报告人简介
李婷，上海财经大学统计与管理学院副教授，本科毕业于华东师范大学统计系，博士毕业于复旦大学管理学院统计学系。曾赴美国德州大学md安德森癌症中心、香港中文大学统计系，北卡罗来纳大学教堂山分校访问学习。研究方向包括函数型数据、医学基因影像数据分析、分位数回归和因果推断。在journal of the american statistical association， annals of applied statistics, statistica sinica，biometrics等统计学期刊发表以及人工智能顶会neurips，icml上发表过多篇论文。
内容简介
conformal prediction, a powerful framework that constructs a prediction band for the response variable using any regression function estimators, often faces the challenge of producing overly broad bands with limited target data. in this paper, we study the transfer learning problem in conformal prediction, aiming to improve the precision of the prediction interval of the target data with insufficient data by leveraging information from related auxiliary source datasets. allowing for non-exchangeability between source datasets and the target dataset, two transfer conformal prediction algorithms have been proposed for scenarios with and without knowledge about the informative source data. utilizing the conditional kullback-leibler divergence, the proposed algorithm effectively identifies relevant source data for transfer. we present a comprehensive analysis of the non-asymptotic theoretical properties of the proposed algorithms, including lower and upper bounds as well as the bounds on the width of the prediction bands, demonstrating the potential for efficiency gains through narrower intervals while preserving coverage accuracy. empirical evidence, drawn from extensive simulations and real data analysis, further validates the effectiveness of our algorithms in improving prediction interval quality by leveraging source data, and achieving narrower intervals while maintaining desired coverage levels.
