QuACT 学术前沿讲座：New Coresets for Clustering: Beyond Euclidean Geometry
时 间：
2021年9月16日，星期四，上午10点
讲 者：
姜少峰，北京大学
摘 要：
Coreset is a powerful data-reduction technique that turns massive datasets into tiny ones, so that data analysis can be performed efficiently on top of it. Moreover, coresets have been widely used for designing efficient algorithms in “sublinear” models such as streaming and distributed computing.
The study of coresets for clustering problems, especially for k-clustering problems in Euclidean spaces, has been very fruitful. However, very few results are known when the space is beyond Euclidean or the objective is more general than k-clustering. In this talk, I will introduce a series of my recent works on coresets, including coresets for k-clustering in doubling spaces, in planar graphs, and generalized coresets for flexible and fair clustering. I will conclude the talk with future directions.
讲者简介：
Shaofeng Jiang is an assistant professor at Center on Frontiers of Computing Studies, Peking University. He obtained his Ph.D. from the University of Hong Kong, and before he joined PKU, he worked as a postdoctoral researcher at the Weizmann Institute of Science and then an assistant professor at Aalto University. His research interest is generally theoretical computer science, with a focus on algorithms for massive datasets, online algorithms and approximation algorithms.