A Variational Perspective on Accelerated Methods in Optimization

14 March 2016

Papers citing "A Variational Perspective on Accelerated Methods in Optimization"

50 / 66 papers shown

Title
Spike-timing-dependent Hebbian learning as noisy gradient descent Niklas Dexheimer Sascha Gaudlitz Johannes Schmidt-Hieber 35 0 0 15 May 2025
Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy Functionals Alexander Mielke Jia Jie Zhu 68 1 0 28 Jan 2025
Distributed Event-Based Learning via ADMM Güner Dilsad Er Sebastian Trimpe Michael Muehlebach FedML 49 2 0 17 May 2024
A General Continuous-Time Formulation of Stochastic ADMM and Its Variants Chris Junchi Li 42 0 0 22 Apr 2024
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks Hristo Papazov Scott Pesme Nicolas Flammarion 40 5 0 08 Mar 2024
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework Fangyikang Wang Huminhao Zhu Chao Zhang Han Zhao Hui Qian 36 5 0 27 Dec 2023
Quantum Langevin Dynamics for Optimization Zherui Chen Yuchen Lu Hao Wang Yizhou Liu Tongyang Li AI4CE 26 10 0 27 Nov 2023
Accelerating optimization over the space of probability measures Shi Chen Wenxuan Wu Yuhang Yao Stephen J. Wright 37 5 0 06 Oct 2023
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning Shuxin Zheng Jiyan He Chang-Shu Liu Yu Shi Ziheng Lu ... Peiran Jin Chi Chen Frank Noé Haiguang Liu Tie-Yan Liu AI4CE 43 41 0 08 Jun 2023
On Underdamped Nesterov's Acceleration Shu Chen Bin Shi Ya-xiang Yuan 40 5 0 28 Apr 2023
Beyond first-order methods for non-convex non-concave min-max optimization Abhijeet Vyas Brian Bullins 36 1 0 17 Apr 2023
Sublinear Convergence Rates of Extragradient-Type Methods: A Survey on Classical and Recent Developments Quoc Tran-Dinh 40 7 0 30 Mar 2023
Quantum Hamiltonian Descent Jiaqi Leng Ethan Hickman Joseph Li Xiaodi Wu 31 13 0 02 Mar 2023
Accelerated First-Order Optimization under Nonlinear Constraints Michael Muehlebach Michael I. Jordan 55 3 0 01 Feb 2023
Generalized Gradient Flows with Provable Fixed-Time Convergence and Fast Evasion of Non-Degenerate Saddle Points Mayank Baranwal Param Budhraja V. Raj A. Hota 33 2 0 07 Dec 2022
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties David Martínez-Rubio Sebastian Pokutta 35 9 0 26 Nov 2022
Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation Jun-Kun Wang Andre Wibisono 37 7 0 18 Oct 2022
On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks Yizhou Liu Weijie J. Su Tongyang Li 41 18 0 29 Sep 2022
Gradient Norm Minimization of Nesterov Acceleration: $o(1/k^3)$ Shu Chen Bin Shi Ya-xiang Yuan 44 15 0 19 Sep 2022
Conformal Mirror Descent with Logarithmic Divergences Amanjit Kainth Ting-Kam Leonard Wong Frank Rudzicz 33 4 0 07 Sep 2022
Multilevel Geometric Optimization for Regularised Constrained Linear Inverse Problems Sebastian Müller Stefania Petra Matthias Zisler AI4CE 25 1 0 11 Jul 2022
Alternating Mirror Descent for Constrained Min-Max Games Andre Wibisono Molei Tao Georgios Piliouras 39 14 0 08 Jun 2022
Perseus: A Simple and Optimal High-Order Method for Variational Inequalities Tianyi Lin Michael I. Jordan 35 10 0 06 May 2022
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents Alessandro Barp Lancelot Da Costa G. Francca Karl J. Friston Mark Girolami Michael I. Jordan G. Pavliotis 38 25 0 20 Mar 2022
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective Yasong Feng Weiguo Gao 23 0 0 09 Dec 2021
Breaking the Convergence Barrier: Optimization via Fixed-Time Convergent Flows Param Budhraja Mayank Baranwal Kunal Garg A. Hota 23 9 0 02 Dec 2021
No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimization Jun-Kun Wang Jacob D. Abernethy Kfir Y. Levy 32 21 0 22 Nov 2021
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel Yindong Chen Yiwei Wang Lulu Kang Chun Liu 33 1 0 21 Nov 2021
Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum J. Kim Panos Toulis Anastasios Kyrillidis 31 8 0 11 Nov 2021
On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems Michael Muehlebach Michael I. Jordan 42 18 0 17 Jul 2021
The Confluence of Networks, Games and Learning Tao Li Guanze Peng Quanyan Zhu Tamer Basar AI4CE 29 46 0 17 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 32 9 0 23 Feb 2021
Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization Jelena Diakonikolas Puqian Wang 33 13 0 28 Jan 2021
First-Order Methods for Convex Optimization Pavel Dvurechensky Mathias Staudigl Shimrit Shtern ODL 36 25 0 04 Jan 2021
Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets Avetik G. Karagulyan A. Dalalyan 24 8 0 24 Jun 2020
A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm O. Romero Subhro Das Pin-Yu Chen S. Pequito 16 1 0 23 Jun 2020
Accelerated Learning with Robustness to Adversarial Regressors Joseph E. Gaudio Anuradha M. Annaswamy J. Moreu M. Bolender T. Gibson 64 20 0 04 May 2020
On Learning Rates and Schrödinger Operators Bin Shi Weijie J. Su Michael I. Jordan 34 60 0 15 Apr 2020
On dissipative symplectic integration with applications to gradient-based optimization G. Francca Michael I. Jordan René Vidal 21 47 0 15 Apr 2020
Optimal anytime regret with two experts Nicholas J. A. Harvey Christopher Liaw E. Perkins Sikander Randhawa 21 16 0 20 Feb 2020
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models Chin-Wei Huang Laurent Dinh Aaron Courville DRL 31 87 0 17 Feb 2020
From Nesterov's Estimate Sequence to Riemannian Acceleration Kwangjun Ahn S. Sra 24 74 0 24 Jan 2020
Demon: Improved Neural Network Training with Momentum Decay John Chen Cameron R. Wolfe Zhaoqi Li Anastasios Kyrillidis ODL 24 15 0 11 Oct 2019
Accelerated Information Gradient flow Yifei Wang Wuchen Li 25 56 0 04 Sep 2019
Conjugate Gradients and Accelerated Methods Unified: The Approximate Duality Gap View Jelena Diakonikolas L. Orecchia 31 1 0 29 Jun 2019
Continuous Time Analysis of Momentum Methods Nikola B. Kovachki Andrew M. Stuart 23 33 0 10 Jun 2019
Theoretical guarantees for sampling and inference in generative models with latent diffusions Belinda Tzen Maxim Raginsky DiffM 26 99 0 05 Mar 2019
Accelerated Flow for Probability Distributions Amirhossein Taghvaei P. Mehta 47 31 0 10 Jan 2019
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations Qianxiao Li Cheng Tai E. Weinan 11 149 0 05 Nov 2018
Understanding the Acceleration Phenomenon via High-Resolution Differential Equations Bin Shi S. Du Michael I. Jordan Weijie J. Su 17 254 0 21 Oct 2018