On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo

15 February 2018

Niladri S. Chatterji

Nicolas Flammarion

Peter L. Bartlett

Michael I. Jordan

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

9 / 9 papers shown

Title
Random Reshuffling for Stochastic Gradient Langevin Dynamics Luke Shaw Peter A. Whalley 133 3 0 28 Jan 2025
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics Daniel Paulin Peter Whalley Neil K. Chada Benedict Leimkuhler BDL 70 4 0 14 Oct 2024
Estimating Normalizing Constants for Log-Concave Distributions: Algorithms and Lower Bounds Rong Ge Holden Lee Jianfeng Lu 35 22 0 08 Nov 2019
The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data J. Bierkens Paul Fearnhead Gareth O. Roberts 67 232 0 11 Jul 2016
On Markov chain Monte Carlo methods for tall data Rémi Bardenet Arnaud Doucet Chris Holmes 68 276 0 11 May 2015
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 105 1,817 0 01 Jul 2014
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 82 906 0 17 Feb 2014
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 248 1,246 0 10 Sep 2013
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang 115 1,031 0 10 Sep 2012