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Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration

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Published in:JMLR
Format: Online Article RSS Article
Published: 2026
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spellingShingle Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
subject_facet Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
title Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
title_auth Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
title_full Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
title_fullStr Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
title_full_unstemmed Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
title_short Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
title_sort revisiting gradient normalization and clipping for nonconvex sgd under heavy-tailed noise: necessity, sufficiency, and acceleration
topic Artificial Intelligence & Machine Learning
Computer Science & IT
Engineering & Technology
url http://jmlr.org/papers/v26/24-1991.html