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Kernelized Stein discrepancy (KSD), though being extensively used in goodness-of-fit tests and model learning, suffers from the curse-of-dimensionality.
Strip integration in radio astronomy
Ronald N Bracewell · 1956
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A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
Charles Stein et al · 1972
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Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Michael F Hutchinson · 1990
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On the bootstrap of u and v statistics
Miguel A Arcones and Evarist Gine · 1992
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A class of statistics with asymptotically normal distribution
Wassily Hoeffding · 1992
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Consistency of the generalized bootstrap for degenerate u-statistics
Marie Huskova and Paul Janssen · 1993
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The change-of-variables formula using matrix volume
Adi Ben-Israel · 1999
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Use of exchangeable pairs in the analysis of simulations
Charles Stein, Persi Diaconis, Susan Holmes, Gesine Reinert, et al · 2004
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2008
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Approximation theorems of mathematical statistics , volume 162
Robert J Serfling · 2009
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On integral probability metrics, \ \backslash phi-divergences and binary classification
Bharath K Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, and Gert RG Lanckriet · 2009
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Vector valued reproducing kernel hilbert spaces and universality
Claudio Carmeli, Ernesto De Vito, Alessandro Toigo, and Veronica Umanitá · 2010
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
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Stochastic gradient hamiltonian monte carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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A linear-time kernel goodness-of-fit test
Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, and Arthur Gretton · 2017
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VAE learning via Stein variational gradient descent
Yuchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han, and Lawrence Carin · 2017
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Message passing Stein variational gradient descent
Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, and Bo Zhang · 2017
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Conditional noise-contrastive estimation of unnormalised models
Ciwan Ceylan and Michael U Gutmann · 2018
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Generative modeling using the sliced Wasserstein distance
Ishan Deshpande, Ziyu Zhang, and Alexander G Schwing · 2018
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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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Sliced Wasserstein kernels for probability distributions
Soheil Kolouri, Yang Zou, and Gustavo K Rohde · 2016
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Two methods for wild variational inference
Qiang Liu and Yihao Feng · 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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Operator variational inference
Rajesh Ranganath, Dustin Tran, Jaan Altosaar, and David Blei · 2016
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Tianyang Hu, Zixiang Chen, Hanxi Sun, Jincheng Bai, Mao Ye, and Guang Cheng · 2018
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Random feature Stein discrepancies
Jonathan Huggins and Lester Mackey · 2018
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Stein variational message passing for continuous graphical models
Dilin Wang, Zhe Zeng, and Qiang Liu · 2018
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Max-sliced Wasserstein distance and its use for gans
Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Zhizhen Zhao, David Forsyth, and Alexander G Schwing · 2019
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Generalized sliced Wasserstein distances
Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, and Gustavo Rohde · 2019
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Kernelized complete conditional Stein discrepancy
Raghav Singhal, Xintian Han, Saad Lahlou, and Rajesh Ranganath · 2019
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Sliced score matching: A scalable approach to density and score estimation
Yang Song, Sahaj Garg, Jiaxin Shi, and Stefano Ermon · 2019
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Projected Stein variational gradient descent
Peng Chen and Omar Ghattas · 2020
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Cutting out the middle-man: Training and evaluating energy-based models without sampling
Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, and Richard Zemel · 2020
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