Faster Sampling via Stochastic Gradient Proximal Sampler

Faster Sampling via Stochastic Gradient Proximal Sampler

27 May 2024

Tong Zhang

Papers citing "Faster Sampling via Stochastic Gradient Proximal Sampler"

13 / 13 papers shown

Title
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo Xunpeng Huang Difan Zou Hanze Dong Yian Ma Tong Zhang DiffM 50 12 0 12 Jan 2024
Faster high-accuracy log-concave sampling via algorithmic warm starts Jason M. Altschuler Sinho Chewi 76 35 0 20 Feb 2023
Improved analysis for a proximal algorithm for sampling Yongxin Chen Sinho Chewi Adil Salim Andre Wibisono 94 57 0 13 Feb 2022
A Proximal Algorithm for Sampling from Non-smooth Potentials Jiaming Liang Yongxin Chen 81 26 0 09 Oct 2021
Structured Logconcave Sampling with a Restricted Gaussian Oracle Y. Lee Ruoqi Shen Kevin Tian 50 70 0 07 Oct 2020
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm Wenlong Mou Yian Ma Martin J. Wainwright Peter L. Bartlett Michael I. Jordan DiffM 45 85 0 28 Aug 2019
Optimal Convergence Rate of Hamiltonian Monte Carlo for Strongly Logconcave Distributions Zongchen Chen Santosh Vempala 61 65 0 07 May 2019
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices Santosh Vempala Andre Wibisono 79 264 0 20 Mar 2019
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis Maxim Raginsky Alexander Rakhlin Matus Telgarsky 70 521 0 13 Feb 2017
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 106 908 0 17 Feb 2014
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming Saeed Ghadimi Guanghui Lan ODL 120 1,549 0 22 Sep 2013
Langevin diffusions and the Metropolis-adjusted Langevin algorithm Tatiana Xifara Chris Sherlock Samuel Livingstone Simon Byrne Mark Girolami 82 126 0 11 Sep 2013
MCMC using Hamiltonian dynamics Radford M. Neal 290 3,281 0 09 Jun 2012

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.