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Particle-based Variational Inference with Preconditioned Functional Gradient Flow

25 November 2022

Hanze Dong

Xi Wang

Yong Lin

Tong Zhang

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Papers citing "Particle-based Variational Inference with Preconditioned Functional Gradient Flow"

32 / 32 papers shown

Title
ELBOing Stein: Variational Bayes with Stein Mixture Inference Ola Rønning Eric T. Nalisnick Christophe Ley Padhraic Smyth Thomas Hamelryck BDL 97 1 0 30 Oct 2024
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference Dai Hai Nguyen Tetsuya Sakurai Hiroshi Mamitsuka 88 2 0 25 Oct 2023
Grassmann Stein Variational Gradient Descent Xingtu Liu Harrison Zhu Jean-François Ton George Wynne Andrew Duncan 78 12 0 07 Feb 2022
Variational Wasserstein gradient flow JiaoJiao Fan Qinsheng Zhang Amirhossein Taghvaei Yongxin Chen 140 57 0 04 Dec 2021
Neural Variational Gradient Descent L. Langosco Vincent Fortuin Heiko Strathmann BDL 89 20 0 22 Jul 2021
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks David Alvarez-Melis Yair Schiff Youssef Mroueh 80 57 0 01 Jun 2021
Large-Scale Wasserstein Gradient Flows Petr Mokrov Alexander Korotin Lingxiao Li Aude Genevay Justin Solomon Evgeny Burnaev 74 75 0 01 Jun 2021
Wasserstein Proximal of GANs A. Lin Wuchen Li Stanley Osher Guido Montúfar GAN 46 47 0 13 Feb 2021
Projected Wasserstein gradient descent for high-dimensional Bayesian inference Yifei Wang Peng Chen Wuchen Li 54 26 0 12 Feb 2021
Sliced Kernelized Stein Discrepancy Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato 69 37 0 30 Jun 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet 92 70 0 03 Jun 2020
Information Newton's flow: second-order optimization method in probability space Yifei Wang Wuchen Li 87 31 0 13 Jan 2020
Stein Variational Gradient Descent With Matrix-Valued Kernels Dilin Wang Ziyang Tang Minh Nguyen Qiang Liu 81 62 0 28 Oct 2019
Accelerated Information Gradient flow Yifei Wang Wuchen Li 74 57 0 04 Sep 2019
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices Santosh Vempala Andre Wibisono 90 269 0 20 Mar 2019
Stein Neural Sampler Tianyang Hu Zixiang Chen Hanxi Sun Jincheng Bai Mao Ye Guang Cheng SyDa GAN 63 35 0 08 Oct 2018
A Stein variational Newton method Gianluca Detommaso Tiangang Cui Alessio Spantini Youssef Marzouk Robert Scheichl 120 117 0 08 Jun 2018
Langevin Monte Carlo and JKO splitting Espen Bernton 60 80 0 23 Feb 2018
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem Andre Wibisono 101 183 0 22 Feb 2018
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models Atsushi Nitanda Taiji Suzuki GAN 94 12 0 07 Jan 2018
Message Passing Stein Variational Gradient Descent Jingwei Zhuo Chang-rui Liu Jiaxin Shi Jun Zhu Ning Chen Bo Zhang 61 92 0 13 Nov 2017
Learning to Draw Samples with Amortized Stein Variational Gradient Descent Yihao Feng Dilin Wang Qiang Liu GAN BDL 67 82 0 20 Jul 2017
Stein Variational Gradient Descent as Gradient Flow Qiang Liu OT 98 277 0 25 Apr 2017
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning Dilin Wang Qiang Liu GAN BDL 133 119 0 06 Nov 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 80 1,094 0 16 Aug 2016
A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation Qiang Liu Jason D. Lee Michael I. Jordan 108 486 0 10 Feb 2016
A Kernel Test of Goodness of Fit Kacper P. Chwialkowski Heiko Strathmann Arthur Gretton BDL 213 328 0 09 Feb 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 298 4,807 0 04 Jan 2016
Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks Chunyuan Li Changyou Chen David Carlson Lawrence Carin ODL BDL 91 327 0 23 Dec 2015
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 115 913 0 17 Feb 2014
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 150 1,167 0 31 Dec 2013
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 177 4,313 0 18 Nov 2011