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Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition

16 August 2016

Papers citing "Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition"

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Stability and Generalization of Stochastic Gradient Methods for Minimax Problems Yunwen Lei Zhenhuan Yang Tianbao Yang Yiming Ying 74 48 0 08 May 2021
Towards Sharper Utility Bounds for Differentially Private Pairwise Learning Yilin Kang Yong Liu Jian Li Weiping Wang FedML 62 2 0 07 May 2021
Stochastic gradient descent with noise of machine learning type. Part I: Discrete time analysis Stephan Wojtowytsch 69 52 0 04 May 2021
Convergence Analysis and System Design for Federated Learning over Wireless Networks Shuo Wan Jiaxun Lu Pingyi Fan Yunfeng Shao Chenghui Peng Khaled B. Letaief 82 55 0 30 Apr 2021
Decentralized Federated Averaging Tao Sun Dongsheng Li Bao Wang FedML 88 217 0 23 Apr 2021
A Theoretical Analysis of Learning with Noisily Labeled Data Yi Tian Xu Qi Qian Hao Li Rong Jin NoLa 31 1 0 08 Apr 2021
Why Do Local Methods Solve Nonconvex Problems? Tengyu Ma 49 13 0 24 Mar 2021
Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$ Yegor Klochkov Nikita Zhivotovskiy 81 62 0 22 Mar 2021
Algorithmic Challenges in Ensuring Fairness at the Time of Decision Jad Salem Swati Gupta Vijay Kamble FaML 57 4 0 16 Mar 2021
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency Yuyang Deng M. Mahdavi 107 61 0 25 Feb 2021
Distributionally Robust Federated Averaging Yuyang Deng Mohammad Mahdi Kamani M. Mahdavi FedML 62 143 0 25 Feb 2021
Provable Super-Convergence with a Large Cyclical Learning Rate Samet Oymak 62 12 0 22 Feb 2021
Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes Steffen Dereich Sebastian Kassing 108 27 0 16 Feb 2021
Fast and accurate optimization on the orthogonal manifold without retraction Pierre Ablin Gabriel Peyré 115 30 0 15 Feb 2021
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers Kenji Kawaguchi PINN 64 42 0 15 Feb 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 112 160 0 14 Feb 2021
Stochastic Gradient Langevin Dynamics with Variance Reduction Zhishen Huang Stephen Becker 77 7 0 12 Feb 2021
Proximal and Federated Random Reshuffling Konstantin Mishchenko Ahmed Khaled Peter Richtárik FedML 77 32 0 12 Feb 2021
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry Ziyi Chen Yi Zhou Tengyu Xu Yingbin Liang 115 35 0 09 Feb 2021
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network Mo Zhou Rong Ge Chi Jin 145 46 0 04 Feb 2021
Stability and Generalization of the Decentralized Stochastic Gradient Descent Tao Sun Dongsheng Li Bao Wang 16 0 0 02 Feb 2021
On the Local Linear Rate of Consensus on the Stiefel Manifold Shixiang Chen Alfredo García Mingyi Hong Shahin Shahrampour 61 14 0 22 Jan 2021
Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem Matthew J. Colbrook Vegard Antun A. Hansen 114 136 0 20 Jan 2021
Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent Junyuan Hong Zhangyang Wang Jiayu Zhou 49 9 0 19 Jan 2021
Learning with Gradient Descent and Weakly Convex Losses Dominic Richards Michael G. Rabbat MLT 71 15 0 13 Jan 2021
CADA: Communication-Adaptive Distributed Adam Tianyi Chen Ziye Guo Yuejiao Sun W. Yin ODL 39 24 0 31 Dec 2020
Variance Reduction on General Adaptive Stochastic Mirror Descent Wenjie Li Zhanyu Wang Yichen Zhang Guang Cheng 61 4 0 26 Dec 2020
Noisy Linear Convergence of Stochastic Gradient Descent for CV@R Statistical Learning under Polyak-Łojasiewicz Conditions Dionysios S. Kalogerias 76 8 0 14 Dec 2020
Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation Aleksandra Burashnikova Marianne Clausel Charlotte Laclau Frack Iutzeller Yury Maximov Massih-Reza Amini 29 4 0 12 Dec 2020
Recent Theoretical Advances in Non-Convex Optimization Marina Danilova Pavel Dvurechensky Alexander Gasnikov Eduard A. Gorbunov Sergey Guminov Dmitry Kamzolov Innokentiy Shibaev 129 79 0 11 Dec 2020
A Study of Condition Numbers for First-Order Optimization Charles Guille-Escuret Baptiste Goujaud M. Girotti Ioannis Mitliagkas 80 20 0 10 Dec 2020
Characterization of Excess Risk for Locally Strongly Convex Population Risk Mingyang Yi Ruoyu Wang Zhi-Ming Ma 31 2 0 04 Dec 2020
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients Jun Li Yumeng Shao Ming Ding Chuan Ma Kang Wei Zhu Han H. Vincent Poor 46 9 0 02 Dec 2020
Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization G. Fort Eric Moulines Hoi-To Wai TPM 44 6 0 24 Nov 2020
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization Abolfazl Hashemi Anish Acharya Rudrajit Das H. Vikalo Sujay Sanghavi Inderjit Dhillon 72 9 0 20 Nov 2020
Convergence Analysis of Homotopy-SGD for non-convex optimization Matilde Gargiani Andrea Zanelli Quoc Tran-Dinh Moritz Diehl Frank Hutter 44 3 0 20 Nov 2020
Towards Optimal Problem Dependent Generalization Error Bounds in Statistical Learning Theory Yunbei Xu A. Zeevi 116 17 0 12 Nov 2020
A fast randomized incremental gradient method for decentralized non-convex optimization Ran Xin U. Khan S. Kar 70 33 0 07 Nov 2020
LQR with Tracking: A Zeroth-order Approach and Its Global Convergence Tongzheng Ren Aoxiao Zhong Na Li 42 3 0 03 Nov 2020
Efficient constrained sampling via the mirror-Langevin algorithm Kwangjun Ahn Sinho Chewi 104 57 0 30 Oct 2020
Finite-Time Convergence Rates of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning Sihan Zeng Thinh T. Doan Justin Romberg 68 13 0 28 Oct 2020
Optimal Client Sampling for Federated Learning Jiajun He Samuel Horváth Peter Richtárik FedML 89 201 0 26 Oct 2020
Decentralized Deep Learning using Momentum-Accelerated Consensus Aditya Balu Zhanhong Jiang Sin Yong Tan Chinmay Hedge Young M. Lee Soumik Sarkar FedML 91 22 0 21 Oct 2020
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer Chen Zhu Zheng Xu Ali Shafahi Manli Shu Amin Ghiasi Tom Goldstein MQ 53 3 0 14 Oct 2020
AEGD: Adaptive Gradient Descent with Energy Hailiang Liu Xuping Tian ODL 55 11 0 10 Oct 2020
WeMix: How to Better Utilize Data Augmentation Yi Tian Xu Asaf Noy Ming Lin Qi Qian Hao Li Rong Jin 82 16 0 03 Oct 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark Schmidt Francis R. Bach Peter Richtárik 100 117 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 84 4 0 02 Oct 2020
Linear Convergence of Generalized Mirror Descent with Time-Dependent Mirrors Adityanarayanan Radhakrishnan M. Belkin Caroline Uhler 39 9 0 18 Sep 2020
Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality Rui Gao 72 94 0 09 Sep 2020