Publications associées à 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.

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Stochastic optimization

Graph Chatbot

Chattez avec Graph Search

Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

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

On the Double Descent of Random Features Models Trained with SGD

Momentum-Based Policy Gradient with Second-Order Information

Generalized parallel tempering on Bayesian inverse problems

Optimization methods for collaborative learning

Turning PathFinder Upside -Down: 1-4,xploring FPGA Switch -Blocks by Negotiating Switch Presence

Graph-Homomorphic Perturbations For Private Decentralized Learning

A General Framework for Optimal Data-Driven Optimization

Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

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

Optimization methods for collaborative learning

Turning PathFinder Upside -Down: 1-4,xploring FPGA Switch -Blocks by Negotiating Switch Presence

A General Framework for Optimal Data-Driven Optimization

On the Double Descent of Random Features Models Trained with SGD

Generalized parallel tempering on Bayesian inverse problems

Momentum-Based Policy Gradient with Second-Order Information

Graph-Homomorphic Perturbations For Private Decentralized Learning