Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond

27 June 2019

Papers citing "Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond"

24 / 24 papers shown

Title
Minimisation of Quasar-Convex Functions Using Random Zeroth-Order Oracles Amir Ali Farzin Yuen-Man Pun Iman Shames 31 0 0 04 May 2025
Expected Variational Inequalities B. Zhang Ioannis Anagnostides Emanuel Tewolde Ratip Emin Berker Gabriele Farina Vincent Conitzer T. Sandholm 265 0 0 25 Feb 2025
Nesterov acceleration in benignly non-convex landscapes Kanan Gupta Stephan Wojtowytsch 42 2 0 10 Oct 2024
Demystifying SGD with Doubly Stochastic Gradients Kyurae Kim Joohwan Ko Yian Ma Jacob R. Gardner 55 0 0 03 Jun 2024
Mean-field underdamped Langevin dynamics and its spacetime discretization Qiang Fu Ashia Wilson 52 4 0 26 Dec 2023
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates Siqi Zhang S. Choudhury Sebastian U. Stich Nicolas Loizou FedML 28 3 0 08 Jun 2023
PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment Yiqing Zhang Xinming Huang Ziming Zhang 38 4 0 21 Mar 2023
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule Maor Ivgi Oliver Hinder Y. Carmon ODL 40 57 0 08 Feb 2023
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties David Martínez-Rubio Sebastian Pokutta 25 9 0 26 Nov 2022
Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces Amirali Aghazadeh Nived Rajaraman Tony Tu Kannan Ramchandran 27 2 0 05 Oct 2022
SP2: A Second Order Stochastic Polyak Method Shuang Li W. Swartworth Martin Takávc Deanna Needell Robert Mansel Gower 29 13 0 17 Jul 2022
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Lam M. Nguyen Trang H. Tran 34 2 0 13 Jun 2022
Sharper Utility Bounds for Differentially Private Models Yilin Kang Yong Liu Jian Li Weiping Wang FedML 35 3 0 22 Apr 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 30 2 0 21 Mar 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Pranay Sharma Rohan Panda Gauri Joshi P. Varshney FedML 21 47 0 09 Mar 2022
Tackling benign nonconvexity with smoothing and stochastic gradients Harsh Vardhan Sebastian U. Stich 31 8 0 18 Feb 2022
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent Sharan Vaswani Benjamin Dubois-Taine Reza Babanezhad 53 11 0 21 Oct 2021
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints Shaojie Li Yong Liu 26 13 0 19 Jul 2021
Stochastic Polyak Stepsize with a Moving Target Robert Mansel Gower Aaron Defazio Michael G. Rabbat 32 17 0 22 Jun 2021
Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization Jun-Kun Wang Jacob D. Abernethy 24 7 0 04 Oct 2020
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation Robert Mansel Gower Othmane Sebbouh Nicolas Loizou 27 74 0 18 Jun 2020
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication Sebastian U. Stich Sai Praneeth Karimireddy FedML 25 20 0 11 Sep 2019
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark Schmidt 139 1,205 0 16 Aug 2016
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights Weijie Su Stephen P. Boyd Emmanuel J. Candes 108 1,157 0 04 Mar 2015