Papers citing 'Direct Runge-Kutta Discretization Achieves Acceleration'

Title
Optimization using Parallel Gradient Evaluations on Multiple Parameters Yash Chandak Shiv Shankar V. Gandikota Philip S. Thomas A. Mazumdar 42 0 0 06 Feb 2023
NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer Valentin Leplat D. Merkulov Aleksandr Katrutsa Daniel Bershatsky Olga Tsymboi Ivan Oseledets 185 4 0 29 Sep 2022
Domain Adversarial Training: A Game Perspective David Acuna M. Law Guojun Zhang Sanja Fidler 92 11 0 10 Feb 2022
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective Yasong Feng Weiguo Gao 131 0 0 09 Dec 2021
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip Mathieu Even Raphael Berthier Francis R. Bach Nicolas Flammarion Pierre Gaillard Aymeric Dieuleveut Laurent Massoulié Adrien B. Taylor 165 23 0 10 Jun 2021
Fast symplectic integrator for Nesterov-type acceleration method S. Goto H. Hino 40 4 0 01 Jun 2021
A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows Pedro Cisneros-Velarde Francesco Bullo 184 7 0 18 May 2021
Online Algorithms and Policies Using Adaptive and Machine Learning Approaches Anuradha M. Annaswamy A. Guha Yingnan Cui Sunbochen Tang Peter A. Fisher Joseph E. Gaudio 155 27 0 13 May 2021
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization Peiyuan Zhang Antonio Orvieto Hadi Daneshmand Thomas Hofmann Roy S. Smith 83 10 0 23 Feb 2021
Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization Jelena Diakonikolas Puqian Wang 103 13 0 28 Jan 2021
Implicit Gradient Regularization David Barrett Benoit Dherin 212 163 0 23 Sep 2020
How Does Momentum Help Frank Wolfe? Bingcong Li M. Coutiño G. Giannakis G. Leus 97 7 0 19 Jun 2020
On dissipative symplectic integration with applications to gradient-based optimization G. Francca Michael I. Jordan René Vidal 205 47 0 15 Apr 2020
Shadowing Properties of Optimization Algorithms Antonio Orvieto Aurelien Lucchi 101 18 0 12 Nov 2019
The Role of Memory in Stochastic Optimization Antonio Orvieto Jonas Köhler Aurelien Lucchi 116 32 0 02 Jul 2019
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond Xuechen Li Denny Wu Lester W. Mackey Murat A. Erdogdu 174 75 0 19 Jun 2019
Continuous Time Analysis of Momentum Methods Nikola B. Kovachki Andrew M. Stuart 173 38 0 10 Jun 2019
Generalized Momentum-Based Methods: A Hamiltonian Perspective Jelena Diakonikolas Michael I. Jordan 162 61 0 02 Jun 2019
Conformal Symplectic and Relativistic Optimization G. Francca Jeremias Sulam Daniel P. Robinson René Vidal 241 69 0 11 Mar 2019
Acceleration via Symplectic Discretization of High-Resolution Differential EquationsNeural Information Processing Systems (NeurIPS), 2025 Bin Shi S. Du Weijie J. Su Michael I. Jordan 91 122 0 11 Feb 2019
Is There an Analog of Nesterov Acceleration for MCMC? Yian Ma Niladri Chatterji Xiang Cheng Nicolas Flammarion Peter L. Bartlett Michael I. Jordan BDL 144 78 0 04 Feb 2019
A continuous-time analysis of distributed stochastic gradient Nicholas M. Boffi Jean-Jacques E. Slotine 103 15 0 28 Dec 2018
Understanding the Acceleration Phenomenon via High-Resolution Differential Equations Bin Shi S. Du Michael I. Jordan Weijie J. Su 147 284 0 21 Oct 2018
Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective Filip Tronarp Hans Kersting Simo Särkkä Philipp Hennig 183 68 0 08 Oct 2018
Continuous-time Models for Stochastic Optimization Algorithms Antonio Orvieto Aurelien Lucchi 143 32 0 05 Oct 2018
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration Ningyuan Chen Mert Gurbuzbalaban Lingjiong Zhu 154 65 0 12 Sep 2018
Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced S. Du Wei Hu Jason D. Lee MLT 279 252 0 04 Jun 2018