報告題目：On Linear Convergence of ADMM for Decentralized Quantile Regression
報 告 人：練恒 教授
所在單位：香港城市大學
報告時間：2023年12月13日 星期三 10:30-11:30
報告地點：騰訊會議 ID: 174-637-676
校内聯系人：程建華 chengjh@jlu.edu.cn
報告摘要：The alternating direction method of multipliers (ADMM) is a natural method of choice for distributed parameter learning. For smooth and strongly convex consensus optimization problems, it has been shown that ADMM and some of its variants enjoy linear convergence in the distributed setting, much like in the traditional non-distributed setting. The optimization problem associated with parameter estimation in quantile regression is neither smooth nor strongly convex (although is convex) and thus it can only have sublinear convergence at best. Although this insinuates slow convergence, we show that, if the local sample size is sufficiently large compared to parameter dimension and network size, distributed estimation in quantile regression actually exhibits linear convergence up to the statistical precision.
報告人簡介：練恒，現任香港城市大學數學系教授，于2000年在中國科學技術大學獲得數學和計算機學士學位，2007年在美國布朗大學獲得計算機碩士，經濟學碩士和應用數學博士學位。先後在新加坡南洋理工大學，澳大利亞新南威爾士大學，和香港城市大學工作。在高水平國際期刊上發表學術論文30多篇，包括《Annals of Statistics》、《Journal of the Royal Statistical Society，Series B》、《Journal of the American Statistical Association》、《Journal of Machine Learning Research》、《IEEE Transactions on Pattern Analysis and Machine Intelligence》. 研究方向包括高維數據分析，函數數據分析，機器學習等。
