會員，先后主持自科優秀和杰出青年基金項目，曾獲中國青年科技獎。