Publications associées à Stochastic Spectral Descent for Discrete Graphical Models

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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

Stochastic Spectral Descent for Discrete Graphical Models

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Understanding generalization and robustness in modern deep learning

Phenomenological theory of variational quantum ground-state preparation

Universal and adaptive methods for robust stochastic optimization

Masked Training of Neural Networks with Partial Gradients

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

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

Towards Understanding Sharpness-Aware Minimization

Metrizing Fairness

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

Understanding generalization and robustness in modern deep learning

Universal and adaptive methods for robust stochastic optimization

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

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

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

Metrizing Fairness

Phenomenological theory of variational quantum ground-state preparation

Masked Training of Neural Networks with Partial Gradients

Towards Understanding Sharpness-Aware Minimization

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