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1907.02707
Cited By
Algorithms of Robust Stochastic Optimization Based on Mirror Descent Method
5 July 2019
A. Juditsky
A. Nazin
A. S. Nemirovsky
Alexandre B. Tsybakov
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Papers citing
"Algorithms of Robust Stochastic Optimization Based on Mirror Descent Method"
34 / 34 papers shown
Title
Convergence of Clipped-SGD for Convex
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-Smooth Optimization with Heavy-Tailed Noise
S. Chezhegov
Aleksandr Beznosikov
Samuel Horváth
Eduard A. Gorbunov
24
0
0
27 May 2025
From Gradient Clipping to Normalization for Heavy Tailed SGD
Florian Hübler
Ilyas Fatkhullin
Niao He
113
10
0
17 Oct 2024
Making Robust Generalizers Less Rigid with Loss Concentration
Matthew J. Holland
Toma Hamada
OOD
85
0
0
07 Aug 2024
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence
Ilyas Fatkhullin
Niao He
76
4
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27 Feb 2024
The Price of Adaptivity in Stochastic Convex Optimization
Y. Carmon
Oliver Hinder
85
7
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16 Feb 2024
General Tail Bounds for Non-Smooth Stochastic Mirror Descent
Khaled Eldowa
Andrea Paudice
74
6
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12 Dec 2023
Smoothed Gradient Clipping and Error Feedback for Distributed Optimization under Heavy-Tailed Noise
Shuhua Yu
D. Jakovetić
S. Kar
62
1
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25 Oct 2023
Robust Stochastic Optimization via Gradient Quantile Clipping
Ibrahim Merad
Stéphane Gaïffas
79
2
0
29 Sep 2023
Clip21: Error Feedback for Gradient Clipping
Sarit Khirirat
Eduard A. Gorbunov
Samuel Horváth
Rustem Islamov
Fakhri Karray
Peter Richtárik
61
10
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30 May 2023
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited
Jinyan Su
Changhong Zhao
Di Wang
70
5
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31 Mar 2023
High Probability Convergence of Stochastic Gradient Methods
Zijian Liu
Ta Duy Nguyen
Thien Hai Nguyen
Alina Ene
Huy Le Nguyen
55
45
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28 Feb 2023
Large deviations rates for stochastic gradient descent with strongly convex functions
Dragana Bajović
D. Jakovetić
S. Kar
72
6
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02 Nov 2022
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise
D. A. Parletta
Andrea Paudice
Massimiliano Pontil
Saverio Salzo
116
10
0
17 Aug 2022
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
Eduard A. Gorbunov
Marina Danilova
David Dobre
Pavel Dvurechensky
Alexander Gasnikov
Gauthier Gidel
77
27
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02 Jun 2022
Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise
D. Jakovetić
Dragana Bajović
Anit Kumar Sahu
S. Kar
Nemanja Milošević
Dusan Stamenkovic
62
14
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06 Apr 2022
Flexible risk design using bi-directional dispersion
Matthew J. Holland
97
6
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28 Mar 2022
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance
Nuri Mert Vural
Lu Yu
Krishnakumar Balasubramanian
S. Volgushev
Murat A. Erdogdu
50
23
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23 Feb 2022
Stochastic linear optimization never overfits with quadratically-bounded losses on general data
Matus Telgarsky
87
12
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14 Feb 2022
Heavy-tailed Streaming Statistical Estimation
Che-Ping Tsai
Adarsh Prasad
Sivaraman Balakrishnan
Pradeep Ravikumar
86
10
0
25 Aug 2021
Robust Online Convex Optimization in the Presence of Outliers
T. Erven
Sarah Sachs
Wouter M. Koolen
W. Kotłowski
45
8
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05 Jul 2021
Robust learning with anytime-guaranteed feedback
Matthew J. Holland
OOD
26
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24 May 2021
Parameter-free Gradient Temporal Difference Learning
Andrew Jacobsen
Alan Chan
OffRL
62
2
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10 May 2021
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance
Hongjian Wang
Mert Gurbuzbalaban
Lingjiong Zhu
Umut cSimcsekli
Murat A. Erdogdu
83
42
0
20 Feb 2021
Optimal robust mean and location estimation via convex programs with respect to any pseudo-norms
Jules Depersin
Guillaume Lecué
84
12
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01 Feb 2021
Better scalability under potentially heavy-tailed feedback
Matthew J. Holland
52
1
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14 Dec 2020
Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise
Yan Yan
Xin Man
Tianbao Yang
21
0
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17 Jun 2020
Sparse recovery by reduced variance stochastic approximation
A. Juditsky
A. Kulunchakov
Hlib Tsyntseus
48
7
0
11 Jun 2020
Improved scalability under heavy tails, without strong convexity
Matthew J. Holland
26
1
0
02 Jun 2020
Better scalability under potentially heavy-tailed gradients
Matthew J. Holland
49
1
0
01 Jun 2020
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
Eduard A. Gorbunov
Marina Danilova
Alexander Gasnikov
68
123
0
21 May 2020
A termination criterion for stochastic gradient descent for binary classification
Sina Baghal
Courtney Paquette
S. Vavasis
29
0
0
23 Mar 2020
ERM and RERM are optimal estimators for regression problems when malicious outliers corrupt the labels
Chinot Geoffrey
74
13
0
24 Oct 2019
A generalization of regularized dual averaging and its dynamics
Shih-Kang Chao
Guang Cheng
60
18
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22 Sep 2019
From low probability to high confidence in stochastic convex optimization
Damek Davis
Dmitriy Drusvyatskiy
Lin Xiao
Junyu Zhang
73
5
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31 Jul 2019
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