Publications associées à Algorithme du gradient stochastique

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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

Algorithme du gradient stochastique

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A Continuized View on Nesterov Acceleration

Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Differentially Private Stochastic Coordinate Descent

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Convergence of adaptive algorithms for constrained weakly convex optimization

Robust Learning-Augmented Caching: An Experimental Study

Representing graphs through data with learning and optimal transport

Neural network surrogate of QuaLiKiz using JET experimental data to populate training space

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

A Continuized View on Nesterov Acceleration

Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes

Representing graphs through data with learning and optimal transport

Neural network surrogate of QuaLiKiz using JET experimental data to populate training space

Convergence of adaptive algorithms for constrained weakly convex optimization

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization

Differentially Private Stochastic Coordinate Descent

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Robust Learning-Augmented Caching: An Experimental Study