Publications associées à Universal and adaptive methods for robust stochastic optimization

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.

Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

Universal and adaptive methods for robust stochastic optimization

Graph Chatbot

Chattez avec Graph Search

Convergence of adaptive algorithms for constrained weakly convex optimization

AKSEL: Fast Byzantine SGD

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Shape optimization for a noise reduction problem by non-intrusive parametric reduced modeling

Second-Order Guarantees In Federated Learning

Convergence without Convexity: Sampling, Optimization, and Games

Extrapolation for Large-batch Training in Deep Learning

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Forward-Backward Splitting for Optimal Transport Based Problems

Adaptive Gradient Descent without Descent

Adaptive Gradient Descent without Descent

Convergence without Convexity: Sampling, Optimization, and Games

Convergence of adaptive algorithms for constrained weakly convex optimization

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Second-Order Guarantees In Federated Learning

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Forward-Backward Splitting for Optimal Transport Based Problems

AKSEL: Fast Byzantine SGD

Shape optimization for a noise reduction problem by non-intrusive parametric reduced modeling

Extrapolation for Large-batch Training in Deep Learning