报告题目 | Optimal Parameter-Transfer Learning by Model Averaging under Semiparametric Models | ||
报告人（单位） | 张新雨（中科院） | ||
邀请人（单位） | 李泳臻（东南大学） | 点评人（单位） | 倪文君（东南大学） |
会议时间 | 2022年6月6日 15:30-16:30 | ||
会议地点 | 腾讯会议 518 781 694 | ||
报告人简介 | |||
张新雨，中科院数学与系统科学研究院预测中心研究员。主要从事计量经济学和统计学的理论和应用研究工作。 | |||
报告内容提要 | |||
Transfer learning has attracted more and more attention in the field of artificial intelligence, of which the aim is to improve one target task of interest by utilizing tasks from several related source domains. In this article, we focus on the prediction for semiparametric additive linear model under the setting of transfer learning. Inheriting the spirits of parameter-transfer learning, we assume existing common knowledge shared in parametric components among different models in our framework that is possibly helpful for the target predictive task. We adopt a frequentist model averaging strategy to utilize parameter information. The theoretical properties have also been established, including the asymptotic optimality based on out-of-sample prediction risk and the property of weight convergence under some regularity conditions. Extensive numerical results demonstrate the superiority of the proposed method under various simulation designs comparing with competitive methods. (Jointly with Xiaonan Hu) | |||
