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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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On Communication Compression for Distributed Optimization on Heterogeneous Data Sebastian U. Stich 93 23 0 04 Sep 2020
Extensions to the Proximal Distance Method of Constrained Optimization Alfonso Landeros Oscar Hernan Madrid Padilla Hua Zhou K. Lange 58 9 0 02 Sep 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 77 0 0 26 Aug 2020
Adaptive Hierarchical Hyper-gradient Descent Renlong Jie Junbin Gao A. Vasnev Minh-Ngoc Tran 54 5 0 17 Aug 2020
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time Weichen Wang Jiequn Han Zhuoran Yang Zhaoran Wang 90 29 0 16 Aug 2020
FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching Farzin Haddadpour Belhal Karimi Ping Li Xiaoyun Li FedML 80 32 0 11 Aug 2020
An improved convergence analysis for decentralized online stochastic non-convex optimization Ran Xin U. Khan S. Kar 112 104 0 10 Aug 2020
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning Sai Praneeth Karimireddy Martin Jaggi Satyen Kale M. Mohri Sashank J. Reddi Sebastian U. Stich A. Suresh FedML 165 217 0 08 Aug 2020
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression Jun Qi Jun Du Sabato Marco Siniscalchi Xiaoli Ma Chin-Hui Lee 105 42 0 04 Aug 2020
On the Convergence of SGD with Biased Gradients Ahmad Ajalloeian Sebastian U. Stich 78 90 0 31 Jul 2020
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks Jun Shu Yanwen Zhu Qian Zhao Zongben Xu Deyu Meng 66 7 0 29 Jul 2020
AdaScale SGD: A User-Friendly Algorithm for Distributed Training Tyler B. Johnson Pulkit Agrawal Haijie Gu Carlos Guestrin ODL 87 37 0 09 Jul 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou Hugo Berard Alexia Jolicoeur-Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas 59 50 0 08 Jul 2020
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle Shaocong Ma Yi Zhou 39 3 0 07 Jul 2020
Variance reduction for Riemannian non-convex optimization with batch size adaptation Andi Han Junbin Gao 85 5 0 03 Jul 2020
Tilted Empirical Risk Minimization Tian Li Ahmad Beirami Maziar Sanjabi Virginia Smith 89 135 0 02 Jul 2020
Federated Learning with Compression: Unified Analysis and Sharp Guarantees Farzin Haddadpour Mohammad Mahdi Kamani Aryan Mokhtari M. Mahdavi FedML 101 280 0 02 Jul 2020
DeltaGrad: Rapid retraining of machine learning models Yinjun Wu Yan Sun S. Davidson MU 72 202 0 26 Jun 2020
Randomized Block-Diagonal Preconditioning for Parallel Learning Celestine Mendler-Dünner Aurelien Lucchi 12 1 0 24 Jun 2020
Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds Yingxue Zhou Xiangyi Chen Mingyi Hong Zhiwei Steven Wu A. Banerjee 96 25 0 24 Jun 2020
Towards Understanding Label Smoothing Yi Tian Xu Yuanhong Xu Qi Qian Hao Li Rong Jin UQCV 55 42 0 20 Jun 2020
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning Samuel Horváth Peter Richtárik 79 60 0 19 Jun 2020
Exploring Weight Importance and Hessian Bias in Model Pruning Mingchen Li Yahya Sattar Christos Thrampoulidis Samet Oymak 71 4 0 19 Jun 2020
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation Robert Mansel Gower Othmane Sebbouh Nicolas Loizou 120 76 0 18 Jun 2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent Anna Korba Adil Salim Michael Arbel Giulia Luise Arthur Gretton 81 78 0 17 Jun 2020
Linear Last-iterate Convergence in Constrained Saddle-point Optimization Chen-Yu Wei Chung-Wei Lee Mengxiao Zhang Haipeng Luo 134 11 0 16 Jun 2020
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization Hassan Mortagy Swati Gupta Sebastian Pokutta 54 7 0 15 Jun 2020
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias Lu Yu Krishnakumar Balasubramanian S. Volgushev Murat A. Erdogdu 99 52 0 14 Jun 2020
A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization Zhize Li Peter Richtárik FedML 93 36 0 12 Jun 2020
SGD with shuffling: optimal rates without component convexity and large epoch requirements Kwangjun Ahn Chulhee Yun S. Sra 68 67 0 12 Jun 2020
STL-SGD: Speeding Up Local SGD with Stagewise Communication Period Shuheng Shen Yifei Cheng Jingchang Liu Linli Xu LRM 70 7 0 11 Jun 2020
Asymptotic Analysis of Conditioned Stochastic Gradient Descent Rémi Leluc Franccois Portier 75 4 0 04 Jun 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet 100 70 0 03 Jun 2020
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks Itay Safran Gilad Yehudai Ohad Shamir 146 35 0 01 Jun 2020
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness Murat A. Erdogdu Rasa Hosseinzadeh 88 77 0 27 May 2020
Subgradient Regularized Multivariate Convex Regression at Scale Wenyu Chen Rahul Mazumder 45 8 0 23 May 2020
Exponential ergodicity of mirror-Langevin diffusions Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet Austin J. Stromme 69 51 0 19 May 2020
Detached Error Feedback for Distributed SGD with Random Sparsification An Xu Heng-Chiao Huang 71 9 0 11 Apr 2020
Convergence rates and approximation results for SGD and its continuous-time counterpart Xavier Fontaine Valentin De Bortoli Alain Durmus 14 7 0 08 Apr 2020
Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions V. Patel 74 23 0 01 Apr 2020
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness Thinh T. Doan Lam M. Nguyen Nhan H. Pham Justin Romberg 75 22 0 24 Mar 2020
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method Babak Barazandeh Meisam Razaviyayn 46 24 0 18 Mar 2020
The Implicit Regularization of Stochastic Gradient Flow for Least Squares Alnur Ali Yan Sun Robert Tibshirani 94 77 0 17 Mar 2020
Boosting Frank-Wolfe by Chasing Gradients Cyrille W. Combettes Sebastian Pokutta 76 29 0 13 Mar 2020
Machine Learning on Volatile Instances Xiaoxi Zhang Jianyu Wang Gauri Joshi Carlee Joe-Wong 54 25 0 12 Mar 2020
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization Sudeep Salgia Qing Zhao Sattar Vakili 77 2 0 11 Mar 2020
Communication-efficient Variance-reduced Stochastic Gradient Descent H. S. Ghadikolaei Sindri Magnússon 52 3 0 10 Mar 2020
Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives Zhishuai Guo Yan Yan Tianbao Yang MoMe 63 4 0 09 Mar 2020
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS Yong Liu Lizhong Ding Weiping Wang 18 0 0 09 Mar 2020
Approximate Cross-validation: Guarantees for Model Assessment and Selection Ashia Wilson Maximilian Kasy Lester W. Mackey 67 23 0 02 Mar 2020