Publications associées à Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

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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

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

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Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Masked Training of Neural Networks with Partial Gradients

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

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks

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

Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Wasserstein Adversarial Regularization for learning with label noise

A Catalyst Framework for Minimax Optimization

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

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

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

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

Wasserstein Adversarial Regularization for learning with label noise

Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks

Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Masked Training of Neural Networks with Partial Gradients

A Catalyst Framework for Minimax Optimization