91自拍 学术报告[2025]149号

(高水平大学建设系列报告1250号)

报告题目：Distribution-Free Prediction Sets for Regression under Target Shift

报告人：唐炎林 教授（华东师范大学）

报告时间：2025年12月7日上午10:20-11:20

报告地点：91自拍 粤海校区汇星楼514

报告内容：In real-world applications, the limited availability of labeled outcomes presents significant challenges for statistical inference, due to high collection costs, technical barriers, and other constraints. In this work, we construct efficient conformal prediction sets for new target outcomes by leveraging a source distribution—distinct from the target but related through a distributional shift assumption—that provides abundant labeled data.

When the target data are fully unlabeled, our predictions rely solely on the source distribution; when partial labels are available, they are integrated with the source data to improve efficiency. To address the challenges of data non-exchangeability and distribution non-identifiability, we identify the likelihood ratio by matching the covariate distributions of the source and target domains within a finite B-spline space. To accommodate complex error structures such as asymmetry and multimodality, our method constructs the highest predictive density sets using a novel weight-adjusted conditional density estimator. This estimator models the source conditional density along a quantile process and transforms it—via appropriate weighting adjustments—to approximate the target conditional density. We establish the theoretical properties of the proposed method and evaluate its finite-sample performance through simulation studies and a real-data application to the MIMIC-III clinical database.

报告人简历：唐炎林，华东师范大学统计学院教授，博士生导师，统计学系主任；入选国家青年高层次人才计划、上海市浦江人才计划。主要研究方向为分位数回归、共形预测、高维异质性数据统计推断，主持多项国家自然科学基金、上海市自然科学基金，担任SCI期刊Statistica Sinica、Journal of the Korean Statistical Society的编委。在Biometrika、JRSSB、PNAS、Biometrics等发表论文40余篇。

欢迎师生参加！

邀请人：周彦

91自拍

2025年12月6日