学术报告

Optimal parameter-transfer learning by semiparametric model averaging-张新雨研究员 (中国科学院数学与系统科学研究院)

报告题目:Optimal parameter-transfer learning by semiparametric model averaging

报告人:张新雨 (中国科学院数学与系统科学研究院)

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.

报告人简介:张新雨,2005年毕业于中央财经大学,目前是中科院数学与系统科学研究院预测中心研究员,中科院管理、决策与信息系统重点实验室副主任。主要从事计量经济学和统计学的理论和应用研究工作,具体研究方向包括模型平均、机器学习和组合预测等,发表论文70余篇,其中多篇论文发表在统计学四大期刊和计量经济学顶级期刊JoE。担任SCI期刊《JSSC》领域主编、期刊《系统科学与数学》、《数理统计与管理》等的编委,是双法学会数据科学分会副理事长、系统工程学会青工委副主任委员、国际统计学会当选会员,先后主持自科优秀和杰出青年基金项目,曾获中国青年科技奖。

报告时间:2022年11月25日(周五)下午15:00

线上参加:#腾讯会议 523-3915-6716

2022年11月25日-张新雨研究员-周洁.jpg


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