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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
A First Order Meta Stackelberg Method for Robust Federated Learning Yunian Pan Tao Li Henger Li Tianyi Xu Zizhan Zheng Quanyan Zhu FedML 88 10 0 23 Jun 2023
Distributed Random Reshuffling Methods with Improved Convergence Kun-Yen Huang Linli Zhou Shi Pu 129 4 0 21 Jun 2023
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths Charles Guille-Escuret Hiroki Naganuma Kilian Fatras Ioannis Mitliagkas 73 4 0 20 Jun 2023
Convergence and concentration properties of constant step-size SGD through Markov chains Ibrahim Merad Stéphane Gaïffas 68 5 0 20 Jun 2023
Gradient is All You Need? Konstantin Riedl T. Klock Carina Geldhauser M. Fornasier 52 8 0 16 Jun 2023
Robustly Learning a Single Neuron via Sharpness Puqian Wang Nikos Zarifis Ilias Diakonikolas Jelena Diakonikolas 67 9 0 13 Jun 2023
Learning Unnormalized Statistical Models via Compositional Optimization Wei Jiang Jiayu Qin Lingyu Wu Changyou Chen Tianbao Yang Lijun Zhang 98 4 0 13 Jun 2023
Achieving Consensus over Compact Submanifolds Jiang Hu Jiaojiao Zhang Kangkang Deng 62 4 0 07 Jun 2023
Minimum intrinsic dimension scaling for entropic optimal transport Austin J. Stromme 47 10 0 06 Jun 2023
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems Chaoyue Liu Dmitriy Drusvyatskiy M. Belkin Damek Davis Yi-An Ma ODL 77 18 0 05 Jun 2023
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking Frederik Kunstner V. S. Portella Mark Schmidt Nick Harvey 77 10 0 05 Jun 2023
A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition Quan-Wu Xiao Songtao Lu Tianyi Chen 78 2 0 04 Jun 2023
Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm A. Akhavan Evgenii Chzhen Massimiliano Pontil Alexandre B. Tsybakov 46 11 0 03 Jun 2023
Solving Robust MDPs through No-Regret Dynamics E. Guha 53 0 0 30 May 2023
Knowledge Distillation Performs Partial Variance Reduction M. Safaryan Alexandra Peste Dan Alistarh 82 7 0 27 May 2023
Fine-Tuning Language Models with Just Forward Passes Sadhika Malladi Tianyu Gao Eshaan Nichani Alexandru Damian Jason D. Lee Danqi Chen Sanjeev Arora 134 205 0 27 May 2023
A Guide Through the Zoo of Biased SGD Yury Demidovich Grigory Malinovsky Igor Sokolov Peter Richtárik 100 28 0 25 May 2023
How to escape sharp minima with random perturbations Kwangjun Ahn Ali Jadbabaie S. Sra ODL 113 8 0 25 May 2023
On the Convergence of Black-Box Variational Inference Kyurae Kim Jisu Oh Kaiwen Wu Yi-An Ma Jacob R. Gardner BDL 94 17 0 24 May 2023
The Crucial Role of Normalization in Sharpness-Aware Minimization Yan Dai Kwangjun Ahn S. Sra 118 19 0 24 May 2023
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees Sharan Vaswani A. Kazemi Reza Babanezhad Nicolas Le Roux OffRL 83 4 0 24 May 2023
Improving Convergence and Generalization Using Parameter Symmetries Bo Zhao Robert Mansel Gower Robin Walters Rose Yu MoMe 127 16 0 22 May 2023
Classical-to-Quantum Transfer Learning Facilitates Machine Learning with Variational Quantum Circuit Jun Qi Chao-Han Huck Yang Pin-Yu Chen Min-hsiu Hsieh Hector Zenil Jesper Tegner 68 1 0 18 May 2023
Low-complexity subspace-descent over symmetric positive definite manifold Yogesh Darmwal K. Rajawat 95 3 0 03 May 2023
Can Decentralized Stochastic Minimax Optimization Algorithms Converge Linearly for Finite-Sum Nonconvex-Nonconcave Problems? Yihan Zhang Wenhao Jiang Feng-Song Zheng C. C. Tan Xinghua Shi Hongchang Gao 50 1 0 24 Apr 2023
Near-Optimal Decentralized Momentum Method for Nonconvex-PL Minimax Problems Feihu Huang Songcan Chen 67 5 0 21 Apr 2023
Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks Jing An Jianfeng Lu 55 4 0 18 Apr 2023
Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator Haobo Qi Feifei Wang Hansheng Wang 54 13 0 13 Apr 2023
Fast Convergence of Random Reshuffling under Over-Parameterization and the Polyak-Łojasiewicz Condition Chen Fan Christos Thrampoulidis Mark Schmidt 56 2 0 02 Apr 2023
Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems Sihan Zeng Thinh T. Doan Justin Romberg OffRL 51 3 0 23 Mar 2023
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference Kyurae Kim Kaiwen Wu Jisu Oh Jacob R. Gardner BDL 96 8 0 18 Mar 2023
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression Avetik G. Karagulyan Peter Richtárik FedML 59 6 0 08 Mar 2023
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning Xinnan Yuan W. Ni Ming Ding Kang Wei Jun Li H. Vincent Poor FedML 64 45 0 07 Mar 2023
On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level Feihu Huang 99 19 0 07 Mar 2023
Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization Feihu Huang Chunyu Xuan Xinrui Wang Siqi Zhang Songcan Chen 114 7 0 07 Mar 2023
Fast and Interpretable Dynamics for Fisher Markets via Block-Coordinate Updates Tianlong Nan Yuan Gao Christian Kroer 131 3 0 01 Mar 2023
From Optimization to Sampling Through Gradient Flows Nicolas García Trillos B. Hosseini D. Sanz-Alonso 44 11 0 22 Feb 2023
Fusion of Global and Local Knowledge for Personalized Federated Learning Tiansheng Huang Li Shen Yan Sun Weiwei Lin Dacheng Tao FedML 86 12 0 21 Feb 2023
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization Mathieu Dagréou Thomas Moreau Samuel Vaiter Pierre Ablin 109 12 0 17 Feb 2023
On Rank Energy Statistics via Optimal Transport: Continuity, Convergence, and Change Point Detection Matthew Werenski Shoaib Bin Masud James M. Murphy Shuchin Aeron 62 4 0 15 Feb 2023
Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization Jun-Kun Wang Andre Wibisono 76 10 0 15 Feb 2023
A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee Mo Zhou Jian-Xiong Lu 107 10 0 11 Feb 2023
On the Privacy-Robustness-Utility Trilemma in Distributed Learning Youssef Allouah R. Guerraoui Nirupam Gupta Rafael Pinot John Stephan FedML 70 27 0 09 Feb 2023
Federated Minimax Optimization with Client Heterogeneity Pranay Sharma Rohan Panda Gauri Joshi FedML 92 9 0 08 Feb 2023
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule Maor Ivgi Oliver Hinder Y. Carmon ODL 154 66 0 08 Feb 2023
Online Resource Allocation: Bandits feedback and Advice on Time-varying Demands Lixing Lyu Wang Chi Cheung 59 0 0 08 Feb 2023
On the Convergence of Federated Averaging with Cyclic Client Participation Yae Jee Cho Pranay Sharma Gauri Joshi Zheng Xu Satyen Kale Tong Zhang FedML 101 33 0 06 Feb 2023
Quantized Distributed Training of Large Models with Convergence Guarantees I. Markov Adrian Vladu Qi Guo Dan Alistarh MQ 82 11 0 05 Feb 2023
Follower Agnostic Methods for Stackelberg Games C. Maheshwari James Cheng S. S. Sasty Lillian J. Ratliff Eric Mazumdar 82 2 0 02 Feb 2023
Distributed Stochastic Optimization under a General Variance Condition Kun-Yen Huang Xiao Li Shin-Yi Pu FedML 74 7 0 30 Jan 2023