Publications associées à Stochastic Zeroth-Order Optimisation Algorithms with Variance Reduction

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 Zeroth-Order Optimisation Algorithms with Variance Reduction

Graph Chatbot

Chattez avec Graph Search

Understanding generalization and robustness in modern deep learning

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Universal and adaptive methods for robust stochastic optimization

Deep Learning Generalization with Limited and Noisy Labels

Stochastic distributed learning with gradient quantization and double-variance reduction

Momentum-Based Policy Gradient with Second-Order Information

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

Memory of Motion for Initializing Optimization in Robotics

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Masked Training of Neural Networks with Partial Gradients

Masked Training of Neural Networks with Partial Gradients

Memory of Motion for Initializing Optimization in Robotics

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

Stochastic distributed learning with gradient quantization and double-variance reduction

Deep Learning Generalization with Limited and Noisy Labels

Universal and adaptive methods for robust stochastic optimization

Understanding generalization and robustness in modern deep learning

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Momentum-Based Policy Gradient with Second-Order Information

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions