2018-12-17 | Guanghui (George) Lan：Stochastic optimization for learning over networks
Stochastic optimization methods, e.g., stochastic gradient descent (SGD), have recently found wide applications in large-scale data analysis, especially in machine learning. These methods are very attractive to process online streaming data as they scan through the dataset only once but still generate solutions with acceptable accuracy. However, it is known that classical SGDs are ineffective in processing streaming data distributed over multi-agent network systems (e.g., sensor and social networks), mainly due to the high communication costs incurred by these methods.
In this talk, we present a few new classes of SGDs which can significantly reduce the aforementioned communication costs for distributed or decentralized machine learning. We show that these methods can significantly save inter-node communications when performing SGD iterations. Meanwhile, the total number of stochastic (sub)gradient computations required by these methods are comparable to those optimal ones achieved by classical centralized SGD type methods.
This talk is based on the following two papers.
1. G. Lan and Y. Zhou, Random gradient extrapolation for distributed and stochastic optimization, SIAM Journal on Optimization, 28(4), 2753-2782, 2018.
2. G. Lan, S. Lee and Y. Zhou, Communication-efficient Algorithms for Decentralized and Stochastic Optimization, Mathematical Programming, to appear, 2018.
Guanghui (George) Lan serves as an associate professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology since January 2016. Before that he had been a faculty member in the Department of Industrial and Systems Engineering at the University of Florida from 2009 to 2015, after receiving his Ph.D. degree from Georgia Institute of Technology in August, 2009. His main research interests lie in optimization and machine learning/intelligence. Dr. Lan serves as the associate editor for Mathematical Programming, SIAM Journal on Optimization, and Computational Optimization and Applications.