Publication

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

Related publications (66)

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.

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

Graph Chatbot

Chat with Graph Search

Minimax rate for optimal transport regression between distributions

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Improving sample and feature selection with principal covariates regression

Distributed Learning in Non-Convex Environments-Part I: Agreement at a Linear Rate

AKSEL: Fast Byzantine SGD

Sparsest piecewise-linear regression of one-dimensional data

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem