88 citations · 172 across the 10 of their papers we have counts for
5 papers · 1 filter
CADA: Communication-Adaptive Distributed Adam
Tianyi Chen, Ziye Guo, Yuejiao Sun +1
Stochastic gradient descent (SGD) has taken the stage as the primary workhorse for large-scale machine learning. It is often used with its adaptive variants such as AdaGrad, Adam,…
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…
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization
Tianyi Chen, Yuejiao Sun, Wotao Yin
Stochastic compositional optimization generalizes classic (non-compositional) stochastic optimization to the minimization of compositions of functions. Each composition may introdu…
VAFL: a Method of Vertical Asynchronous Federated Learning
Tianyi Chen, Xiao Jin, Yuejiao Sun +1
Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from diffe…
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…