Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization

10 September 2013

Tong Zhang

Papers citing "Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization"

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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 36 0 0 18 Oct 2024
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning Michal Dereziñski Christopher Musco Jiaming Yang 45 2 0 09 May 2024
Multiple Locally Linear Kernel Machines David Picard 15 1 0 17 Jan 2024
Variance-Reduced Conservative Policy Iteration Naman Agarwal Brian Bullins Karan Singh 29 3 0 12 Dec 2022
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Aaron Sidford 55 11 0 17 Jun 2022
A Stochastic Bundle Method for Interpolating Networks Alasdair Paren Leonard Berrada Rudra P. K. Poudel M. P. Kumar 24 4 0 29 Jan 2022
On the Complexity of a Practical Primal-Dual Coordinate Method Ahmet Alacaoglu V. Cevher Stephen J. Wright 23 12 0 19 Jan 2022
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao Lingxiao Wang Mladen Kolar Ziqi Liu Qing Cui Jun Zhou Chaochao Chen FedML 34 10 0 28 Dec 2021
Structured Convolutional Kernel Networks for Airline Crew Scheduling Yassine Yaakoubi F. Soumis Simon Lacoste-Julien AI4TS 23 10 0 25 May 2021
First-Order Methods for Convex Optimization Pavel Dvurechensky Mathias Staudigl Shimrit Shtern ODL 31 25 0 04 Jan 2021
Optimal Client Sampling for Federated Learning Wenlin Chen Samuel Horváth Peter Richtárik FedML 42 190 0 26 Oct 2020
Lower Bounds and Optimal Algorithms for Personalized Federated Learning Filip Hanzely Slavomír Hanzely Samuel Horváth Peter Richtárik FedML 47 186 0 05 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 32 0 0 26 Aug 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song Yong Jiang Yi Ma 53 23 0 18 Jun 2020
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks Chunfeng Cui Kaiqi Zhang Talgat Daulbaev Julia Gusak Ivan V. Oseledets Zheng-Wei Zhang AAML 26 25 0 29 Oct 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
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model Tong Wang Qihang Lin 38 19 0 10 May 2019
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization Nhan H. Pham Lam M. Nguyen Dzung Phan Quoc Tran-Dinh 11 139 0 15 Feb 2019
Asynchronous Accelerated Proximal Stochastic Gradient for Strongly Convex Distributed Finite Sums Hadrien Hendrikx Francis R. Bach Laurent Massoulié FedML 8 26 0 28 Jan 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning Aaron Defazio Léon Bottou UQCV DRL 23 112 0 11 Dec 2018
Online Adaptive Methods, Universality and Acceleration Kfir Y. Levy A. Yurtsever V. Cevher ODL 25 88 0 08 Sep 2018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator Cong Fang C. J. Li Zhouchen Lin Tong Zhang 41 570 0 04 Jul 2018
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates Kaiwen Zhou Fanhua Shang James Cheng 14 74 0 28 Jun 2018
On the insufficiency of existing momentum schemes for Stochastic Optimization Rahul Kidambi Praneeth Netrapalli Prateek Jain Sham Kakade ODL 22 117 0 15 Mar 2018
Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization Zeyuan Allen-Zhu ODL 44 52 0 12 Feb 2018
Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields Rémi Le Priol Alexandre Piché Simon Lacoste-Julien 26 5 0 22 Dec 2017
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent Chi Jin Praneeth Netrapalli Michael I. Jordan ODL 37 261 0 28 Nov 2017
Leverage Score Sampling for Faster Accelerated Regression and ERM Naman Agarwal Sham Kakade Rahul Kidambi Y. Lee Praneeth Netrapalli Aaron Sidford 26 21 0 22 Nov 2017
An inexact subsampled proximal Newton-type method for large-scale machine learning Xuanqing Liu Cho-Jui Hsieh J. Lee Yuekai Sun 27 15 0 28 Aug 2017
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning Frank E. Curtis K. Scheinberg 33 45 0 30 Jun 2017
A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints Bin Hu Peter M. Seiler Anders Rantzer 27 35 0 25 Jun 2017
Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification Maksim Lapin Matthias Hein Bernt Schiele 34 99 0 12 Dec 2016
Asynchronous Stochastic Block Coordinate Descent with Variance Reduction Bin Gu Zhouyuan Huo Heng-Chiao Huang 23 10 0 29 Oct 2016
An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration Hongzhou Lin Julien Mairal Zaïd Harchaoui 28 13 0 04 Oct 2016
Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method Lihua Lei Michael I. Jordan 26 96 0 12 Sep 2016
On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization Xingguo Li T. Zhao R. Arora Han Liu Mingyi Hong 21 15 0 10 Jul 2016
Dimension-Free Iteration Complexity of Finite Sum Optimization Problems Yossi Arjevani Ohad Shamir 16 24 0 30 Jun 2016
Variance-Reduced Proximal Stochastic Gradient Descent for Non-convex Composite optimization Xiyu Yu Dacheng Tao 19 5 0 02 Jun 2016
Tight Complexity Bounds for Optimizing Composite Objectives Blake E. Woodworth Nathan Srebro 26 185 0 25 May 2016
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Aryan Mokhtari Alejandro Ribeiro ODL 17 32 0 24 May 2016
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization Shun Zheng Jialei Wang Fen Xia Wenyuan Xu Tong Zhang 18 22 0 13 Apr 2016
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods Zeyuan Allen-Zhu ODL 15 575 0 18 Mar 2016
Variance Reduction for Faster Non-Convex Optimization Zeyuan Allen-Zhu Elad Hazan ODL 16 390 0 17 Mar 2016
On the Influence of Momentum Acceleration on Online Learning Kun Yuan Bicheng Ying Ali H. Sayed 26 58 0 14 Mar 2016
A Simple Practical Accelerated Method for Finite Sums Aaron Defazio 25 120 0 08 Feb 2016
SCOPE: Scalable Composite Optimization for Learning on Spark Shen-Yi Zhao Ru Xiang Yinghuan Shi Peng Gao Wu-Jun Li 16 16 0 30 Jan 2016
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling Zeyuan Allen-Zhu Zheng Qu Peter Richtárik Yang Yuan 44 172 0 30 Dec 2015
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework Virginia Smith Simone Forte Michael I. Jordan Martin Jaggi 28 28 0 13 Dec 2015
Kalman-based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning V. Patel 15 35 0 03 Dec 2015
Stochastic modified equations and adaptive stochastic gradient algorithms Qianxiao Li Cheng Tai E. Weinan 30 279 0 19 Nov 2015