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Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
15 February 2022
Aleksandr Beznosikov
Eduard A. Gorbunov
Hugo Berard
Nicolas Loizou
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Papers citing
"Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods"
34 / 34 papers shown
Extragradient Method for
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(L_0, L_1)
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-Lipschitz Root-finding Problems
S. Choudhury
Nicolas Loizou
148
1
0
25 Oct 2025
Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
Yueyang Quan
Chang Wang
Shengjie Zhai
Minghong Fang
Zhuqing Liu
144
0
0
10 Aug 2025
Layer-wise Quantization for Quantized Optimistic Dual Averaging
Anh Duc Nguyen
Ilia Markov
Frank Zhengqing Wu
Ali Ramezani-Kebrya
Kimon Antonakopoulos
Dan Alistarh
Volkan Cevher
MQ
306
1
0
20 May 2025
HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization
Wei Zhang
Arif Hassan Zidan
Arif Hassan Zidan
Wei Zhang
Tianming Liu
ODL
302
0
0
16 May 2025
Properties of Fixed Points of Generalised Extra Gradient Methods Applied to Min-Max Problems
IEEE Control Systems Letters (L-CSS), 2025
Amir Ali Farzin
Yuen-Man Pun
Philipp Braun
Iman Shames
219
2
0
03 Apr 2025
Sharpness-Aware Minimization: General Analysis and Improved Rates
International Conference on Learning Representations (ICLR), 2025
Dimitris Oikonomou
Nicolas Loizou
390
10
0
04 Mar 2025
Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization
Neural Information Processing Systems (NeurIPS), 2024
Quoc Tran-Dinh
Trang H. Tran
Lam M. Nguyen
224
0
0
29 Oct 2024
Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems
Annual Conference of the IEEE Industrial Electronics Society (IECON), 2024
Steve Yuwono
Marlon Löppenberg
Dorothea Schwung
Andreas Schwung
253
5
0
14 Jun 2024
Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization
IEEE Control Systems Letters (L-CSS), 2024
Tianqi Zheng
Nicolas Loizou
Pengcheng You
Enrique Mallada
366
5
0
14 Mar 2024
Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Konstantinos Emmanouilidis
René Vidal
Nicolas Loizou
219
6
0
11 Mar 2024
Balancing Act: Constraining Disparate Impact in Sparse Models
International Conference on Learning Representations (ICLR), 2023
Meraj Hashemizadeh
Juan Ramirez
Rohan Sukumaran
G. Farnadi
Damien Scieur
Jose Gallego-Posada
435
9
0
31 Oct 2023
DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average Constraints
Raheeb Hassan
K. M. S. Wadith
Md. Mamun-or Rashid
Md. Mosaddek Khan
274
4
0
22 Oct 2023
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
Ahmad Rammal
Kaja Gruntkowska
Nikita Fedin
Eduard A. Gorbunov
Peter Richtárik
441
15
0
15 Oct 2023
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions
International Conference on Learning Representations (ICLR), 2023
Xu Cai
Ahmet Alacaoglu
Jelena Diakonikolas
439
13
0
04 Oct 2023
Distributed Extra-gradient with Optimal Complexity and Communication Guarantees
International Conference on Learning Representations (ICLR), 2023
Ali Ramezani-Kebrya
Kimon Antonakopoulos
Igor Krawczuk
Justin Deschenaux
Volkan Cevher
351
4
0
17 Aug 2023
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Emmanouil-Vasileios Vlatakis-Gkaragkounis
Angeliki Giannou
Yudong Chen
Qiaomin Xie
307
8
0
28 Jun 2023
Omega: Optimistic EMA Gradients
Juan Ramirez
Rohan Sukumaran
Quentin Bertrand
Gauthier Gidel
334
0
0
13 Jun 2023
A Central Limit Theorem for Algorithmic Estimator of Saddle Point
Abhishek Roy
Yian Ma
424
1
0
09 Jun 2023
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
International Conference on Learning Representations (ICLR), 2023
Siqi Zhang
S. Choudhury
Sebastian U. Stich
Nicolas Loizou
FedML
582
9
0
08 Jun 2023
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
Neural Information Processing Systems (NeurIPS), 2023
Aleksandr Beznosikov
S. Samsonov
Marina Sheshukova
Alexander Gasnikov
A. Naumov
Eric Moulines
327
22
0
25 May 2023
Unified analysis of SGD-type methods
Eduard A. Gorbunov
296
3
0
29 Mar 2023
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
Neural Information Processing Systems (NeurIPS), 2023
Zaiwei Chen
Jianchao Tan
Eric Mazumdar
Asuman Ozdaglar
Adam Wierman
383
16
0
03 Mar 2023
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions
Neural Information Processing Systems (NeurIPS), 2023
S. Choudhury
Eduard A. Gorbunov
Nicolas Loizou
358
18
0
27 Feb 2023
Solving stochastic weak Minty variational inequalities without increasing batch size
International Conference on Learning Representations (ICLR), 2023
Thomas Pethick
Olivier Fercoq
P. Latafat
Panagiotis Patrinos
Volkan Cevher
250
32
0
17 Feb 2023
SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization
International Conference on Learning Representations (ICLR), 2022
Hanseul Cho
Chulhee Yun
268
10
0
12 Oct 2022
SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities
Aleksandr Beznosikov
Alexander Gasnikov
333
3
0
12 Oct 2022
Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems: A Survey
European Mathematical Society Magazine (EMS Magazine), 2022
Aleksandr Beznosikov
Boris Polyak
Eduard A. Gorbunov
D. Kovalev
Alexander Gasnikov
406
35
0
29 Aug 2022
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
Neural Information Processing Systems (NeurIPS), 2022
Eduard A. Gorbunov
Marina Danilova
David Dobre
Pavel Dvurechensky
Alexander Gasnikov
Gauthier Gidel
261
34
0
02 Jun 2022
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top
Eduard A. Gorbunov
Samuel Horváth
Peter Richtárik
Gauthier Gidel
AAML
375
0
0
01 Jun 2022
Distributed Methods with Absolute Compression and Error Compensation
Marina Danilova
Eduard A. Gorbunov
292
5
0
04 Mar 2022
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
International Conference on Machine Learning (ICML), 2022
Konstantin Mishchenko
Grigory Malinovsky
Sebastian U. Stich
Peter Richtárik
389
198
0
18 Feb 2022
Optimal Algorithms for Decentralized Stochastic Variational Inequalities
Neural Information Processing Systems (NeurIPS), 2022
D. Kovalev
Aleksandr Beznosikov
Abdurakhmon Sadiev
Michael Persiianov
Peter Richtárik
Alexander Gasnikov
332
39
0
06 Feb 2022
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
Aleksandr Beznosikov
Pavel Dvurechensky
Anastasia Koloskova
V. Samokhin
Sebastian U. Stich
Alexander Gasnikov
393
46
0
15 Jun 2021
Distributed Learning with Compressed Gradient Differences
Konstantin Mishchenko
Eduard A. Gorbunov
Martin Takáč
Peter Richtárik
330
210
0
26 Jan 2019
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