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Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD) have grown central to a wide range of applications, including hypothesis testing, sampler selection, distribution approximation, and variational inference.
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Mario Micheli and Joan Alexis Glaunes · 2013
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On the inclusion relation of reproducing kernel Hilbert spaces
Haizhang Zhang and Liang Zhao · 2013
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On positive and conditionally negative definite functions with a singularity at zero, and their applications in potential theory
Tomos Phillips · 2018
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Kernel distribution embeddings: Universal kernels, characteristic kernels and kernel metrics on distributions
Carl-Johann Simon-Gabriel and Bernhard Schölkopf · 2018
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Minimum Stein discrepancy estimators
Alessandro Barp, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, and Lester Mackey · 2019
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Statistical inference for generative models with maximum mean discrepancy
Francois-Xavier Briol, Alessandro Barp, Andrew B. Duncan, and Mark Girolami · 2019
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Stein point Markov chain Monte Carlo
Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark Girolami, Lester Mackey, Chris Oates, et al · 2019
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Chris J. Oates, Mark Girolami, and Nicolas Chopin · 2014
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Global divergence theorems in nonlinear PDEs and geometry
Stefano Pigola and Alberto G. Setti · 2014
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Training generative neural networks via maximum mean discrepancy optimization
Gintare K. Dziugaite, Daniel M. Roy, and Zoubin Ghahramani · 2015
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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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Bayesian posterior approximation via greedy particle optimization
Futoshi Futami, Zhenghang Cui, Issei Sato, and Masashi Sugiyama · 2019
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Measuring sample quality with diffusions
Jackson Gorham, Andrew B. Duncan, Sebastian J. Vollmer, and Lester Mackey · 2019
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Convergence rates for a class of estimators based on Stein’s method
Chris J. Oates, Jon Cockayne, François-Xavier Briol, and Mark Girolami · 2019
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MMD-Bayes: Robust Bayesian estimation via maximum mean discrepancy
Badr-Eddine Chérief-Abdellatif and Pierre Alquier · 2020
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Stochastic Stein discrepancies
Jackson Gorham, Anant Raj, and Lester Mackey · 2020
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The reproducing Stein kernel approach for post-hoc corrected sampling
Liam Hodgkinson, Robert Salomone, and Fred Roosta · 2020
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A unifying and canonical description of measure-preserving diffusions
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Robust generalised Bayesian inference for intractable likelihoods
Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol, Chris Oates, et al · 2021
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Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap
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