Random Reshuffling for Stochastic Gradient Langevin Dynamics

28 January 2025

Papers citing "Random Reshuffling for Stochastic Gradient Langevin Dynamics"

27 / 27 papers shown

Title
A Langevin sampling algorithm inspired by the Adam optimizer Benedict Leimkuhler René Lohmann Peter Whalley 178 0 0 26 Apr 2025
Randomised Splitting Methods and Stochastic Gradient Descent Luke Shaw Peter A. Whalley 108 1 0 05 Apr 2025
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics Daniel Paulin Peter Whalley Neil K. Chada Benedict Leimkuhler BDL 130 4 0 14 Oct 2024
Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond Jaeyoung Cha Jaewook Lee Chulhee Yun 95 24 0 13 Mar 2023
Contraction and Convergence Rates for Discretized Kinetic Langevin Dynamics Benedict Leimkuhler Daniel Paulin Peter Whalley 152 17 0 21 Feb 2023
Split Hamiltonian Monte Carlo revisited F. Casas J. Sanz-Serna Luke Shaw 127 9 0 15 Jul 2022
Sqrt(d) Dimension Dependence of Langevin Monte Carlo Ruilin Li H. Zha Molei Tao 96 29 0 08 Sep 2021
Asymptotic bias of inexact Markov Chain Monte Carlo methods in high dimension Alain Durmus A. Eberle 90 21 0 02 Aug 2021
Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations J. Sanz-Serna K. Zygalakis 79 23 0 26 Apr 2021
Random Reshuffling: Simple Analysis with Vast Improvements Konstantin Mishchenko Ahmed Khaled Peter Richtárik 140 135 0 10 Jun 2020
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization Ying Zhang Ömer Deniz Akyildiz Theodoros Damoulas Sotirios Sabanis 114 47 0 04 Oct 2019
Stochastic gradient Markov chain Monte Carlo Christopher Nemeth Paul Fearnhead BDL 83 139 0 16 Jul 2019
SGD: General Analysis and Improved Rates Robert Mansel Gower Nicolas Loizou Xun Qian Alibek Sailanbayev Egor Shulgin Peter Richtárik 120 383 0 27 Jan 2019
The promises and pitfalls of Stochastic Gradient Langevin Dynamics N. Brosse Alain Durmus Eric Moulines 91 78 0 25 Nov 2018
Non-asymptotic bounds for sampling algorithms without log-concavity Mateusz B. Majka Aleksandar Mijatović Lukasz Szpruch 81 75 0 21 Aug 2018
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo Niladri S. Chatterji Nicolas Flammarion Yian Ma Peter L. Bartlett Michael I. Jordan 90 87 0 15 Feb 2018
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient A. Dalalyan Avetik G. Karagulyan 166 298 0 29 Sep 2017
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization Pan Xu Jinghui Chen Difan Zou Quanquan Gu 95 205 0 20 Jul 2017
Control Variates for Stochastic Gradient MCMC Jack Baker Paul Fearnhead E. Fox Christopher Nemeth BDL 102 101 0 16 Jun 2017
Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent A. Dalalyan BDL 73 175 0 16 Apr 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis Maxim Raginsky Alexander Rakhlin Matus Telgarsky 88 521 0 13 Feb 2017
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm Alain Durmus Eric Moulines 184 358 0 05 May 2016
Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm Alain Durmus Eric Moulines 140 415 0 17 Jul 2015
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 146 916 0 17 Feb 2014
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring S. Ahn Anoop Korattikara Balan Max Welling 98 306 0 27 Jun 2012
Practical recommendations for gradient-based training of deep architectures Yoshua Bengio 3DH ODL 233 2,208 0 24 Jun 2012
MCMC using Hamiltonian dynamics Radford M. Neal 377 3,287 0 09 Jun 2012