题目: Learning theory for distributed learning

报告人：林绍波，贝斯特bst3344游戏教授

时间：2019年11月4日（周一)下午15：00-16：30

地点：管院313会议室

欢迎广大师生前来参加！

报告内容: 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.

报告人简介：林绍波，贝斯特bst3344游戏教授，青年拔尖人才。博士毕业于西安交通大学数学与统计学院。曾先后于香港城市大学数学系、香港理工大学应用数学系、香港城市大学数据科学学院担任博士后、副研究员、研究员。研究方向为大数据分析、分布式计算及深度学习。主持或以核心成员参与国家自然科学基金9项。 在Journal of Machine Learning Research, Applied and Computational Harmonics Analysis, IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks and Learning Systems等著名期刊发表论文50余篇。