报告题目: Semiparametric Learning from Open-Set Label Shift Data

报告人：刘玉坤教授华东师范大学统计金沙娱乐城

报告时间：2026年11月11日10:00-11:00

报告地点：腾讯会议：162-921-861

校内联系人：杜明月[email protected]

报告摘要: We study the open-set label shift problem, where the test data may include a novel class absent from the training data. This setting is challenging because both the class proportions and the distribution of the novel class are not identifiable without extra assumptions. Existing approaches often rely on restrictive separability conditions, prior knowledge, or computationally infeasible procedures, and some may lack theoretical guarantees. We propose a semiparametric density ratio model framework that ensures identifiability while allowing overlap between novel and known classes. Within this framework, we develop maximum empirical likelihood estimators and confidence intervals for class proportions, establish their asymptotic validity, and design a stable Expectation-Maximization algorithm for computation. We also develop a goodness-of-fit test for model assessment and an approximately optimal classifier with theoretical guarantees. Simulations and a real-data application confirm that our methods improve both estimation accuracy and classification performance compared with existing approaches.

简介:刘玉坤，华东师范大学统计金沙娱乐城 教授、博士生导师，入选国家级高层次人才计划。本科与博士均毕业于南开大学统计系，此后一直在华东师范大学任教。研究方向涵盖分布偏移数据、因果推断、半参数统计与机器学习等。研究成果发表于JRSSB、AOS、JASA、Biometrika、Biometrics、JOE等国际期刊，以及NeurIPS、ICLR等计算机会议；现担任教育部“生态安全的智慧决策”学科突破先导项目PI，主持科技部国家重点研发计划课题与国家自然科学基金项目。