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

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

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

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

Understanding generalization and robustness in modern deep learning

Masked Training of Neural Networks with Partial Gradients

Universal and adaptive methods for robust stochastic optimization

Metrizing Fairness

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

Towards Understanding Sharpness-Aware Minimization

Phenomenological theory of variational quantum ground-state preparation

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