Stochastic Variance Reduction Methods for Saddle-Point Problems

20 May 2016

Papers citing "Stochastic Variance Reduction Methods for Saddle-Point Problems"

41 / 41 papers shown

Title
Contractivity and linear convergence in bilinear saddle-point problems: An operator-theoretic approach Colin Dirren Mattia Bianchi Panagiotis D. Grontas John Lygeros Florian Dorfler 41 0 0 18 Oct 2024
Multiple Greedy Quasi-Newton Methods for Saddle Point Problems Minheng Xiao Shi Bo Zhizhong Wu 48 5 0 01 Aug 2024
SOREL: A Stochastic Algorithm for Spectral Risks Minimization Yuze Ge Rujun Jiang 47 0 0 19 Jul 2024
Stochastic Halpern iteration in normed spaces and applications to reinforcement learning Mario Bravo Juan Pablo Contreras 48 3 0 19 Mar 2024
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities Aleksandr Beznosikov S. Samsonov Marina Sheshukova Alexander Gasnikov A. Naumov Eric Moulines 57 14 0 25 May 2023
Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Problems Jérôme Bolte Edouard Pauwels Antonio Silveti-Falls 28 13 0 15 Dec 2022
SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities Aleksandr Beznosikov Alexander Gasnikov 45 2 0 12 Oct 2022
Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems: A Survey Aleksandr Beznosikov Boris Polyak Eduard A. Gorbunov D. Kovalev Alexander Gasnikov 44 31 0 29 Aug 2022
AUC Maximization in the Era of Big Data and AI: A Survey Tianbao Yang Yiming Ying 51 179 0 28 Mar 2022
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods Aleksandr Beznosikov Eduard A. Gorbunov Hugo Berard Nicolas Loizou 24 49 0 15 Feb 2022
Optimal Algorithms for Decentralized Stochastic Variational Inequalities D. Kovalev Aleksandr Beznosikov Abdurakhmon Sadiev Michael Persiianov Peter Richtárik Alexander Gasnikov 42 35 0 06 Feb 2022
Decentralized Stochastic Variance Reduced Extragradient Method Luo Luo Haishan Ye 27 7 0 01 Feb 2022
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization Siqi Zhang Junchi Yang Cristóbal Guzmán Negar Kiyavash Niao He 38 61 0 29 Mar 2021
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! D. Kovalev Anastasia Koloskova Martin Jaggi Peter Richtárik Sebastian U. Stich 31 73 0 03 Nov 2020
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems Jiawei Zhang Peijun Xiao Ruoyu Sun Zhi-Quan Luo 37 97 0 29 Oct 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark Schmidt Francis R. Bach Peter Richtárik 24 112 0 02 Oct 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou Hugo Berard Alexia Jolicoeur-Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas 41 50 0 08 Jul 2020
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration Wenlong Mou C. J. Li Martin J. Wainwright Peter L. Bartlett Michael I. Jordan 33 75 0 09 Apr 2020
Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems Junchi Yang Negar Kiyavash Niao He 30 83 0 22 Feb 2020
Accelerating Smooth Games by Manipulating Spectral Shapes Waïss Azizian Damien Scieur Ioannis Mitliagkas Simon Lacoste-Julien Gauthier Gidel 35 48 0 02 Jan 2020
Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization Abhishek Roy Yifang Chen Krishnakumar Balasubramanian P. Mohapatra 19 26 0 03 Dec 2019
Variance Reduced Stochastic Proximal Algorithm for AUC Maximization Soham Dan Dushyant Sahoo 12 3 0 08 Nov 2019
A Decentralized Proximal Point-type Method for Saddle Point Problems Weijie Liu Aryan Mokhtari Asuman Ozdaglar S. Pattathil Zebang Shen Nenggan Zheng 72 30 0 31 Oct 2019
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization Adithya M. Devraj Jianshu Chen 32 13 0 22 Jul 2019
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators Xun Zhang W. Haskell Z. Ye 25 3 0 22 Jun 2019
Linear Lower Bounds and Conditioning of Differentiable Games Adam Ibrahim Waïss Azizian Gauthier Gidel Ioannis Mitliagkas 31 10 0 17 Jun 2019
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates Sharan Vaswani Aaron Mishkin I. Laradji Mark Schmidt Gauthier Gidel Simon Lacoste-Julien ODL 50 205 0 24 May 2019
$Stochastic Primal-Dual Algorithms with Faster Convergence than $O(1/\sqrt{T})$ for Problems without Bilinear Structure$ Stochastic Primal-Dual Algorithms with Faster Convergence than $O(1/\sqrt{T})$ for Problems without Bilinear Structure Yan Yan Yi Tian Xu Qihang Lin Lijun Zhang Tianbao Yang 27 35 0 23 Apr 2019
Reducing Noise in GAN Training with Variance Reduced Extragradient Tatjana Chavdarova Gauthier Gidel François Fleuret Simon Lacoste-Julien 25 135 0 18 Apr 2019
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 20 2 0 21 Mar 2019
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach Aryan Mokhtari Asuman Ozdaglar S. Pattathil 32 325 0 24 Jan 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning Aaron Defazio Léon Bottou UQCV DRL 23 112 0 11 Dec 2018
Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning Hassan Rafique Mingrui Liu Qihang Lin Tianbao Yang 15 107 0 04 Oct 2018
Stochastic Variance-Reduced Policy Gradient Matteo Papini Damiano Binaghi Giuseppe Canonaco Matteo Pirotta Marcello Restelli 24 174 0 14 Jun 2018
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization Hoi-To Wai Zhuoran Yang Zhaoran Wang Mingyi Hong 30 169 0 03 Jun 2018
A Variational Inequality Perspective on Generative Adversarial Networks Gauthier Gidel Hugo Berard Gaëtan Vignoud Pascal Vincent Simon Lacoste-Julien GAN 28 351 0 28 Feb 2018
Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity S. Du Wei Hu 68 120 0 05 Feb 2018
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications A. Chambolle Matthias Joachim Ehrhardt Peter Richtárik Carola-Bibiane Schönlieb 38 184 0 15 Jun 2017
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms Jialei Wang Lin Xiao 11 42 0 07 Mar 2017
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 93 737 0 19 Mar 2014
Convex Sparse Matrix Factorizations Francis R. Bach Julien Mairal Jean Ponce 142 143 0 10 Dec 2008