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Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity

12 December 2017

Papers citing "Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity"

26 / 26 papers shown

Title
Glocal Smoothness: Line Search can really help! Curtis Fox Aaron Mishkin Sharan Vaswani Mark Schmidt 41 2 0 14 Jun 2025
On the asymptotic behaviour of stochastic processes, with applications to supermartingale convergence, Dvoretzky's approximation theorem, and stochastic quasi-Fejér monotonicity Morenikeji Neri Nicholas Pischke Thomas Powell 42 0 0 17 Apr 2025
Towards Weaker Variance Assumptions for Stochastic Optimization Ahmet Alacaoglu Yura Malitsky Stephen J. Wright 82 1 0 14 Apr 2025
Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization Dmitry Kovalev 139 5 0 16 Mar 2025
Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization Benjamin Grimmer Danlin Li 128 6 0 31 Dec 2024
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds Daniel Dodd Louis Sharrock Christopher Nemeth 122 0 0 04 Jun 2024
Towards Stability of Parameter-free Optimization Yijiang Pang Shuyang Yu Hoang Bao Jiayu Zhou 66 1 0 07 May 2024
Optimization on a Finer Scale: Bounded Local Subgradient Variation Perspective Jelena Diakonikolas Cristóbal Guzmán 422 2 0 24 Mar 2024
Directional Smoothness and Gradient Methods: Convergence and Adaptivity Aaron Mishkin Ahmed Khaled Yuanhao Wang Aaron Defazio Robert Mansel Gower 133 9 0 06 Mar 2024
Revisiting Convergence of AdaGrad with Relaxed Assumptions Yusu Hong Junhong Lin 51 13 0 21 Feb 2024
Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity Wenzhi Gao Qi Deng 61 1 0 25 Jan 2024
A Unified Analysis for the Subgradient Methods Minimizing Composite Nonconvex, Nonsmooth and Non-Lipschitz Functions Daoli Zhu Lei Zhao Shuzhong Zhang 111 2 0 30 Aug 2023
Normalized Gradients for All Francesco Orabona 108 10 0 10 Aug 2023
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method Ahmed Khaled Konstantin Mishchenko Chi Jin ODL 86 28 0 25 May 2023
Revisiting Subgradient Method: Complexity and Convergence Beyond Lipschitz Continuity Xiao Li Lei Zhao Daoli Zhu Anthony Man-Cho So 23 3 0 23 May 2023
Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets Ning Liu Benjamin Grimmer 95 4 0 09 Mar 2023
Randomized Coordinate Subgradient Method for Nonsmooth Composite Optimization Lei Zhao Ding-Yuan Chen Daoli Zhu Xiao Li 74 1 0 30 Jun 2022
Stochastic Approximation with Discontinuous Dynamics, Differential Inclusions, and Applications N. Nguyen G. Yin 55 7 0 28 Aug 2021
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses Yihan Zhou V. S. Portella Mark Schmidt Nicholas J. A. Harvey 55 21 0 22 Oct 2020
Mitigating Sybil Attacks on Differential Privacy based Federated Learning Yupeng Jiang Yong Li Yipeng Zhou Xi Zheng FedML AAML 65 15 0 20 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 87 0 0 26 Aug 2020
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization Ahmed Khaled Othmane Sebbouh Nicolas Loizou Robert Mansel Gower Peter Richtárik 119 47 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
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 107 187 0 09 Feb 2020
Unified Optimal Analysis of the (Stochastic) Gradient Method Sebastian U. Stich 82 113 0 09 Jul 2019
The Value of Collaboration in Convex Machine Learning with Differential Privacy Nan Wu Farhad Farokhi David B. Smith M. Kâafar FedML 78 99 0 24 Jun 2019