Decentralized Stochastic Gradient Descent (SGD) is an emerging neural ne...
We propose a generalization of the standard matched pairs design in whic...
Decentralized algorithm is a form of computation that achieves a global ...
Decentralized SGD is an emerging training method for deep learning known...
Various bias-correction methods such as EXTRA, DIGing, and exact diffusi...
This work derives and analyzes an online learning strategy for tracking ...
This work studies the problem of learning under both large data and larg...
In empirical risk optimization, it has been observed that stochastic gra...
In diffusion social learning over weakly-connected graphs, it has been s...
This work develops a fully decentralized variance-reduced learning algor...
Several useful variance-reduced stochastic gradient algorithms, such as ...
The analysis in Part I revealed interesting properties for subgradient
l...
The article examines in some detail the convergence rate and
mean-square...
The stochastic dual coordinate-ascent (S-DCA) technique is a useful
alte...
In this work and the supporting Part II, we examine the performance of
s...