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Statistical tasks such as density estimation and approximate Bayesian inference often involve densities with unknown normalising constants.
“Estimation of non-normalized statistical models by score matching”
Aapo Hyvärinen · 2005
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“Support vector machines”, Information Science and Statistics
Ingo Steinwart and Andreas Christmann · 2008
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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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“A Kernelized Stein Discrepancy for Goodness-of-fit Tests”
Qiang Liu, Jason. Lee and Michael Jordan · 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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“Measuring sample quality with kernels”
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“A Spectral Approach to Gradient Estimation for Implicit Distributions”
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“Message passing Stein variational gradient descent”
Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen and Bo Zhang · 2018
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“Measuring sample quality with diffusions”
Jackson Gorham, Andrew Duncan, Sebastian Vollmer and Lester Mackey · 2019
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“Generative modeling by estimating gradients of the data distribution”
Yang Song and Stefano Ermon · 2019
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“Learning deep kernels for exponential family densities”
Li. Wenliang, D.. Sutherland, Heiko Strathmann and Arthur Gretton · 2019
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“Stochastic particle-optimization sampling and the non-asymptotic convergence theory”
Jianyi Zhang, Ruiyi Zhang, Lawrence Carin and Changyou Chen · 2020
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“Annealed stein variational gradient descent”
Francesco D’Angelo and Vincent Fortuin · 2021
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