报告题目：Maximum Correntropy Criterion with a Distributed Method
报告时间：2024年7月1日 9:30-10:30
报告地点：综合楼644
报告人：汪宝彬
报告人简介：
汪宝彬，中南民族大学数学与太阳成集团tyc7111cc副教授、硕士生导师，主要从事数理统计及其交叉应用研究。目前，在 Neurocomputing和Acta Mathematica Scientia等国内外杂志发表学术论文20余篇。
报告摘要：The Maximum Correntropy Criterion (MCC) has recently triggered enormous research activities in engineering and machine learning communities since it is robust when faced with heavy-tailed noise or outliers in practice. This talk is interested in distributed MCC algorithms, based on a divide-and-conquer strategy, which can deal with big data efficiently. By establishing minmax optimal error bounds, our results show that the averaging output function of this distributed algorithm can achieve comparable convergence rates to the algorithm processing the total data in one single machine.
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