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 / 167 papers shown

Title
Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent Sara Venturini Andrea Cristofari Francesco Rinaldi Francesco Tudisco 20 2 0 28 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 46 34 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 27 2 0 23 Jan 2023
Convergence beyond the over-parameterized regime using Rayleigh quotients David A. R. Robin Kevin Scaman Marc Lelarge 17 3 0 19 Jan 2023
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation Xiao-Tong Yuan P. Li 32 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 27 2 0 05 Jan 2023
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization G. Fort Eric Moulines 44 6 0 02 Jan 2023
Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis Quoc Tran-Dinh Marten van Dijk 26 0 0 19 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 23 2 0 07 Dec 2022
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms Thomas Guilmeau Émilie Chouzenoux Victor Elvira 29 3 0 09 Nov 2022
Optimization for Amortized Inverse Problems Tianci Liu Tong Yang Quan Zhang Qi Lei 26 4 0 25 Oct 2022
Adaptive Top-K in SGD for Communication-Efficient Distributed Learning Mengzhe Ruan Guangfeng Yan Yuanzhang Xiao Linqi Song Weitao Xu 24 3 0 24 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 21 4 0 13 Oct 2022
Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces Amirali Aghazadeh Nived Rajaraman Tony Tu Kannan Ramchandran 17 2 0 05 Oct 2022
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations Jialing Liao Zheng Chen Erik G. Larsson 13 12 0 05 Oct 2022
Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition Jianhao Ma Li-Zhen Guo S. Fattahi 34 4 0 01 Oct 2022
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability Peisong Wen Qianqian Xu Zhiyong Yang Yuan He Qingming Huang 48 10 0 27 Sep 2022
Efficiency Ordering of Stochastic Gradient Descent Jie Hu Vishwaraj Doshi Do Young Eun 28 6 0 15 Sep 2022
Statistical Learning Theory for Control: A Finite Sample Perspective Anastasios Tsiamis Ingvar M. Ziemann Nikolai Matni George J. Pappas 23 73 0 12 Sep 2022
Improved Policy Optimization for Online Imitation Learning J. Lavington Sharan Vaswani Mark W. Schmidt OffRL 13 6 0 29 Jul 2022
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity Analysis Tao Li Haozhe Lei Quanyan Zhu AAML 19 7 0 29 Jul 2022
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization Wei Jiang Gang Li Yibo Wang Lijun Zhang Tianbao Yang 27 16 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 19 9 0 09 Jul 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 19 20 0 22 Jun 2022
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction Kaifeng Lyu Zhiyuan Li Sanjeev Arora FAtt 35 69 0 14 Jun 2022
Towards Understanding Sharpness-Aware Minimization Maksym Andriushchenko Nicolas Flammarion AAML 24 133 0 13 Jun 2022
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Lam M. Nguyen Trang H. Tran 32 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 28 50 0 08 Jun 2022
Learning from time-dependent streaming data with online stochastic algorithms Antoine Godichon-Baggioni Nicklas Werge Olivier Wintenberger 22 3 0 25 May 2022
Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD Konstantinos E. Nikolakakis Farzin Haddadpour Amin Karbasi Dionysios S. Kalogerias 31 17 0 26 Apr 2022
Sharper Utility Bounds for Differentially Private Models Yilin Kang Yong Liu Jian Li Weiping Wang FedML 19 3 0 22 Apr 2022
Convergence of gradient descent for deep neural networks S. Chatterjee ODL 19 20 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 20 2 0 21 Mar 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Pranay Sharma Rohan Panda Gauri Joshi P. Varshney FedML 19 46 0 09 Mar 2022
Tackling benign nonconvexity with smoothing and stochastic gradients Harsh Vardhan Sebastian U. Stich 18 8 0 18 Feb 2022
Delay-adaptive step-sizes for asynchronous learning Xuyang Wu Sindri Magnússon Hamid Reza Feyzmahdavian M. Johansson 23 14 0 17 Feb 2022
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization Wei Jiang Bokun Wang Yibo Wang Lijun Zhang Tianbao Yang 74 17 0 15 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 35 60 0 10 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 33 14 0 01 Feb 2022
Differentially Private SGDA for Minimax Problems Zhenhuan Yang Shu Hu Yunwen Lei Kush R. Varshney Siwei Lyu Yiming Ying 31 19 0 22 Jan 2022
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems Thinh T. Doan 18 15 0 17 Dec 2021
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions Martin Hutzenthaler Arnulf Jentzen Katharina Pohl Adrian Riekert Luca Scarpa MLT 32 6 0 13 Dec 2021
Breaking the Convergence Barrier: Optimization via Fixed-Time Convergent Flows Param Budhraja Mayank Baranwal Kunal Garg A. Hota 13 9 0 02 Dec 2021
Linear Speedup in Personalized Collaborative Learning El Mahdi Chayti Sai Praneeth Karimireddy Sebastian U. Stich Nicolas Flammarion Martin Jaggi FedML 13 13 0 10 Nov 2021
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent Sharan Vaswani Benjamin Dubois-Taine Reza Babanezhad 43 11 0 21 Oct 2021
A Unified and Refined Convergence Analysis for Non-Convex Decentralized Learning Sulaiman A. Alghunaim Kun Yuan 17 57 0 19 Oct 2021
A global convergence theory for deep ReLU implicit networks via over-parameterization Tianxiang Gao Hailiang Liu Jia Liu Hridesh Rajan Hongyang Gao MLT 23 16 0 11 Oct 2021
DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning Javier Yu Joseph A. Vincent Mac Schwager 38 35 0 17 Sep 2021
Bundled Gradients through Contact via Randomized Smoothing H. Suh Tao Pang Russ Tedrake 78 52 0 11 Sep 2021
Iterated Vector Fields and Conservatism, with Applications to Federated Learning Zachary B. Charles Keith Rush 19 6 0 08 Sep 2021