A Non-Asymptotic Analysis for Stein Variational Gradient Descent

17 June 2020

Papers citing "A Non-Asymptotic Analysis for Stein Variational Gradient Descent"

23 / 23 papers shown

Title
The equivalence between Stein variational gradient descent and black-box variational inference Casey Chu Kentaro Minami Kenji Fukumizu 23 7 0 04 Apr 2020
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices Santosh Vempala Andre Wibisono 43 262 0 20 Mar 2019
Stein Variational Gradient Descent as Moment Matching Qiang Liu Dilin Wang 69 38 0 27 Oct 2018
A Stein variational Newton method Gianluca Detommaso Tiangang Cui Alessio Spantini Youssef Marzouk Robert Scheichl 83 115 0 08 Jun 2018
Scaling limit of the Stein variational gradient descent: the mean field regime Jianfeng Lu Yulong Lu J. Nolen 22 79 0 10 May 2018
Analysis of Langevin Monte Carlo via convex optimization Alain Durmus Szymon Majewski B. Miasojedow 57 217 0 26 Feb 2018
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem Andre Wibisono 80 178 0 22 Feb 2018
Riemannian Stein Variational Gradient Descent for Bayesian Inference Chang-rui Liu Jun Zhu 49 67 0 30 Nov 2017
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 130 684 0 15 Nov 2017
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient A. Dalalyan Avetik G. Karagulyan 60 296 0 29 Sep 2017
Learning to Draw Samples with Amortized Stein Variational Gradient Descent Yihao Feng Dilin Wang Qiang Liu GAN BDL 46 79 0 20 Jul 2017
Stein Variational Gradient Descent as Gradient Flow Qiang Liu OT 60 274 0 25 Apr 2017
Stein Variational Policy Gradient Yang Liu Prajit Ramachandran Qiang Liu Jian-wei Peng 53 138 0 07 Apr 2017
Measuring Sample Quality with Kernels Jackson Gorham Lester W. Mackey 116 223 0 06 Mar 2017
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning Dilin Wang Qiang Liu GAN BDL 105 119 0 06 Nov 2016
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark Schmidt 221 1,208 0 16 Aug 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 61 1,082 0 16 Aug 2016
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm Alain Durmus Eric Moulines 71 352 0 05 May 2016
A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation Qiang Liu Jason D. Lee Michael I. Jordan 94 478 0 10 Feb 2016
A Kernel Test of Goodness of Fit Kacper P. Chwialkowski Heiko Strathmann Arthur Gretton BDL 159 327 0 09 Feb 2016
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 68 1,157 0 31 Dec 2013
Rényi Divergence and Kullback-Leibler Divergence T. Erven P. Harremoes 60 1,326 0 12 Jun 2012
Universality, Characteristic Kernels and RKHS Embedding of Measures Bharath K. Sriperumbudur Kenji Fukumizu Gert R. G. Lanckriet 154 526 0 03 Mar 2010