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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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Title
Generalization Bounded Implicit Learning of Nearly Discontinuous Functions Bibit Bianchini Mathew Halm Nikolai Matni Michael Posa 83 12 0 13 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 105 7 0 13 Dec 2021
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity Junchi Yang Antonio Orvieto Aurelien Lucchi Niao He 109 64 0 10 Dec 2021
Extending AdamW by Leveraging Its Second Moment and Magnitude Guoqiang Zhang Niwa Kenta W. Kleijn 53 3 0 09 Dec 2021
Breaking the Convergence Barrier: Optimization via Fixed-Time Convergent Flows Param Budhraja Mayank Baranwal Kunal Garg A. Hota 53 9 0 02 Dec 2021
Improved Fine-Tuning by Better Leveraging Pre-Training Data Ziquan Liu Yi Tian Xu Yuanhong Xu Qi Qian Hao Li Xiangyang Ji Antoni B. Chan Rong Jin 55 42 0 24 Nov 2021
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning Zhenhuan Yang Yunwen Lei Puyu Wang Tianbao Yang Yiming Ying 70 26 0 23 Nov 2021
Linear Speedup in Personalized Collaborative Learning El Mahdi Chayti Sai Praneeth Karimireddy Sebastian U. Stich Nicolas Flammarion Martin Jaggi FedML 68 13 0 10 Nov 2021
Learning Rates for Nonconvex Pairwise Learning Shaojie Li Yong Liu 87 2 0 09 Nov 2021
Finding the Optimal Dynamic Treatment Regime Using Smooth Fisher Consistent Surrogate Loss Nilanjana Laha Aaron Sonabend-W Rajarshi Mukherjee Tianxi Cai 24 2 0 03 Nov 2021
Distributed Principal Component Analysis with Limited Communication Foivos Alimisis Peter Davies Bart Vandereycken Dan Alistarh 75 12 0 27 Oct 2021
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent Sharan Vaswani Benjamin Dubois-Taine Reza Babanezhad 98 13 0 21 Oct 2021
Stochastic Learning Rate Optimization in the Stochastic Approximation and Online Learning Settings Theodoros Mamalis D. Stipanović P. Voulgaris 25 4 0 20 Oct 2021
A Unified and Refined Convergence Analysis for Non-Convex Decentralized Learning Sulaiman A. Alghunaim Kun Yuan 85 63 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 102 16 0 11 Oct 2021
Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain Dmitrii Ostrovskii Babak Barazandeh Meisam Razaviyayn 83 12 0 08 Oct 2021
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou Anthony Man-Cho So James Cheng 63 1 0 30 Sep 2021
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning Sihan Zeng Thinh T. Doan Justin Romberg 163 26 0 29 Sep 2021
DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning Javier Yu Joseph A. Vincent Mac Schwager 136 38 0 17 Sep 2021
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data Antoine Godichon-Baggioni Nicklas Werge Olivier Wintenberger 101 7 0 15 Sep 2021
Bundled Gradients through Contact via Randomized Smoothing H.J. Terry Suh Tao Pang Russ Tedrake 157 54 0 11 Sep 2021
Iterated Vector Fields and Conservatism, with Applications to Federated Learning Zachary B. Charles Keith Rush 72 6 0 08 Sep 2021
Dash: Semi-Supervised Learning with Dynamic Thresholding Yi Tian Xu Lei Shang Jinxing Ye Qi Qian Yu-Feng Li Baigui Sun Hao Li Rong Jin 120 225 0 01 Sep 2021
Adaptive shot allocation for fast convergence in variational quantum algorithms Andi Gu Angus Lowe Pavel A. Dub Patrick J. Coles A. Arrasmith 67 22 0 23 Aug 2021
Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation Simon Eberle Arnulf Jentzen Adrian Riekert G. Weiss 68 12 0 18 Aug 2021
A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions Arnulf Jentzen Adrian Riekert 69 13 0 10 Aug 2021
Convergence of gradient descent for learning linear neural networks Gabin Maxime Nguegnang Holger Rauhut Ulrich Terstiege MLT 54 18 0 04 Aug 2021
Exact Pareto Optimal Search for Multi-Task Learning and Multi-Criteria Decision-Making Debabrata Mahapatra Vaibhav Rajan 59 2 0 02 Aug 2021
Faster Rates of Private Stochastic Convex Optimization Jinyan Su Lijie Hu Di Wang 70 13 0 31 Jul 2021
SGD with a Constant Large Learning Rate Can Converge to Local Maxima Liu Ziyin Botao Li James B. Simon Masakuni Ueda 75 9 0 25 Jul 2021
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms Shuyu Cheng Guoqiang Wu Jun Zhu 42 17 0 21 Jul 2021
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints Shaojie Li Yong Liu 103 13 0 19 Jul 2021
Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping Ilja Kuzborskij Csaba Szepesvári 89 7 0 12 Jul 2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity Nicolas Loizou Hugo Berard Gauthier Gidel Ioannis Mitliagkas Simon Lacoste-Julien 94 54 0 30 Jun 2021
Faithful Edge Federated Learning: Scalability and Privacy Meng Zhang Ermin Wei R. Berry FedML 79 45 0 30 Jun 2021
Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets Baojian Zhou Yifan Sun 63 1 0 29 Jun 2021
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent Spencer Frei Quanquan Gu 89 26 0 25 Jun 2021
Who Leads and Who Follows in Strategic Classification? Tijana Zrnic Eric Mazumdar S. Shankar Sastry Michael I. Jordan 82 55 0 23 Jun 2021
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks Alexandra Peste Eugenia Iofinova Adrian Vladu Dan Alistarh AI4CE 415 72 0 23 Jun 2021
Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes Jai Moondra Hassan Mortagy Swati Gupta 87 4 0 22 Jun 2021
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent Anish Acharya Abolfazl Hashemi Prateek Jain Sujay Sanghavi Inderjit S. Dhillon Ufuk Topcu 59 33 0 16 Jun 2021
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent Jason M. Altschuler Sinho Chewi P. Gerber Austin J. Stromme 84 37 0 16 Jun 2021
Communication-efficient SGD: From Local SGD to One-Shot Averaging Artin Spiridonoff Alexander Olshevsky I. Paschalidis FedML 95 20 0 09 Jun 2021
On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction Jiawei Huang Nan Jiang 91 5 0 02 Jun 2021
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation M. Belkin 68 186 0 29 May 2021
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems Jiahao Xie Chao Zhang Zebang Shen Weijie Liu Hui Qian FedML 46 1 0 29 May 2021
SG-PALM: a Fast Physically Interpretable Tensor Graphical Model Yu Wang Alfred Hero 57 4 0 26 May 2021
Saddle Point Optimization with Approximate Minimization Oracle and its Application to Robust Berthing Control Youhei Akimoto Yoshiki Miyauchi A. Maki 90 16 0 25 May 2021
Leveraging Non-uniformity in First-order Non-convex Optimization Jincheng Mei Yue Gao Bo Dai Csaba Szepesvári Dale Schuurmans 90 50 0 13 May 2021
Why Does Multi-Epoch Training Help? Yi Tian Xu Qi Qian Hao Li Rong Jin 57 1 0 13 May 2021