On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo

15 February 2018

Niladri S. Chatterji

Papers citing "On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo"

27 / 27 papers shown

Title
Random Reshuffling for Stochastic Gradient Langevin Dynamics Luke Shaw Peter A. Whalley 113 3 0 28 Jan 2025
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics Daniel Paulin P. Whalley Neil K. Chada B. Leimkuhler BDL 52 4 0 14 Oct 2024
Contraction Rate Estimates of Stochastic Gradient Kinetic Langevin Integrators B. Leimkuhler Daniel Paulin P. Whalley 36 5 0 14 Jun 2023
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression Avetik G. Karagulyan Peter Richtárik FedML 39 6 0 08 Mar 2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction Bowen Lei Dongkuan Xu Ruqi Zhang Shuren He Bani Mallick 47 6 0 09 Jan 2023
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms Vincent Plassier Alain Durmus Eric Moulines FedML 34 6 0 31 Oct 2022
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition Lukang Sun Avetik G. Karagulyan Peter Richtárik 39 19 0 01 Jun 2022
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo Krishnakumar Balasubramanian Sinho Chewi Murat A. Erdogdu Adil Salim Matthew Shunshi Zhang 58 61 0 10 Feb 2022
HMC and underdamped Langevin united in the unadjusted convex smooth case Nicolai Gouraud Pierre Le Bris Adrien Majka Pierre Monmarché 25 11 0 02 Feb 2022
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint Y. Freund Yi Ma Tong Zhang 42 16 0 05 Oct 2021
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction Wei Deng Qi Feng G. Karagiannis Guang Lin F. Liang 38 9 0 02 Oct 2020
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness Murat A. Erdogdu Rasa Hosseinzadeh 23 75 0 27 May 2020
Aggregated Gradient Langevin Dynamics Chao Zhang Jiahao Xie Zebang Shen P. Zhao Tengfei Zhou Hui Qian 36 1 0 21 Oct 2019
Stochastic gradient Markov chain Monte Carlo Christopher Nemeth Paul Fearnhead BDL 32 135 0 16 Jul 2019
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates Adil Salim D. Kovalev Peter Richtárik 27 25 0 28 May 2019
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting Maxime Vono Daniel Paulin Arnaud Doucet 37 37 0 23 May 2019
Optimal Convergence Rate of Hamiltonian Monte Carlo for Strongly Logconcave Distributions Zongchen Chen Santosh Vempala 26 64 0 07 May 2019
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets R. Cornish Paul Vanetti Alexandre Bouchard-Côté George Deligiannidis Arnaud Doucet 57 16 0 28 Jan 2019
Breaking Reversibility Accelerates Langevin Dynamics for Global Non-Convex Optimization Xuefeng Gao Mert Gurbuzbalaban Lingjiong Zhu 29 30 0 19 Dec 2018
Algorithmic Theory of ODEs and Sampling from Well-conditioned Logconcave Densities Y. Lee Zhao Song Santosh Vempala 37 37 0 15 Dec 2018
On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case M. Barkhagen N. H. Chau '. Moulines Miklós Rásonyi S. Sabanis Ying Zhang 28 37 0 06 Dec 2018
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory Jianyi Zhang Ruiyi Zhang Lawrence Carin Changyou Chen 17 46 0 05 Sep 2018
On sampling from a log-concave density using kinetic Langevin diffusions A. Dalalyan L. Riou-Durand 34 155 0 24 Jul 2018
Hamiltonian Monte Carlo with Energy Conserving Subsampling Khue-Dung Dang M. Quiroz Robert Kohn Minh-Ngoc Tran M. Villani 43 62 0 02 Aug 2017
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization Pan Xu Jinghui Chen Difan Zou Quanquan Gu 36 200 0 20 Jul 2017
Control Variates for Stochastic Gradient MCMC Jack Baker Paul Fearnhead E. Fox Christopher Nemeth BDL 40 101 0 16 Jun 2017
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data J. Bierkens Paul Fearnhead Gareth O. Roberts 58 231 0 11 Jul 2016