Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise

6 April 2022

Papers citing "Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise"

5 / 5 papers shown

Title
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees Aleksandar Armacki Shuhua Yu Pranay Sharma Gauri Joshi Dragana Bajović D. Jakovetić S. Kar 55 2 0 17 Oct 2024
From Gradient Clipping to Normalization for Heavy Tailed SGD Florian Hübler Ilyas Fatkhullin Niao He 40 5 0 17 Oct 2024
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise Aleksandar Armacki Pranay Sharma Gauri Joshi Dragana Bajović D. Jakovetić S. Kar 17 4 0 28 Oct 2023
Smoothed Gradient Clipping and Error Feedback for Distributed Optimization under Heavy-Tailed Noise Shuhua Yu D. Jakovetić S. Kar 28 1 0 25 Oct 2023
Stochastic Nonsmooth Convex Optimization with Heavy-Tailed Noises: High-Probability Bound, In-Expectation Rate and Initial Distance Adaptation Zijian Liu Zhengyuan Zhou 14 10 0 22 Mar 2023