Publications associées à Almost surely constrained convex 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

Almost surely constrained convex optimization

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On the Generalization of Stochastic Gradient Descent with Momentum

Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

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

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Second-Order Guarantees In Federated Learning

Extrapolation for Large-batch Training in Deep Learning

On the Generalization of Stochastic Gradient Descent with Momentum

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Universal and adaptive methods for robust stochastic optimization

Last iterate convergence of SGD for Least-Squares in the Interpolation regime

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

Second-Order Guarantees In Federated Learning

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

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Extrapolation for Large-batch Training in Deep Learning