Variance-Reduced Methods for Machine Learning

Variance-Reduced Methods for Machine Learning

2 October 2020

Robert Mansel Gower

Mark W. Schmidt

Francis R. Bach

Peter Richtárik

Papers citing "Variance-Reduced Methods for Machine Learning"

18 / 18 papers shown

Title
Efficient Optimization Algorithms for Linear Adversarial Training Antônio H. Ribeiro Thomas B. Schon Dave Zahariah Francis Bach AAML 28 1 0 16 Oct 2024
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition Zhong Zheng Haochen Zhang Lingzhou Xue OffRL 68 2 0 10 Oct 2024
Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-Training of Deep Networks S. Joshi Jiayi Ni Baharan Mirzasoleiman DD 65 2 0 03 Oct 2024
An Effective Dynamic Gradient Calibration Method for Continual Learning Weichen Lin Jiaxiang Chen Ru Huang Huihua Ding CLL 37 0 0 30 Jul 2024
SOREL: A Stochastic Algorithm for Spectral Risks Minimization Yuze Ge Rujun Jiang 33 0 0 19 Jul 2024
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat A. Maranjyan Peter Richtárik 34 3 0 07 Mar 2024
Critical Influence of Overparameterization on Sharpness-aware Minimization Sungbin Shin Dongyeop Lee Maksym Andriushchenko Namhoon Lee AAML 39 1 0 29 Nov 2023
A Large Deviations Perspective on Policy Gradient Algorithms Wouter Jongeneel Daniel Kuhn Mengmeng Li 11 1 0 13 Nov 2023
A Coefficient Makes SVRG Effective Yida Yin Zhiqiu Xu Zhiyuan Li Trevor Darrell Zhuang Liu 15 1 0 09 Nov 2023
$Sketch-and-Project Meets Newton Method: Global $\mathcal O(k^{-2})$ Convergence with Low-Rank Updates$ Sketch-and-Project Meets Newton Method: Global $\mathcal O(k^{-2})$ Convergence with Low-Rank Updates Slavomír Hanzely 15 6 0 22 May 2023
A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization Tesi Xiao Xuxing Chen Krishnakumar Balasubramanian Saeed Ghadimi 11 10 0 20 Feb 2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction Bowen Lei Dongkuan Xu Ruqi Zhang Shuren He Bani Mallick 25 6 0 09 Jan 2023
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Aaron Sidford 37 10 0 17 Jun 2022
A Statistical Analysis of Polyak-Ruppert Averaged Q-learning Xiang Li Wenhao Yang Jiadong Liang Zhihua Zhang Michael I. Jordan 19 15 0 29 Dec 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark W. Schmidt Simon Lacoste-Julien 13 17 0 18 Feb 2021
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 119 1,190 0 16 Aug 2016
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 76 736 0 19 Mar 2014
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 60 317 0 18 Feb 2014