报告题目： Learning Parameter Heterogeneity over Networks: A Distributed Tree-Based Fused-Lasso Approach
报 告 人：Zhengyuan Zhu 教授 美国爱荷华州立大学
报告时间：2019年7月11日上午9:40-10:20
报告地点：数学楼一楼第一报告厅
报告摘要:
We propose an adaptive fused-lasso based coefficient subgroup approach in the decentralized network system. The major goal is to improve the model estimation efficiency by aggregating the neighbors & information as well as identify the subgroup membership for each node in the network. In particular, a tree-based $l_1$ penalty is proposed to save the computation and communication cost. We also design a decentralized generalized alternating direction method of multiplier algorithm for solving the objective function in parallel. The theoretical properties are derived to guarantee both the model consistency and the algorithm convergence. Thorough numerical experiments are also conducted to back up our theory, which also show that our approach outperforms in the aspects of the estimation accuracy, computation speed and communication cost.
报告人简介：
Zhengyuan Zhu是美国爱荷华州立大学LAS经理统计学教授，也是调查统计与方法学中心主任。他于2002年获得美国芝加哥大学统计学博士学位，并于2009年加入没过过爱荷华州立大学，之前他担任美国北卡罗来纳大学教堂山分校统计学助理教授。他拥有空间统计，调查统计，空间抽样设计和时间序列分析方面的专业知识，并对环境统计，遥感，自然资源调查和农业统计中的应用感兴趣。他是许多国家大型纵向调查的PI和co-PI，包括美国国家资源调查，美国BLM管理土地调查和保护影响评估项目调查。
