66 citations · 87 across the 7 of their papers we have counts for
9 papers
Accelerated SGD for Non-Strongly-Convex Least Squares
Aditya Varre, Nicolas Flammarion
We consider stochastic approximation for the least squares regression problem in the non-strongly convex setting. We present the first practical algorithm that achieves the optimal…
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip
Mathieu Even, Raphaël Berthier, Francis Bach +5
We introduce the continuized Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter. The two variables continuou…
A Continuized View on Nesterov Acceleration
Raphaël Berthier, Francis Bach, Nicolas Flammarion +2
We introduce the "continuized" Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter. The two variables continu…
Last iterate convergence of SGD for Least-Squares in the Interpolation regime
Aditya Varre, Loucas Pillaud-Vivien, Nicolas Flammarion
Motivated by the recent successes of neural networks that have the ability to fit the data perfectly and generalize well, we study the noiseless model in the fundamental least-squa…
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent
Scott Pesme, Aymeric Dieuleveut, Nicolas Flammarion
Constant step-size Stochastic Gradient Descent exhibits two phases: a transient phase during which iterates make fast progress towards the optimum, followed by a stationary phase d…
Online Robust Regression via SGD on the l1 loss
Scott Pesme, Nicolas Flammarion
We consider the robust linear regression problem in the online setting where we have access to the data in a streaming manner, one data point after the other. More specifically, fo…