Efficient Optimal Transport Algorithms and Their Application in Echocardiogram Studies
报告人： Cheng Meng（Renmin University of China）
时间：2025-12-04 10:10-12:00
地点：Room 217, Guanghua Building 2
Abstract:
Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling. In this study, we propose a novel metric, called Hilbert curve projection (HCP) distance, to measure the distance between two probability distributions with low complexity. In particular, we first project two high-dimensional probability distributions using Hilbert curve to obtain a coupling between them, and then calculate the transport distance between these two distributions in the original space, according to the coupling. We show that HCP distance is a proper metric and is well-defined for probability measures with bounded supports. Experiments on real-world echocardiogram studies show that our HCP distance works as an effective surrogate of the Wasserstein distance.
About the Speaker:
孟澄，统计与大数据研究院助理教授、博士生导师。中国大百科全书（第三卷）统计学卷-数据科学分卷副主编。主要研究方向为：大数据压缩、最优输运方法、统计及工业交叉科学等，在Biometrika, IEEE TPAMI, JMLR, NeurIPS等期刊会议上发表论文二十余篇。孟澄带领团队获得华为“难题揭榜”价值火花奖三枚、鼓励火花奖三枚，荣获中国人民大学官媒及华为官媒报道。主页：https://cheng-bdal.github.io/Cheng-BDAL-CN.github.io/
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