电竞菠菜外围app

科学研究
报告题目:

Communication Efficiency on Penalized Regression for Distributed Data

报告人:

李楚进 副教授(华中科技大学电竞菠菜外围app )

报告时间:

报告地点:

腾讯会议 ID: 624 911 967 会议密码:564977

报告摘要:

In this talk, we present a novel and communication-efficient approach for sparse and high-dimensional distributed data with the penalized expectile or quantile regression, which is realized by considering the proximal alternating direction method of multipliers (ADMM) algorithm on the master machine and the subgradient about local data on other worker machines. As for the communication efficiency, the proposed approach does not sacrifice any statistical accuracy and provably improves the estimation error obtained by centralized method, provided the penalty levels are chosen properly.