Publications associées à Diffusion Learning In Non-Convex Environments

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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

Diffusion Learning In Non-Convex Environments

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On the Generalization of Stochastic Gradient Descent with Momentum

Phenomenological theory of variational quantum ground-state preparation

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

Masked Training of Neural Networks with Partial Gradients

Quantization for Distributed Processing and Learning of Structured Data

ADAGRAD Avoids Saddle Points

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

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

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

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

Quantization for Distributed Processing and Learning of Structured Data

On the Generalization of Stochastic Gradient Descent with Momentum

Phenomenological theory of variational quantum ground-state preparation

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

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Masked Training of Neural Networks with Partial Gradients

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

ADAGRAD Avoids Saddle Points