题目：Semi-supervised learning using copula-based regression and model averaging

报告人：高子文 (清华大学)    

摘要：The available data in semi-supervised learning usually consists of relatively small sized labeled data and much larger sized unlabeled data. How to effectively exploit unlabeled data is the key issue. In this paper, we write the regression function in the form of a copula and marginal distributions, and the unlabeled data can be exploited to improve the estimation of the marginal distributions. The predictions based on different copulas are weighted, where the weights are obtained by minimizing an asymptotic unbiased estimator of the prediction risk. Error-ambiguity decomposition of the prediction risk is performed such that unlabeled data can be exploited to improve the prediction risk estimation. We demonstrate the asymptotic normality of copula parameters and regression function estimators of the candidate models under the semi-supervised framework, as well as the asymptotic optimality and weight consistency of the model averaging estimator. Our model averaging estimator achieves faster convergence rates of asymptotic optimality and weight consistency than the supervised counterpart. Extensive simulation experiments and the California housing dataset demonstrate the effectiveness of the proposed method.    

报告人简介：高子文是清华大学博士后，2024年6月毕业于中国科学院数学与系统科学研究院，研究方向是模型平均、机器学习等。已发表多篇文章并主持一项博后面上项目。    

报告时间：2025年4月23日（周三）下午14:30-15:30    

报告地点：教四101

联系人：胡晓楠