538 citations · 1.1k across the 31 of their papers we have counts for
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On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging
Chris Junchi Li, Yaodong Yu, Nicolas Loizou +4
We study the stochastic bilinear minimax optimization problem, presenting an analysis of the same-sample Stochastic ExtraGradient (SEG) method with constant step size, and presenti…
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods
Robert M. Gower, Nicolas Le Roux, Francis Bach
Our goal is to improve variance reducing stochastic methods through better control variates. We first propose a modification of SVRG which uses the Hessian to track gradients over…
Distributed SAGA: Maintaining linear convergence rate with limited communication
Clément Calauzènes, Nicolas Le Roux
In recent years, variance-reducing stochastic methods have shown great practical performance, exhibiting linear convergence rate when other stochastic methods offered a sub-linear…
Minimizing Finite Sums with the Stochastic Average Gradient
Mark Schmidt, Nicolas Le Roux, Francis Bach
We propose the stochastic average gradient (SAG) method for optimizing the sum of a finite number of smooth convex functions. Like stochastic gradient (SG) methods, the SAG method'…
Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition
Mark Schmidt, Nicolas Le Roux
We consider optimizing a function smooth convex function that is the average of a set of differentiable functions , under the assumption considered by Solodov [1998] and T…
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets
Nicolas Le Roux, Mark Schmidt, Francis Bach
We propose a new stochastic gradient method for optimizing the sum of a finite set of smooth functions, where the sum is strongly convex. While standard stochastic gradient methods…