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2025 ICML ICML 2025

Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

Authors

Savelii Chezhegov, Klyukin Yaroslav, Andrei Semenov, Aleksandr Beznosikov, Alexander Gasnikov, Samuel Horváth, Martin Takac, Eduard Gorbunov

Topics

Machine Learning > Optimization & Theory > Learning Theory Machine Learning > Optimization & Theory > Optimization Artificial Intelligence > Core AI > Large Language Models

Keywords

gradient clipping heavy-tailed noise high probability convergence adagrad-norm adam-norm delayed stepsizes
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