报告时间：2024年3月27日(周三)10:00-11:00
报告地点：中国人民大学明德主楼1016
报告主题：Transfer Learning by Model Averaging
报告摘要：
In this article, we focus on prediction of 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 spirit of parameter-transfer learning, we assume that different models possibly share common knowledge across parametric components that is helpful for 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 transferred through 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，中科院数学与系统科学研究院研究员。主要从事统计学和计量经济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、经济预测、医学统计等，担任国内SCI期刊《Journal of Systems Science & Complexity (JSSC)》领域主编和其他5个国内外重要期刊的编委。先后主持优青和杰青项目。
