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We provide the first finite-particle convergence rate for Stein variational gradient descent (SVGD), a popular algorithm for approximating a probability distribution with a collection of particles.
Optimal transport: old and new , volume 338
Cédric Villani · 2009
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Reproducing kernel Hilbert spaces in probability and statistics
Alain Berlinet and Christine Thomas-Agnan · 2011
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Log-concavity and strong log-concavity: a review
Adrien Saumard and Jon A Wellner · 2014
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Measuring sample quality with Stein’s method
Jackson Gorham and Lester Mackey · 2015
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A kernel test of goodness of fit
Kacper Chwialkowski, Heiko Strathmann, and Arthur Gretton · 2016
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Stein variational gradient descent: A general purpose Bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
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A kernelized Stein discrepancy for goodness-of-fit tests
Qiang Liu, Jason Lee, and Michael Jordan · 2016
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Learning to draw samples: With application to amortized MLE for generative adversarial learning
Dilin Wang and Qiang Liu · 2016
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Measuring sample quality with kernels
Jackson Gorham and Lester Mackey · 2017
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
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Stein variational gradient descent as gradient flow
Qiang Liu · 2017
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Stein variational policy gradient
Yang Liu, Prajit Ramachandran, Qiang Liu, and Jian Peng · 2017
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Stein points
Wilson Ye Chen, Lester Mackey, Jackson Gorham, François-Xavier Briol, and Chris Oates · 2018
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Global non-convex optimization with discretized diffusions
Murat A. Erdogdu, Lester Mackey, and Ohad Shamir · 2018
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Random feature Stein discrepancies
Jonathan Huggins and Lester Mackey · 2018
Cited alongside, same era.
Stein variational message passing for continuous graphical models
Dilin Wang, Zhe Zeng, and Qiang Liu · 2018
Cited alongside, same era.
A non-asymptotic analysis for Stein variational gradient descent
Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, and Arthur Gretton · 2020
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Convergence and concentration of empirical measures under wasserstein distance in unbounded functional spaces
Jing Lei · 2020
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Learning equivariant energy based models with equivariant Stein variational gradient descent
Priyank Jaini, Lars Holdijk, and Max Welling · 2021
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Targeted separation and convergence with kernel discrepancies
Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami, and Lester Mackey · 2022
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Controlling moments with kernel stein discrepancies
Heishiro Kanagawa, Arthur Gretton, and Lester Mackey · 2022
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Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, and Bo Zhang · 2018
Cited alongside, same era.
On the geometry of Stein variational gradient descent
Andrew Duncan, Nikolas Nüsken, and Lukasz Szpruch · 2019
Cited alongside, same era.
Stochastic Stein discrepancies
Jackson Gorham, Anant Raj, and Lester Mackey · 2020
Cited alongside, same era.
A convergence theory for SVGD in the population limit under Talagrand’s inequality T1
Adil Salim, Lukang Sun, and Peter Richtarik · 2022
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Convergence of Stein variational gradient descent under a weaker smoothness condition
Lukang Sun, Avetik Karagulyan, and Peter Richtarik · 2022
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Provably fast finite particle variants of svgd via virtual particle stochastic approximation
Aniket Das and Dheeraj Nagaraj · 2023
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Towards understanding the dynamics of gaussian–stein variational gradient descent
Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, and Natesh Pillai · 2023
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