Publications associées à Algorithme du gradient stochastique

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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

Algorithme du gradient stochastique

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Masked Training of Neural Networks with Partial Gradients

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

Towards Understanding Sharpness-Aware Minimization

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

System and method for privacy-preserving distributed training of neural network models on distributed datasets

Metrizing Fairness

ADAGRAD Avoids Saddle Points

Momentum-Based Policy Gradient with Second-Order Information

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

Towards Understanding Sharpness-Aware Minimization

Metrizing Fairness

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

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

System and method for privacy-preserving distributed training of neural network models on distributed datasets

Masked Training of Neural Networks with Partial Gradients

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

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

ADAGRAD Avoids Saddle Points

Momentum-Based Policy Gradient with Second-Order Information