12 citations · 17 across the 2 of their papers we have counts for
6 papers
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
Bicheng Ying, Kun Yuan, Hanbin Hu +2
Decentralized algorithm is a form of computation that achieves a global goal through local dynamics that relies on low-cost communication between directly-connected agents. On larg…
Exponential Graph is Provably Efficient for Decentralized Deep Training
Bicheng Ying, Kun Yuan, Yiming Chen +3
Decentralized SGD is an emerging training method for deep learning known for its much less (thus faster) communication per iteration, which relaxes the averaging step in parallel S…
Dynamic Average Diffusion with randomized Coordinate Updates
Bicheng Ying, Kun Yuan, Ali H. Sayed
This work derives and analyzes an online learning strategy for tracking the average of time-varying distributed signals by relying on randomized coordinate-descent updates. During…
Supervised Learning Under Distributed Features
Bicheng Ying, Kun Yuan, Ali H. Sayed
This work studies the problem of learning under both large datasets and large-dimensional feature space scenarios. The feature information is assumed to be spread across agents in…
Stochastic Learning under Random Reshuffling with Constant Step-sizes
Bicheng Ying, Kun Yuan, Stefan Vlaski +1
In empirical risk optimization, it has been observed that stochastic gradient implementations that rely on random reshuffling of the data achieve better performance than implementa…
Online Dual Coordinate Ascent Learning
Bicheng Ying, Kun Yuan, Ali H. Sayed
The stochastic dual coordinate-ascent (S-DCA) technique is a useful alternative to the traditional stochastic gradient-descent algorithm for solving large-scale optimization proble…