报告时间：2026年6月10日（周四）15:00—17:00

报告地点：舜耕校区实验楼505

主办单位：鱼乐达人网页版

协办单位：科研处，现代统计交叉科学重点实验室，统计学博士后科研流动站，中加项目管理委员会

      一、工作论文报告

二、特邀专家报告

题目：Neural Wasserstein Two-Sample Tests

摘要：The two-sample homogeneity testing problem is fundamental in statistics and becomes particularly challenging in high dimensions, where classical tests can suffer substantial power loss. We develop a learning-assisted procedure based on the projection Wasserstein distance, which we call the neural Wasserstein test. The method is motivated by the observation that there often exists a low-dimensional projection under which the two high-dimensional distributions differ. In practice, we learn the projection directions via manifold optimization and a witness function using deep neural networks. To adapt to unknown projection dimensions and sparsity levels, we aggregate a collection of candidate statistics through a max-type construction, avoiding explicit tuning while potentially improving power. We establish the validity and consistency of the proposed test and prove a Berry-Esseen type bound for the Gaussian approximation. In particular, under the null hypothesis, the aggregated statistic converges to the absolute maximum of a standard Gaussian vector, yielding an asymptotically pivotal (distribution-free) calibration that bypasses resampling. Simulation studies and a real-data example demonstrate the strong finite-sample performance of the proposed method.

报告专家：林振华

专家介绍：林振华 (Lin Zhenhua) 现任新加坡国立大学 (NUS) 统计与数据科学系校长青年教授(Presidential Young Professor) 。同时兼任数据科学研究院 (IDS) 的附属研究人员。林博士于 2008 年获得复旦大学学士学位，随后分别于 2010和2013 年在西蒙菲莎大学获得硕士学位，并于 2017 年在多伦多大学取得统计学博士学位。加入新加坡国立大学之前，林博士在加州大学戴维斯分校从事博士后工作。他的研究方向主要集中在非欧几里得数据分析、函数型数据分析及高维统计学。林博士在统计学顶级期刊（如 The Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of the Royal Statistical Society (Series B), Biometrics, Journal of Computational and Graphical Statistics 等）发表了多篇论文，并曾任Bernouli期刊副主编，现任 Statistics 和 JASA/TAS 副主编。