Publication

A note on a Lyapunov argument for stochastic gradient methods in the presence of noise

Related publications (32)

About
Privacy
Disclaimer

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

A note on a Lyapunov argument for stochastic gradient methods in the presence of noise

Graph Chatbot

Chat with Graph Search

Adaptive Gradient Descent without Descent

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

Reducing Noise in GAN Training with Variance Reduced Extragradient

Local SGD Converges Fast and Communicates Little

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Adaptive data augmentation for image classification

Stochastic gradient descent with finite samples sizes

Stochastic Spectral Descent for Discrete Graphical Models

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

Adaptive data augmentation for image classification

Adaptive Gradient Descent without Descent

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Reducing Noise in GAN Training with Variance Reduced Extragradient

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Local SGD Converges Fast and Communicates Little

Stochastic gradient descent with finite samples sizes

Stochastic Spectral Descent for Discrete Graphical Models