Publications associées à Last iterate convergence of SGD for Least-Squares in the Interpolation regime

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov, Scott William Pesme

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

The statistical complexity of early-stopped mirror descent

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

Accelerated SGD for Non-Strongly-Convex Least Squares

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Rapid Scalable Distributed Power Flow with Open-Source Implementation

ADAGRAD Avoids Saddle Points

Momentum-Based Policy Gradient with Second-Order Information

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

Rapid Scalable Distributed Power Flow with Open-Source Implementation

Predicting in Uncertain Environments: Methods for Robust Machine Learning

A Least-Squares Method for the Solution of the Non-smooth Prescribed Jacobian Equation

Momentum-Based Policy Gradient with Second-Order Information

The statistical complexity of early-stopped mirror descent

Accelerated SGD for Non-Strongly-Convex Least Squares

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

ADAGRAD Avoids Saddle Points