Publications associées à Stochastic Three-Composite Convex Minimization with a Linear Operator

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Stochastic Three-Composite Convex Minimization with a Linear Operator

Graph Chatbot

Chattez avec Graph Search

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Stochastic homogenization of degenerate integral functionals with linear growth

Stochastic distributed learning with gradient quantization and double-variance reduction

Proximal Point Imitation Learning

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

A first-order primal-dual method with adaptivity to local smoothness

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Fast Projection Onto Convex Smooth Constraints

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A first-order primal-dual method with adaptivity to local smoothness

Stochastic homogenization of degenerate integral functionals with linear growth

Proximal Point Imitation Learning

Stochastic distributed learning with gradient quantization and double-variance reduction

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems

Fast Projection Onto Convex Smooth Constraints