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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"

39 / 39 papers shown

Title
Minimisation of Quasar-Convex Functions Using Random Zeroth-Order Oracles Amir Ali Farzin Yuen-Man Pun Iman Shames 38 0 0 04 May 2025
Effect-driven interpretation: Functors for natural language composition Dylan Bumford Simon Charlow 101 0 0 01 Apr 2025
Expected Variational Inequalities B. Zhang Ioannis Anagnostides Emanuel Tewolde Ratip Emin Berker Gabriele Farina Vincent Conitzer Tuomas Sandholm 457 1 0 25 Feb 2025
Deep Loss Convexification for Learning Iterative Models Ziming Zhang Yuping Shao Yiqing Zhang Fangzhou Lin Haichong K. Zhang Elke Rundensteiner 3DPC 96 0 0 16 Nov 2024
Nesterov acceleration in benignly non-convex landscapes Kanan Gupta Stephan Wojtowytsch 78 2 0 10 Oct 2024
Online Non-Stationary Stochastic Quasar-Convex Optimization Yuen-Man Pun Iman Shames 30 0 0 04 Jul 2024
Demystifying SGD with Doubly Stochastic Gradients Kyurae Kim Joohwan Ko Yian Ma Jacob R. Gardner 140 2 0 03 Jun 2024
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization Andrew Lowy Jonathan R. Ullman Stephen J. Wright 95 8 0 17 Feb 2024
Mean-field underdamped Langevin dynamics and its spacetime discretization Qiang Fu Ashia Wilson 67 4 0 26 Dec 2023
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates Michael Menart Enayat Ullah Raman Arora Raef Bassily Cristóbal Guzmán 86 2 0 22 Nov 2023
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods Constantine Caramanis Dimitris Fotakis Alkis Kalavasis Vasilis Kontonis Christos Tzamos 78 5 0 08 Oct 2023
Invex Programs: First Order Algorithms and Their Convergence Adarsh Barik S. Sra Jean Honorio 58 2 0 10 Jul 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 125 4 0 08 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
PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment Yiqing Zhang Xinming Huang Ziming Zhang 71 4 0 21 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
Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization Jun-Kun Wang Andre Wibisono 76 10 0 15 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
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties David Martínez-Rubio Sebastian Pokutta 66 10 0 26 Nov 2022
Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces Amirali Aghazadeh Nived Rajaraman Tony Tu Kannan Ramchandran 65 2 0 05 Oct 2022
$On the Convergence of AdaGrad(Norm) on $\R^{d}$: Beyond Convexity, Non-Asymptotic Rate and Acceleration$ On the Convergence of AdaGrad(Norm) on $\R^{d}$ : Beyond Convexity, Non-Asymptotic Rate and Acceleration Zijian Liu Ta Duy Nguyen Alina Ene Huy Le Nguyen 75 8 0 29 Sep 2022
SP2: A Second Order Stochastic Polyak Method Shuang Li W. Swartworth Martin Takávc Deanna Needell Robert Mansel Gower 61 13 0 17 Jul 2022
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Lam M. Nguyen Trang H. Tran 63 2 0 13 Jun 2022
Special Properties of Gradient Descent with Large Learning Rates Amirkeivan Mohtashami Martin Jaggi Sebastian U. Stich MLT 98 9 0 30 May 2022
Sharper Utility Bounds for Differentially Private Models Yilin Kang Yong Liu Jian Li Weiping Wang FedML 83 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 55 2 0 21 Mar 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Pranay Sharma Rohan Panda Gauri Joshi P. Varshney FedML 110 49 0 09 Mar 2022
Tackling benign nonconvexity with smoothing and stochastic gradients Harsh Vardhan Sebastian U. Stich 91 8 0 18 Feb 2022
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent Sharan Vaswani Benjamin Dubois-Taine Reza Babanezhad 98 13 0 21 Oct 2021
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints Shaojie Li Yong Liu 103 13 0 19 Jul 2021
Stochastic Polyak Stepsize with a Moving Target Robert Mansel Gower Aaron Defazio Michael G. Rabbat 76 17 0 22 Jun 2021
Recent Theoretical Advances in Non-Convex Optimization Marina Danilova Pavel Dvurechensky Alexander Gasnikov Eduard A. Gorbunov Sergey Guminov Dmitry Kamzolov Innokentiy Shibaev 129 79 0 11 Dec 2020
Global Riemannian Acceleration in Hyperbolic and Spherical Spaces David Martínez-Rubio 107 20 0 07 Dec 2020
Persistent Reductions in Regularized Loss Minimization for Variable Selection Amin Jalali 111 0 0 30 Nov 2020
Towards Optimal Problem Dependent Generalization Error Bounds in Statistical Learning Theory Yunbei Xu A. Zeevi 116 17 0 12 Nov 2020
On The Convergence of First Order Methods for Quasar-Convex Optimization Jikai Jin 55 9 0 10 Oct 2020
Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization Jun-Kun Wang Jacob D. Abernethy 96 7 0 04 Oct 2020
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation Robert Mansel Gower Othmane Sebbouh Nicolas Loizou 120 76 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 80 20 0 11 Sep 2019