報告題目： Learning Parameter Heterogeneity over Networks: A Distributed Tree-Based Fused-Lasso Approach
報 告 人：Zhengyuan Zhu 教授 美國愛荷華州立大學
報告時間：2019年7月11日上午9:40-10:20
報告地點：數學樓一樓第一報告廳
報告摘要:
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.
報告人簡介：
Zhengyuan Zhu是美國愛荷華州立大學LAS院長統計學教授，也是調查統計與方法學中心主任。他于2002年獲得美國芝加哥大學統計學博士學位，并于2009年加入沒過過愛荷華州立大學，之前他擔任美國北卡羅來納大學教堂山分校統計學助理教授。他擁有空間統計，調查統計，空間抽樣設計和時間序列分析方面的專業知識，并對環境統計，遙感，自然資源調查和農業統計中的應用感興趣。他是許多國家大型縱向調查的PI和co-PI，包括美國國家資源調查，美國BLM管理土地調查和保護影響評估項目調查。
