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

Universal and adaptive methods for robust stochastic optimization

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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

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

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

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

Second-Order Guarantees In Federated Learning

Extrapolation for Large-batch Training in Deep Learning