14 citations · 23 across the 4 of their papers we have counts for
6 papers · 1 filter
Decentralized Learning with Lazy and Approximate Dual Gradients
Yanli Liu, Yuejiao Sun, Wotao Yin
This paper develops algorithms for decentralized machine learning over a network, where data are distributed, computation is localized, and communication is restricted between neig…
LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning
Tianyi Chen, Yuejiao Sun, Wotao Yin
This paper targets solving distributed machine learning problems such as federated learning in a communication-efficient fashion. A class of new stochastic gradient descent (SGD) a…
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme
Tao Sun, Yuejiao Sun, Dongsheng Li +1
The incremental aggregated gradient algorithm is popular in network optimization and machine learning research. However, the current convergence results require the objective funct…
Decentralized Markov Chain Gradient Descent
Tao Sun, Dongsheng Li
Decentralized stochastic gradient method emerges as a promising solution for solving large-scale machine learning problems. This paper studies the decentralized Markov chain gradie…
Markov Chain Block Coordinate Descent
Tao Sun, Yuejiao Sun, Yangyang Xu +1
The method of block coordinate gradient descent (BCD) has been a powerful method for large-scale optimization. This paper considers the BCD method that successively updates a serie…
On Markov Chain Gradient Descent
Tao Sun, Yuejiao Sun, Wotao Yin
Stochastic gradient methods are the workhorse (algorithms) of large-scale optimization problems in machine learning, signal processing, and other computational sciences and enginee…