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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"

50 / 588 papers shown

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A Theoretical Analysis of the Learning Dynamics under Class Imbalance Emanuele Francazi Marco Baity-Jesi Aurelien Lucchi 81 18 0 01 Jul 2022
On the sample complexity of entropic optimal transport Philippe Rigollet Austin J. Stromme OT 82 43 0 27 Jun 2022
Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Łojasiewicz Functions when the Non-Convexity is Averaged-Out Jun-Kun Wang Chi-Heng Lin Andre Wibisono Bin Hu 84 22 0 22 Jun 2022
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator L. Danon Dan Garber 51 4 0 19 Jun 2022
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction Kaifeng Lyu Zhiyuan Li Sanjeev Arora FAtt 121 75 0 14 Jun 2022
A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization Dounia Lakhmiri D. Orban Andrea Lodi 27 0 0 14 Jun 2022
Towards Understanding Sharpness-Aware Minimization Maksym Andriushchenko Nicolas Flammarion AAML 106 142 0 13 Jun 2022
Anchor Sampling for Federated Learning with Partial Client Participation Feijie Wu Song Guo Zhihao Qu Shiqi He Ziming Liu Jing Gao FedML 76 14 0 13 Jun 2022
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Lam M. Nguyen Trang H. Tran 63 2 0 13 Jun 2022
Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression Jun Qi Chao-Han Huck Yang Pin-Yu Chen Min-hsiu Hsieh 96 54 0 08 Jun 2022
Rotation to Sparse Loadings using $L^p$ Losses and Related Inference Problems Xinyi Liu Gabriel Wallin Yunxiao Chen I. Moustaki 38 3 0 05 Jun 2022
Federated Learning with a Sampling Algorithm under Isoperimetry Lukang Sun Adil Salim Peter Richtárik FedML 88 7 0 02 Jun 2022
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top Eduard A. Gorbunov Samuel Horváth Peter Richtárik Gauthier Gidel AAML 38 0 0 01 Jun 2022
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games Sihan Zeng Thinh T. Doan Justin Romberg 143 22 0 27 May 2022
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function Saeed Masiha Saber Salehkaleybar Niao He Negar Kiyavash Patrick Thiran 127 18 0 25 May 2022
Learning from time-dependent streaming data with online stochastic algorithms Antoine Godichon-Baggioni Nicklas Werge Olivier Wintenberger 96 3 0 25 May 2022
Differentially private Riemannian optimization Andi Han Bamdev Mishra Pratik Jawanpuria Junbin Gao 64 10 0 19 May 2022
Policy Gradient Method For Robust Reinforcement Learning Yue Wang Shaofeng Zou 126 77 0 15 May 2022
A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization H. Tanabe E. H. Fukuda N. Yamashita 19 8 0 11 May 2022
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization Laurent Condat Kai Yi Peter Richtárik 105 21 0 09 May 2022
Network Gradient Descent Algorithm for Decentralized Federated Learning Shuyuan Wu Danyang Huang Hansheng Wang FedML 64 11 0 06 May 2022
Implicit Regularization Properties of Variance Reduced Stochastic Mirror Descent Yiling Luo X. Huo Y. Mei 44 1 0 29 Apr 2022
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD Konstantinos E. Nikolakakis Farzin Haddadpour Amin Karbasi Dionysios S. Kalogerias 128 19 0 26 Apr 2022
Riemannian Hamiltonian methods for min-max optimization on manifolds Andi Han Bamdev Mishra Pratik Jawanpuria Pawan Kumar Junbin Gao 80 17 0 25 Apr 2022
Sharper Utility Bounds for Differentially Private Models Yilin Kang Yong Liu Jian Li Weiping Wang FedML 83 3 0 22 Apr 2022
A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization Ziyi Chen B. Kailkhura Yi Zhou 84 9 0 30 Mar 2022
Convergence of gradient descent for deep neural networks S. Chatterjee ODL 63 22 0 30 Mar 2022
A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization With Non-isolated Local Minima Tae-Eon Ko Xiantao Li 55 2 0 21 Mar 2022
Learning Distributionally Robust Models at Scale via Composite Optimization Farzin Haddadpour Mohammad Mahdi Kamani M. Mahdavi Amin Karbasi OOD 69 5 0 17 Mar 2022
Private Non-Convex Federated Learning Without a Trusted Server Andrew Lowy Ali Ghafelebashi Meisam Razaviyayn FedML 99 27 0 13 Mar 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Pranay Sharma Rohan Panda Gauri Joshi P. Varshney FedML 110 49 0 09 Mar 2022
Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate Ziye Ma Somayeh Sojoudi 65 7 0 08 Mar 2022
Whiplash Gradient Descent Dynamics Subhransu S. Bhattacharjee I. Petersen 24 0 0 04 Mar 2022
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation Jing Dong Li Shen Ying Xu Baoxiang Wang 80 1 0 28 Feb 2022
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract) Aleksandra Burashnikova Yury Maximov Marianne Clausel Charlotte Laclau F. Iutzeler Massih-Reza Amini 22 0 0 26 Feb 2022
From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality Fusheng Liu Haizhao Yang Soufiane Hayou Qianxiao Li AI4CE 51 2 0 22 Feb 2022
Tackling benign nonconvexity with smoothing and stochastic gradients Harsh Vardhan Sebastian U. Stich 88 8 0 18 Feb 2022
Delay-adaptive step-sizes for asynchronous learning Xuyang Wu Sindri Magnússon Hamid Reza Feyzmahdavian M. Johansson 67 14 0 17 Feb 2022
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization Wei Jiang Bokun Wang Yibo Wang Lijun Zhang Tianbao Yang 149 18 0 15 Feb 2022
Improved analysis for a proximal algorithm for sampling Yongxin Chen Sinho Chewi Adil Salim Andre Wibisono 100 58 0 13 Feb 2022
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo Krishnakumar Balasubramanian Sinho Chewi Murat A. Erdogdu Adil Salim Matthew Shunshi Zhang 105 65 0 10 Feb 2022
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity Dan Garber Ron Fisher 53 1 0 08 Feb 2022
Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution Lam M. Nguyen Trang H. Tran Marten van Dijk 91 3 0 07 Feb 2022
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation Matilde Gargiani Andrea Zanelli Andrea Martinelli Tyler H. Summers John Lygeros 71 14 0 01 Feb 2022
DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity Chung-Yiu Yau Hoi-To Wai 115 5 0 01 Feb 2022
Differentially Private SGDA for Minimax Problems Zhenhuan Yang Shu Hu Yunwen Lei Kush R. Varshney Siwei Lyu Yiming Ying 68 21 0 22 Jan 2022
On generalization bounds for deep networks based on loss surface implicit regularization Masaaki Imaizumi Johannes Schmidt-Hieber ODL 68 3 0 12 Jan 2022
Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size Adityanarayanan Radhakrishnan M. Belkin Caroline Uhler ODL 30 0 0 30 Dec 2021
DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning Ismat Jarin Birhanu Eshete AAML 64 10 0 24 Dec 2021
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems Thinh T. Doan 90 16 0 17 Dec 2021