Publications associées à Online Adaptive Methods, Universality and Acceleration

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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

Online Adaptive Methods, Universality and Acceleration

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Scalable constrained optimization

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

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Probabilistic methods for neural combinatorial optimization

Alternation makes the adversary weaker in two-player games

Predicting in Uncertain Environments: Methods for Robust Machine Learning

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

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

Universal and adaptive methods for robust stochastic optimization

Scalable constrained optimization

Probabilistic methods for neural combinatorial optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Predicting in Uncertain Environments: Methods for Robust Machine Learning

On the Generalization of Stochastic Gradient Descent with Momentum

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

Alternation makes the adversary weaker in two-player games

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

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective