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Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient
  Clipping

Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping

21 May 2020
Eduard A. Gorbunov
Marina Danilova
Alexander Gasnikov
ArXivPDFHTML

Papers citing "Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping"

7 / 7 papers shown
Title
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis
Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis
Zhijie Chen
Qiaobo Li
A. Banerjee
FedML
51
0
0
11 Nov 2024
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
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
83
2
0
17 Oct 2024
Why are Adaptive Methods Good for Attention Models?
Why are Adaptive Methods Good for Attention Models?
J.N. Zhang
Sai Praneeth Karimireddy
Andreas Veit
Seungyeon Kim
Sashank J. Reddi
Surinder Kumar
S. Sra
79
80
0
06 Dec 2019
Algorithms of Robust Stochastic Optimization Based on Mirror Descent
  Method
Algorithms of Robust Stochastic Optimization Based on Mirror Descent Method
A. Juditsky
A. Nazin
A. S. Nemirovsky
Alexandre B. Tsybakov
34
63
0
05 Jul 2019
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural
  Networks
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
Umut Simsekli
Levent Sagun
Mert Gurbuzbalaban
77
241
0
18 Jan 2019
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
Pavel Dvurechensky
D. Dvinskikh
Alexander Gasnikov
César A. Uribe
Angelia Nedić
37
105
0
11 Jun 2018
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic
  Programming
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming
Saeed Ghadimi
Guanghui Lan
ODL
90
1,538
0
22 Sep 2013
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