A Proximal Stochastic Gradient Method with Progressive Variance Reduction

19 March 2014

Tong Zhang

Papers citing "A Proximal Stochastic Gradient Method with Progressive Variance Reduction"

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Title
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Jingruo Sun Zachary Frangella Madeleine Udell 36 0 0 28 Jan 2025
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement Qinglun Li Miao Zhang Mengzhu Wang Quanjun Yin Li Shen OODD FedML 26 0 0 09 Oct 2024
Obtaining Lower Query Complexities through Lightweight Zeroth-Order Proximal Gradient Algorithms Bin Gu Xiyuan Wei Hualin Zhang Yi Chang Heng-Chiao Huang FedML 23 0 0 03 Oct 2024
SOREL: A Stochastic Algorithm for Spectral Risks Minimization Yuze Ge Rujun Jiang 38 0 0 19 Jul 2024
Implicit Diffusion: Efficient Optimization through Stochastic Sampling Pierre Marion Anna Korba Peter Bartlett Mathieu Blondel Valentin De Bortoli Arnaud Doucet Felipe Llinares-López Courtney Paquette Quentin Berthet 79 12 0 08 Feb 2024
A Coefficient Makes SVRG Effective Yida Yin Zhiqiu Xu Zhiyuan Li Trevor Darrell Zhuang Liu 33 1 0 09 Nov 2023
GBM-based Bregman Proximal Algorithms for Constrained Learning Zhenwei Lin Qi Deng 26 1 0 21 Aug 2023
Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time Xiang Ji Gen Li OffRL 32 7 0 24 May 2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis Dachao Lin Yuze Han Haishan Ye Zhihua Zhang 22 11 0 15 Apr 2023
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited Jinyan Su Changhong Zhao Di Wang 33 3 0 31 Mar 2023
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization Le‐Yu Chen Jing Xu Luo Luo 31 15 0 16 Jan 2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction Bowen Lei Dongkuan Xu Ruqi Zhang Shuren He Bani Mallick 32 6 0 09 Jan 2023
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation Xiao-Tong Yuan P. Li 34 2 0 09 Jan 2023
FSCNN: A Fast Sparse Convolution Neural Network Inference System Bo Ji Tianyi Chen 23 3 0 17 Dec 2022
Stochastic Steffensen method Minda Zhao Zehua Lai Lek-Heng Lim ODL 15 3 0 28 Nov 2022
A Stochastic Variance Reduced Gradient using Barzilai-Borwein Techniques as Second Order Information Hardik Tankaria N. Yamashita 11 1 0 23 Aug 2022
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Aaron Sidford 52 11 0 17 Jun 2022
Stochastic Gradient Methods with Preconditioned Updates Abdurakhmon Sadiev Aleksandr Beznosikov Abdulla Jasem Almansoori Dmitry Kamzolov R. Tappenden Martin Takáč ODL 34 9 0 01 Jun 2022
Data-Consistent Local Superresolution for Medical Imaging Junqi Tang SupR 30 0 0 22 Feb 2022
MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an Exponential Convergence Rate Aixiang Chen Chen Jinting Zhang Zanbo Zhang Zhihong Li 38 0 0 21 Feb 2022
Distributed Learning With Sparsified Gradient Differences Yicheng Chen Rick S. Blum Martin Takáč Brian M. Sadler 31 15 0 05 Feb 2022
On the Complexity of a Practical Primal-Dual Coordinate Method Ahmet Alacaoglu V. Cevher Stephen J. Wright 21 12 0 19 Jan 2022
Accelerated and instance-optimal policy evaluation with linear function approximation Tianjiao Li Guanghui Lan A. Pananjady OffRL 37 13 0 24 Dec 2021
Training Structured Neural Networks Through Manifold Identification and Variance Reduction Zih-Syuan Huang Ching-pei Lee AAML 46 9 0 05 Dec 2021
Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization Xia Jiang Xianlin Zeng Jian Sun Jie Chen Lihua Xie 18 6 0 06 Nov 2021
An Asymptotic Analysis of Minibatch-Based Momentum Methods for Linear Regression Models Yuan Gao Xuening Zhu Haobo Qi Guodong Li Riquan Zhang Hansheng Wang 18 3 0 02 Nov 2021
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima Zixiang Chen Dongruo Zhou Quanquan Gu 38 1 0 25 Oct 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 32 14 0 22 Oct 2021
Stochastic Primal-Dual Deep Unrolling Junqi Tang Subhadip Mukherjee Carola-Bibiane Schönlieb 24 4 0 19 Oct 2021
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou Anthony Man-Cho So James Cheng 19 1 0 30 Sep 2021
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Zhize Li 43 14 0 21 Mar 2021
Distributed Second Order Methods with Fast Rates and Compressed Communication Rustem Islamov Xun Qian Peter Richtárik 32 51 0 14 Feb 2021
Neural Network Compression Via Sparse Optimization Tianyi Chen Bo Ji Yixin Shi Tianyu Ding Biyi Fang Sheng Yi Xiao Tu 36 15 0 10 Nov 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 32 0 0 26 Aug 2020
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning Bin Gu An Xu Zhouyuan Huo Cheng Deng Heng-Chiao Huang FedML 38 27 0 14 Aug 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song Yong Jiang Yi Ma 53 23 0 18 Jun 2020
Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems Junchi Yang Negar Kiyavash Niao He 23 83 0 22 Feb 2020
Sampling and Update Frequencies in Proximal Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 27 4 0 13 Feb 2020
Variance Reduced Stochastic Proximal Algorithm for AUC Maximization Soham Dan Dushyant Sahoo 12 3 0 08 Nov 2019
A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression Jiajin Li Sen Huang Anthony Man-Cho So OOD 24 12 0 28 Oct 2019
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 25 30 0 22 Oct 2019
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction Pan Xu F. Gao Quanquan Gu 28 83 0 18 Sep 2019
Empirical study towards understanding line search approximations for training neural networks Younghwan Chae D. Wilke 27 11 0 15 Sep 2019
Private Stochastic Convex Optimization with Optimal Rates Raef Bassily Vitaly Feldman Kunal Talwar Abhradeep Thakurta 21 236 0 27 Aug 2019
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints Qihang Lin Selvaprabu Nadarajah Negar Soheili Tianbao Yang 27 13 0 07 Aug 2019
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators Xun Zhang W. Haskell Z. Ye 6 3 0 22 Jun 2019
Global Optimality Guarantees For Policy Gradient Methods Jalaj Bhandari Daniel Russo 35 185 0 05 Jun 2019
Why gradient clipping accelerates training: A theoretical justification for adaptivity Junzhe Zhang Tianxing He S. Sra Ali Jadbabaie 30 442 0 28 May 2019
Reducing Noise in GAN Training with Variance Reduced Extragradient Tatjana Chavdarova Gauthier Gidel F. Fleuret Simon Lacoste-Julien 25 134 0 18 Apr 2019
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 20 2 0 21 Mar 2019