讲座时间：2026年10月24日（周六）上午9:30

讲座地点：图书馆一楼报告厅

讲座题目：Distribution-free and model-agnostic changepoint detection with finite-sample guarantees

摘要：

In this talk, I will introduce ART, a distribution-free and model-agnostic framework for changepoint analysis with finite-sample guarantees. ART transforms independent observations into real-valued scores via a symmetric function; under the null hypothesis of no changepoint these scores are exchangeable. Ranking and aggregating the scores yields test statistics whose null distribution is known exactly from the permutation law of ranks, enabling exact finite-sample Type I error control without repeated refitting under permutations. ART extends naturally to a multi-scale setting: by locally ranking scores over a family of intervals and aggregating them, it supports multiple changepoint testing, localization with inference, and post-detection inference, while retaining distribution-free calibration. The approach is model-agnostic: it imposes minimal structural or distributional assumptions and accommodates diverse score constructions, including features learned by statistical or machine-learning models.

专家简介：

邹长亮教授，南开大学统计与数据科学学院执行院长、教授、博士生导师，国家杰出青年科学基金、优秀青年科学基金获得者，教育部首批青年长江学者、中组部首批青年拔尖人才。2008年于南开大学获得理学博士学位，2013年破格晋升教授。长期从事统计学理论及其与大数据、人工智能等领域的交叉研究，主要研究方向包括高维数据统计推断、机器学习、统计过程控制、变点与异常点检测等。在Annals of Statistics、Journal of the American Statistical Association、Biometrika、Journal of Machine Learning Research等国际重要学术期刊发表多篇论文，入选爱思唯尔“中国高被引学者”。先后主持国家自然科学基金杰出青年科学基金、优秀青年科学基金、重点项目及国家重点研发计划课题等多项科研项目。曾获全国优秀博士学位论文、中国数学会钟家庆数学奖、霍英东教育基金会高等院校青年教师一等奖、天津市自然科学一等奖等荣誉和奖励。