Publications associées à Reducing Noise in GAN Training with Variance Reduced Extragradient

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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

Reducing Noise in GAN Training with Variance Reduced Extragradient

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Universal and adaptive methods for robust stochastic optimization

Stochastic adaptive resampling for the estimation of discrete choice models

WINC: A Wireless IoT Network for Multi-Noise Source Cancellation

A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers

Plasma-based Electroacoustic Actuator for Broadband Sound Absorption

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

Differentially Private Stochastic Coordinate Descent

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

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

Extrapolation for Large-batch Training in Deep Learning

A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers

Plasma-based Electroacoustic Actuator for Broadband Sound Absorption

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Universal and adaptive methods for robust stochastic optimization

Stochastic adaptive resampling for the estimation of discrete choice models

WINC: A Wireless IoT Network for Multi-Noise Source Cancellation

Differentially Private Stochastic Coordinate Descent

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

Extrapolation for Large-batch Training in Deep Learning

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