Publications associées à Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

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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

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

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Universal and adaptive methods for robust stochastic optimization

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

Momentum-Based Policy Gradient with Second-Order Information

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

Large Steps in Inverse Rendering of Geometry

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

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

The role of PDE-based parameterization techniques in gradient-based IGA shape optimization applications

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Adaptive Gradient Descent without Descent

Universal and adaptive methods for robust stochastic optimization

Momentum-Based Policy Gradient with Second-Order Information

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

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

The role of PDE-based parameterization techniques in gradient-based IGA shape optimization applications

Adaptive Gradient Descent without Descent

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

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

Large Steps in Inverse Rendering of Geometry

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