Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

16 April 2015

Papers citing "Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting"

50 / 93 papers shown

Title
Error estimates between SGD with momentum and underdamped Langevin diffusion Arnaud Guillin Yu Wang Lihu Xu Haoran Yang 24 1 0 22 Oct 2024
A Unified Theory of Stochastic Proximal Point Methods without Smoothness Peter Richtárik Abdurakhmon Sadiev Yury Demidovich 45 4 0 24 May 2024
Towards a Better Theoretical Understanding of Independent Subnetwork Training Egor Shulgin Peter Richtárik AI4CE 28 6 0 28 Jun 2023
On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent Rahul Singh A. Shukla Dootika Vats 29 0 0 14 Mar 2023
Non asymptotic analysis of Adaptive stochastic gradient algorithms and applications Antoine Godichon-Baggioni Pierre Tarrago 24 5 0 01 Mar 2023
The Stochastic Proximal Distance Algorithm Hao Jiang Jason Xu 30 0 0 21 Oct 2022
Stochastic Primal-Dual Three Operator Splitting Algorithm with Extension to Equivariant Regularization-by-Denoising Junqi Tang Matthias Joachim Ehrhardt Carola-Bibiane Schönlieb 33 0 0 02 Aug 2022
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches Michal Derezinski 55 5 0 06 Jun 2022
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation Farshid Varno Marzie Saghayi Laya Rafiee Sharut Gupta Stan Matwin Mohammad Havaei FedML 34 30 0 27 Apr 2022
Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient Antoine Godichon-Baggioni 8 6 0 26 Jul 2021
Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition A. Perelli M. S. Andersen 16 7 0 25 Mar 2021
AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods Zheng Shi Abdurakhmon Sadiev Nicolas Loizou Peter Richtárik Martin Takávc ODL 34 13 0 19 Feb 2021
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications Shuyan Hu Xiaojing Chen Wei Ni Ekram Hossain Xin Wang AI4CE 47 111 0 02 Dec 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark W. Schmidt Francis R. Bach Peter Richtárik 19 111 0 02 Oct 2020
A variable metric mini-batch proximal stochastic recursive gradient algorithm with diagonal Barzilai-Borwein stepsize Tengteng Yu Xinwei Liu Yuhong Dai Jie Sun 10 4 0 02 Oct 2020
A general framework for decentralized optimization with first-order methods Ran Xin Shi Pu Angelia Nedić U. Khan 16 87 0 12 Sep 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 34 0 0 26 Aug 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou Hugo Berard Alexia Jolicoeur-Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas 39 50 0 08 Jul 2020
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems Zhan Gao Alec Koppel Alejandro Ribeiro 30 10 0 02 Jul 2020
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization Ahmed Khaled Othmane Sebbouh Nicolas Loizou Robert Mansel Gower Peter Richtárik 11 46 0 20 Jun 2020
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization Sudeep Salgia Qing Zhao Sattar Vakili 48 2 0 11 Mar 2020
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 27 30 0 22 Oct 2019
A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization Minghan Yang Andre Milzarek Zaiwen Wen Tong Zhang ODL 9 36 0 21 Oct 2019
Variance-Reduced Decentralized Stochastic Optimization with Gradient Tracking -- Part II: GT-SVRG Ran Xin U. Khan S. Kar 9 8 0 08 Oct 2019
Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip Nicolas Loizou 24 5 0 26 Sep 2019
Towards closing the gap between the theory and practice of SVRG Othmane Sebbouh Nidham Gazagnadou Samy Jelassi Francis R. Bach Robert Mansel Gower 11 17 0 31 Jul 2019
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization Quoc Tran-Dinh Nhan H. Pham T. Dzung Lam M. Nguyen 27 49 0 08 Jul 2019
A Review on Deep Learning in Medical Image Reconstruction Hai-Miao Zhang Bin Dong MedIm 35 122 0 23 Jun 2019
Accelerating Mini-batch SARAH by Step Size Rules Zhuang Yang Zengping Chen Cheng-Yu Wang 8 15 0 20 Jun 2019
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent Eduard A. Gorbunov Filip Hanzely Peter Richtárik 9 143 0 27 May 2019
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources Yanghua Peng Hang Zhang Yifei Ma Tong He Zhi-Li Zhang Sheng Zha Mu Li 25 23 0 26 Apr 2019
Sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems C. S. Kulkarni 15 10 0 07 Feb 2019
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop D. Kovalev Samuel Horváth Peter Richtárik 30 155 0 24 Jan 2019
SAGA with Arbitrary Sampling Xun Qian Zheng Qu Peter Richtárik 34 25 0 24 Jan 2019
ASVRG: Accelerated Proximal SVRG Fanhua Shang L. Jiao Kaiwen Zhou James Cheng Yan Ren Yufei Jin ODL 21 30 0 07 Oct 2018
Fast Variance Reduction Method with Stochastic Batch Size Xuanqing Liu Cho-Jui Hsieh 15 5 0 07 Aug 2018
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches Jie Liu Yu Rong Martin Takáč Junzhou Huang ODL 22 7 0 14 Jul 2018
Direct Acceleration of SAGA using Sampled Negative Momentum Kaiwen Zhou 8 45 0 28 Jun 2018
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates Kaiwen Zhou Fanhua Shang James Cheng 14 74 0 28 Jun 2018
Stochastic Variance-Reduced Policy Gradient Matteo Papini Damiano Binaghi Giuseppe Canonaco Matteo Pirotta Marcello Restelli 19 174 0 14 Jun 2018
Double Quantization for Communication-Efficient Distributed Optimization Yue Yu Jiaxiang Wu Longbo Huang MQ 19 57 0 25 May 2018
D $^2$ : Decentralized Training over Decentralized Data Hanlin Tang Xiangru Lian Ming Yan Ce Zhang Ji Liu 20 348 0 19 Mar 2018
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning Fanhua Shang Kaiwen Zhou Hongying Liu James Cheng Ivor W. Tsang Lijun Zhang Dacheng Tao L. Jiao 24 65 0 26 Feb 2018
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization Fanhua Shang Yuanyuan Liu Kaiwen Zhou James Cheng K. K. Ng Yuichi Yoshida 19 9 0 26 Feb 2018
A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments Radoslav Harman Lenka Filová Peter Richtárik 26 51 0 17 Jan 2018
Improved asynchronous parallel optimization analysis for stochastic incremental methods Rémi Leblond Fabian Pedregosa Simon Lacoste-Julien 16 70 0 11 Jan 2018
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 19 200 0 27 Dec 2017
Linearly convergent stochastic heavy ball method for minimizing generalization error Nicolas Loizou Peter Richtárik 34 45 0 30 Oct 2017
A Generic Approach for Escaping Saddle points Sashank J. Reddi Manzil Zaheer S. Sra Barnabás Póczós Francis R. Bach Ruslan Salakhutdinov Alex Smola 13 83 0 05 Sep 2017
A Robust Multi-Batch L-BFGS Method for Machine Learning A. Berahas Martin Takáč AAML ODL 17 44 0 26 Jul 2017