智能決策與機器學習研究中心系列講座（一）Learning theory for distributed learning 2019-10-30 題目: Learning theory for distributed learning報告人：林紹波，bwin必贏唯一官網教授時間：2019年11月4日（周一)下午15：00-16：30地點：管院313會議室歡迎廣大師生前來參加！報告內容: We analyze the performance of distributed learning based on a divide-and-conquer strategy. This scheme applies a specified learning algorithm to data subsets that are distributively stored on multiple servers to produce individual output functions, and then takes a weighted average of the individual output functions as a final estimator. We study that under which condition distributed learning is feasible and conduct several strategies such as semi-supervised distributed learning and communications to improve its performance further. 報告人簡介：林紹波，bwin必贏唯一官網教授，青年拔尖人才。博士畢業(yè)于bwin必贏唯一官網數學與統(tǒng)計學院。曾先后于香港城市大學數學系、香港理工大學應用數學系、香港城市大學數據科學學院擔任博士后、副研究員、研究員。研究方向為大數據分析、分布式計算及深度學習。主持或以核心成員參與國家自然科學基金9項。 在Journal of Machine Learning Research, Applied and Computational Harmonics Analysis, IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks and Learning Systems等著名期刊發(fā)表論文50余篇。