Publications associées à Universal and adaptive methods for robust stochastic optimization

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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

Universal and adaptive methods for robust stochastic optimization

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Enriching the Computational Toolbox for Organocatalysis

Bayesian Optimization for Chemical Reactions

The statistical complexity of early-stopped mirror descent

Memory of Motion for Initializing Optimization in Robotics

Predicting in Uncertain Environments: Methods for Robust Machine Learning

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

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Momentum-Based Policy Gradient with Second-Order Information

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

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

Bayesian Optimization for Chemical Reactions

The statistical complexity of early-stopped mirror descent

Memory of Motion for Initializing Optimization in Robotics

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Momentum-Based Policy Gradient with Second-Order Information

Enriching the Computational Toolbox for Organocatalysis

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization