Publications associées à Iterative minimization of H2 control performance criteria

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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

Iterative minimization of H2 control performance criteria

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On the Generalization of Stochastic Gradient Descent with Momentum

Universal and adaptive methods for robust stochastic optimization

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

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

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Large Steps in Inverse Rendering of Geometry

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Distributed Learning in Non-Convex Environments-Part I: Agreement at a Linear Rate

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Universal and adaptive methods for robust stochastic optimization

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

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

On the Generalization of Stochastic Gradient Descent with Momentum

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

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Large Steps in Inverse Rendering of Geometry

Distributed Learning in Non-Convex Environments-Part I: Agreement at a Linear Rate

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity