Publications associées à Linear Speedup In Saddle-Point Escape For Decentralized Non-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

Linear Speedup In Saddle-Point Escape For Decentralized Non-Convex Optimization

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BENIGN LANDSCAPES OF LOW-DIMENSIONAL RELAXATIONS FOR ORTHOGONAL SYNCHRONIZATION ON GENERAL GRAPHS

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

Finding stationary points on bounded-rank matrices: a geometric hurdle and a smooth remedy

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

Distributed Learning in Non-Convex Environments-Part II: Polynomial Escape From Saddle-Points

Second-Order Guarantees In Federated Learning

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

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

BENIGN LANDSCAPES OF LOW-DIMENSIONAL RELAXATIONS FOR ORTHOGONAL SYNCHRONIZATION ON GENERAL GRAPHS

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Finding stationary points on bounded-rank matrices: a geometric hurdle and a smooth remedy

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

Distributed Learning in Non-Convex Environments-Part II: Polynomial Escape From Saddle-Points

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

Second-Order Guarantees In Federated Learning

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

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