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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

Title
Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering Rui Zhu Di Tang Siyuan Tang Guanhong Tao Shiqing Ma Xiaofeng Wang Haixu Tang DD 59 4 0 29 Jan 2023
Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent Sara Venturini Andrea Cristofari Francesco Rinaldi Francesco Tudisco 58 2 0 28 Jan 2023
Theoretical Analysis of Offline Imitation With Supplementary Dataset Ziniu Li Tian Xu Y. Yu Zhixun Luo OffRL 60 2 0 27 Jan 2023
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing Jikai Jin Zhiyuan Li Kaifeng Lyu S. Du Jason D. Lee MLT 103 37 0 27 Jan 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 97 2 0 23 Jan 2023
Convergence beyond the over-parameterized regime using Rayleigh quotients David A. R. Robin Kevin Scaman Marc Lelarge 60 3 0 19 Jan 2023
Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces Juan Esteban Suarez Cardona Phil-Alexander Hofmann Michael Hecht 93 3 0 12 Jan 2023
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation Xiao-Tong Yuan P. Li 87 2 0 09 Jan 2023
Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods Ben Adcock Matthew J. Colbrook Maksym Neyra-Nesterenko 64 2 0 05 Jan 2023
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis Le‐Yu Chen Jing Xu J.N. Zhang 106 14 0 02 Jan 2023
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization G. Fort Eric Moulines 84 6 0 02 Jan 2023
Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization Taoli Zheng Lingling Zhu Anthony Man-Cho So Jose H. Blanchet Jiajin Li 119 15 0 26 Dec 2022
Iterative regularization in classification via hinge loss diagonal descent Vassilis Apidopoulos T. Poggio Lorenzo Rosasco S. Villa 56 2 0 24 Dec 2022
Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis Quoc Tran-Dinh Marten van Dijk 53 0 0 19 Dec 2022
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization Xu Cai Chaobing Song Stephen J. Wright Jelena Diakonikolas 73 14 0 09 Dec 2022
Generalized Gradient Flows with Provable Fixed-Time Convergence and Fast Evasion of Non-Degenerate Saddle Points Mayank Baranwal Param Budhraja V. Raj A. Hota 58 3 0 07 Dec 2022
Scalable Hierarchical Over-the-Air Federated Learning Seyed Mohammad Azimi-Abarghouyi Viktoria Fodor 45 11 0 29 Nov 2022
Zeroth-Order Alternating Gradient Descent Ascent Algorithms for a Class of Nonconvex-Nonconcave Minimax Problems Zi Xu Ziqi Wang Junlin Wang Y. Dai 99 11 0 24 Nov 2022
Adaptive Federated Minimax Optimization with Lower Complexities Feihu Huang Xinrui Wang Junyi Li Songcan Chen FedML 107 5 0 14 Nov 2022
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms Thomas Guilmeau Émilie Chouzenoux Victor Elvira 75 3 0 09 Nov 2022
Neural PDE Solvers for Irregular Domains Biswajit Khara Ethan Herron Zhanhong Jiang Aditya Balu Chih-Hsuan Yang ... Anushrut Jignasu Soumik Sarkar Chinmay Hegde A. Krishnamurthy Baskar Ganapathysubramanian AI4CE 50 9 0 07 Nov 2022
Convergence Rates of Stochastic Zeroth-order Gradient Descent for Ł ojasiewicz Functions Tianyu Wang Yasong Feng 32 1 0 31 Oct 2022
Optimization for Amortized Inverse Problems Tianci Liu Tong Yang Quan Zhang Qi Lei 84 6 0 25 Oct 2022
Adaptive Top-K in SGD for Communication-Efficient Distributed Learning Mengzhe Ruan Guangfeng Yan Yuanzhang Xiao Linqi Song Weitao Xu 69 3 0 24 Oct 2022
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization Kun Yuan Xinmeng Huang Yiming Chen Xiaohan Zhang Yingya Zhang Pan Pan 70 21 0 14 Oct 2022
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent Satyen Kale Jason D. Lee Chris De Sa Ayush Sekhari Karthik Sridharan 42 4 0 13 Oct 2022
SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization Hanseul Cho Chulhee Yun 60 9 0 12 Oct 2022
Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies Bin Hu Kai Zhang Na Li M. Mesbahi Maryam Fazel Tamer Bacsar 161 27 0 10 Oct 2022
On skip connections and normalisation layers in deep optimisation L. MacDonald Jack Valmadre Hemanth Saratchandran Simon Lucey ODL 63 2 0 10 Oct 2022
Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces Amirali Aghazadeh Nived Rajaraman Tony Tu Kannan Ramchandran 65 2 0 05 Oct 2022
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations Jialing Liao Zheng Chen Erik G. Larsson 89 13 0 05 Oct 2022
$SAGDA: Achieving $\mathcal{O}(ε^{-2})$ Communication Complexity in Federated Min-Max Learning$ SAGDA: Achieving $\mathcal{O}(ε^{-2})$ Communication Complexity in Federated Min-Max Learning Haibo Yang Zhuqing Liu Xin Zhang Jia-Wei Liu FedML 94 0 0 02 Oct 2022
Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition Jianhao Ma Li-Zhen Guo Salar Fattahi 73 4 0 01 Oct 2022
Restricted Strong Convexity of Deep Learning Models with Smooth Activations A. Banerjee Pedro Cisneros-Velarde Libin Zhu M. Belkin 70 8 0 29 Sep 2022
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability Peisong Wen Qianqian Xu Zhiyong Yang Yuan He Qingming Huang 128 10 0 27 Sep 2022
Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient and their monotonic transformations Daiki Morinaga Kazuto Fukuchi Jun Sakuma Youhei Akimoto 14 4 0 26 Sep 2022
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach Mao Ye B. Liu S. Wright Peter Stone Qian Liu 118 87 0 19 Sep 2022
Efficiency Ordering of Stochastic Gradient Descent Jie Hu Vishwaraj Doshi Do Young Eun 70 7 0 15 Sep 2022
Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses Andrew Lowy Meisam Razaviyayn 86 13 0 15 Sep 2022
Statistical Learning Theory for Control: A Finite Sample Perspective Anastasios Tsiamis Ingvar M. Ziemann Nikolai Matni George J. Pappas 227 77 0 12 Sep 2022
Optimizing the Performative Risk under Weak Convexity Assumptions Yulai Zhao 65 5 0 02 Sep 2022
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning Kazusato Oko Shunta Akiyama Tomoya Murata Taiji Suzuki FedML 72 0 0 01 Sep 2022
Asynchronous Training Schemes in Distributed Learning with Time Delay Haoxiang Wang Zhanhong Jiang Chao Liu Soumik Sarkar D. Jiang Young M. Lee 46 2 0 28 Aug 2022
Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization Zhize Li Jian Li 96 6 0 22 Aug 2022
Adaptive Learning Rates for Faster Stochastic Gradient Methods Samuel Horváth Konstantin Mishchenko Peter Richtárik ODL 56 9 0 10 Aug 2022
Improved Policy Optimization for Online Imitation Learning J. Lavington Sharan Vaswani Mark Schmidt OffRL 79 6 0 29 Jul 2022
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity Analysis Tao Li Haozhe Lei Quanyan Zhu AAML 98 7 0 29 Jul 2022
Fixed-Time Convergence for a Class of Nonconvex-Nonconcave Min-Max Problems Kunal Garg Mayank Baranwal 13 1 0 26 Jul 2022
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization Wei Jiang Gang Li Yibo Wang Lijun Zhang Tianbao Yang 113 18 0 18 Jul 2022
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis Taha Belkhouja Yan Yan J. Doppa OOD AI4TS 61 9 0 09 Jul 2022