Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization

12 September 2011

Nicolas Le Roux

Papers citing "Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization"

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Title
Sum-of-norms regularized Nonnegative Matrix Factorization Andersen Ang Waqas Bin Hamed H. Sterck 23 0 0 30 Jun 2024
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks Fanghui Liu L. Dadi V. Cevher 85 2 0 29 Apr 2024
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation Aaron Mishkin Mert Pilanci Mark Schmidt 66 1 0 03 Apr 2024
An inexact Bregman proximal point method and its acceleration version for unbalanced optimal transport Xiang Chen Faqiang Wang Jun Liu Lipeng Cui OT 19 0 0 26 Feb 2024
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms Minghao Yang Xiyuan Wei Tianbao Yang Yiming Ying 50 1 0 07 Jul 2023
Target-based Surrogates for Stochastic Optimization J. Lavington Sharan Vaswani Reza Babanezhad Mark Schmidt Nicolas Le Roux 60 5 0 06 Feb 2023
On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality Lu Xia M. Hochstenbach Stefano Massei 27 2 0 23 Jan 2023
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation Xiao-Tong Yuan P. Li 41 2 0 09 Jan 2023
A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming Parvin Nazari Ahmad Mousavi Davoud Ataee Tarzanagh George Michailidis 41 4 0 08 Nov 2022
Stability and Generalization for Markov Chain Stochastic Gradient Methods Puyu Wang Yunwen Lei Yiming Ying Ding-Xuan Zhou 24 18 0 16 Sep 2022
Deep Model-Based Architectures for Inverse Problems under Mismatched Priors Shirin Shoushtari Jiaming Liu Yuyang Hu Ulugbek S. Kamilov 28 6 0 26 Jul 2022
Learning from time-dependent streaming data with online stochastic algorithms Antoine Godichon-Baggioni Nicklas Werge Olivier Wintenberger 40 3 0 25 May 2022
Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition Ching-pei Lee Ling Liang Tianyun Tang Kim-Chuan Toh 37 11 0 29 Apr 2022
Sharper Bounds for Proximal Gradient Algorithms with Errors Anis Hamadouche Yun-Shun Wu Andrew M. Wallace João F. C. Mota 13 7 0 04 Mar 2022
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions Aaron Mishkin Arda Sahiner Mert Pilanci OffRL 77 30 0 02 Feb 2022
Federated Optimization of Smooth Loss Functions Ali Jadbabaie A. Makur Devavrat Shah FedML 32 7 0 06 Jan 2022
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
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data Antoine Godichon-Baggioni Nicklas Werge Olivier Wintenberger 25 7 0 15 Sep 2021
From inexact optimization to learning via gradient concentration Bernhard Stankewitz Nicole Mücke Lorenzo Rosasco 31 5 0 09 Jun 2021
Stronger NAS with Weaker Predictors Junru Wu Xiyang Dai Dongdong Chen Yinpeng Chen Mengchen Liu Ye Yu Zhangyang Wang Zicheng Liu Mei Chen Lu Yuan OOD 38 41 0 21 Feb 2021
Constrained and Composite Optimization via Adaptive Sampling Methods Yuchen Xie Raghu Bollapragada R. Byrd J. Nocedal 27 14 0 31 Dec 2020
Learning Graph Neural Networks with Approximate Gradient Descent Qunwei Li Shaofeng Zou Leon Wenliang Zhong GNN 34 1 0 07 Dec 2020
Factorization Machines with Regularization for Sparse Feature Interactions Kyohei Atarashi S. Oyama M. Kurihara 19 5 0 19 Oct 2020
Lower Bounds and Optimal Algorithms for Personalized Federated Learning Filip Hanzely Slavomír Hanzely Samuel Horváth Peter Richtárik FedML 62 187 0 05 Oct 2020
Hybrid Differentially Private Federated Learning on Vertically Partitioned Data Chang Wang Jian Liang Mingkai Huang Bing Bai Kun Bai Hao Li FedML 23 39 0 06 Sep 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 42 0 0 26 Aug 2020
A Survey on Large-scale Machine Learning Meng Wang Weijie Fu Xiangnan He Shijie Hao Xindong Wu 25 110 0 10 Aug 2020
On the Convergence of SGD with Biased Gradients Ahmad Ajalloeian Sebastian U. Stich 6 84 0 31 Jul 2020
Revealing hidden dynamics from time-series data by ODENet Pipi Hu Wuyue Yang Yi Zhu L. Hong AI4TS 24 35 0 11 May 2020
Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling HanQin Cai Daniel McKenzie W. Yin Zhenliang Zhang 63 49 0 29 Mar 2020
Distributed Inexact Successive Convex Approximation ADMM: Analysis-Part I Sandeep Kumar K. Rajawat Daniel P. Palomar 29 4 0 21 Jul 2019
Reducing the variance in online optimization by transporting past gradients Sébastien M. R. Arnold Pierre-Antoine Manzagol Reza Babanezhad Ioannis Mitliagkas Nicolas Le Roux 26 28 0 08 Jun 2019
Neumann Networks for Inverse Problems in Imaging Davis Gilton Greg Ongie Rebecca Willett 19 24 0 13 Jan 2019
Fault Tolerance in Iterative-Convergent Machine Learning Aurick Qiao Bryon Aragam Bingjing Zhang Eric Xing 26 41 0 17 Oct 2018
SEGA: Variance Reduction via Gradient Sketching Filip Hanzely Konstantin Mishchenko Peter Richtárik 25 71 0 09 Sep 2018
On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems Risheng Liu Shichao Cheng Yi He Xin-Yue Fan Zhouchen Lin Zhongxuan Luo 29 68 0 16 Aug 2018
Approximate Temporal Difference Learning is a Gradient Descent for Reversible Policies Yann Ollivier 9 18 0 02 May 2018
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization Ching-pei Lee Cong Han Lim Stephen J. Wright 26 29 0 04 Mar 2018
Analysis of Biased Stochastic Gradient Descent Using Sequential Semidefinite Programs Bin Hu Peter M. Seiler Laurent Lessard 24 39 0 03 Nov 2017
Stochastic Nonconvex Optimization with Large Minibatches Weiran Wang Nathan Srebro 40 26 0 25 Sep 2017
Don't relax: early stopping for convex regularization Simon Matet Lorenzo Rosasco S. Villa Bang Long Vu 30 19 0 18 Jul 2017
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization Qunwei Li Yi Zhou Yingbin Liang P. Varshney 21 94 0 14 May 2017
Deep Relaxation: partial differential equations for optimizing deep neural networks Pratik Chaudhari Adam M. Oberman Stanley Osher Stefano Soatto G. Carlier 27 153 0 17 Apr 2017
Convergence of the Forward-Backward Algorithm: Beyond the Worst Case with the Help of Geometry Guillaume Garrigos Lorenzo Rosasco S. Villa 11 41 0 28 Mar 2017
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox Jialei Wang Weiran Wang Nathan Srebro 16 54 0 21 Feb 2017
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark Schmidt 139 1,205 0 16 Aug 2016
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 117 3,178 0 15 Jun 2016
Highly-Smooth Zero-th Order Online Optimization Vianney Perchet Francis R. Bach Vianney Perchet 20 83 0 26 May 2016
Accelerated Randomized Mirror Descent Algorithms For Composite Non-strongly Convex Optimization L. Hien Cuong V Nguyen Huan Xu Canyi Lu Jiashi Feng 28 19 0 23 May 2016
Hyperparameter optimization with approximate gradient Fabian Pedregosa 45 442 0 07 Feb 2016