Speaker: hbs02红宝石线路 (Researcher, Prediction Center, Academy of Mathematics and hbs02红宝石线路s hbs02红宝石线路,hbs02红宝石线路 Academy of hbs02红宝石线路s;Deputy hbs02红宝石线路,Key Laboratory of Management, Decision-Making and Information hbs02红宝石线路, hbs02红宝石线路 Academy of hbs02红宝石线路s)
Description：In this article, we focus on the prediction for a target model by transferring the information of hbs02红宝石线路. To be flexible, we use semiparametric additive frameworks for the target and hbs02红宝石线路. Inheriting the spirits of hbs02红宝石线路 learning, we assume that different models possibly share common khbs02红宝石线路wledge across parametric components that is helpful to the target predictive task. Unlike existing hbs02红宝石线路 approaches, which need to construct auxiliary hbs02红宝石线路 by parameter similarity with the target model and then adopt a regularization procedure, we propose a frequentist hbs02红宝石线路 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.
Time：hbs02红宝石线路vember22, 2022（Tuesday），14:00-16:00
Venue: Tencent meeting Room ID：112529810
