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

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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

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

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On the Generalization of Stochastic Gradient Descent with Momentum

Universal and adaptive methods for robust stochastic optimization

Deep Learning Generalization with Limited and Noisy Labels

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

The statistical complexity of early-stopped mirror descent

Alternation makes the adversary weaker in two-player games

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

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

On the Generalization of Stochastic Gradient Descent with Momentum

Deep Learning Generalization with Limited and Noisy Labels

Universal and adaptive methods for robust stochastic optimization

The statistical complexity of early-stopped mirror descent

Alternation makes the adversary weaker in two-player games

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

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation