国产a片 学术报告[2026]089号

（高水平大学建设系列报告1348号）

报告题目：Shape Regression and Learning: Distributional Robustness and Gradient Flows

报告人：Yong Sheng Soh 助理教授 （新加坡国立大学）

报告时间：2026年9月22日16:30-17:30

报告地点：国产a片 粤海校区汇研楼601会议室

报告摘要：A broad range of learning problems can be abstractly viewed as performing regression of a suitably parameterized function to data.  When these functions are viewed via its level sets, these learning tasks can be equivalently viewed as instances of shape regression tasks.  In this talk, we will examine learning tasks via the perspective of shape regression tasks.  This allows us to use geometric ideas to understand phenomena that are of interest from a practitioner's perspective.  We characterize the optimal level sets given the underlying data distribution in settings where the level sets are required to be convex bodies as well as star bodies.  We discuss how incorporating distributional robustness (in our learned model) affects the optimal level sets.  Finally, we discuss gradient flows over the space of shapes we learn as a means of understanding the behavior of learning algorithms.

报告人简介：Yong Sheng Soh is an Assistant Professor at the Department of Mathematics, National University of Singapore.  He is broadly interested in mathematical optimization, with a focus on problems arising from data analysis.  He obtained his PhD in Applied and Computational Mathematics at the California Institute of Technology.  Prior to joining NUS, he was a Research Scientist at the Institute for High Performance Computing at the Agency for Science, Technology and Research (A*STAR), Singapore.

邀请人：胡潇尹

国产a片

2026年9月11日