报告题目：General risk minimization under random censorship with empirical likelihood weighting

报告时间：2026年9月13日上午11:00

报告地点：北湖东校区数统新楼201室

主办单位：免费A片

报告人：刘玉坤

报告人简介：刘玉坤，华东师范大学统计学院教授、博士生导师。本科和博士毕业于南开大学统计系，之后一直在华东师范大学任教。研究兴趣包括分布偏移数据、因果推断、共形推断、半参数统计和机器学习等。成果曾发表在JRSSB、AOS、JASA、Biometrika、Biometrics、JOE等期刊和计算机顶会NeurIPS和ICLR等; 担任“生态安全智慧决策”教育部学科突破计划先导项目PI，主持科技部国家重点研发计划课题和国家自然科学基金项目。

摘要：Empirical Risk Minimization (ERM) serves as a foundational framework in statistical learning and machine learning, yet conventional ERM methods struggle with censored data, which are common in various fields. The inherent censoring mechanism generates incomplete observations that disrupt direct loss function evaluation. While inverse probability of censoring weighting (IPCW) methods--based on the conditional survival function $S_C(t|\bx)$--partially mitigate this issue, they exhibit severe instability as $S_C(t|\bx)$  approaches zero. We propose an empirical likelihood weighting-based ERM (ELW-ERM), which overcomes the instability issues associated with IPCW and accommodates general loss functions, including squared loss, logistic loss and quantile loss. We establish non-asymptotic  or asymptotic error bounds on the excess risk of our ELW-ERM predictor under general conditions and three specific model assumptions. Our ELW-ERM is shown to achieve the same learning rates as classical ERMs in absence of censoring under the Cox model, and as the IPCW-based ERM under a fully nonparametric model. Under a general Cox model, with the help of deep neural networks, it is able to mitigate the curse-of-dimensionality issue when $S_{C}(t|\bx)$  has a low-dimensional structure. Numerical experiments and an empirical analysis demonstrate the effectiveness and superiority of our proposed ELW-ERM method.