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Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting distance between distributions, are useful tools for fully nonparametric two-sample testing and learning on distributions.
Decomposition of random variables and vectors
Yu V Linnik and IV Ostrovskii · 1977
Earlier work this paper cites.
Positive definite probability densities and probability distributions
H-J Rossberg · 1995
Earlier work this paper cites.
Scattered Data Approximation
H. Wendland · 2004
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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Dataset Shift in Machine Learning
Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence · 2009
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Hilbert space embeddings and metrics on probability measures
Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, and Gert R.G. Lanckriet · 2010
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Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation
Paul Fearnhead and Dennis Prangle · 2012
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Learning from distributions via support measure machines
Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, and Bernhard Schölkopf · 2012
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Mixture model for multiple instance regression and applications in remote sensing
Z. Wang, L. Lan, and S. Vucetic · 2012
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M. Lichman · 2013
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Kernel embeddings of conditional distributions: A unified kernel framework for nonparametric inference in graphical models
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Two-stage sampled learning theory on distributions
Zoltán Szabó, Arthur Gretton, Barnabás Póczos, and Bharath K. Sriperumbudur · 2015
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Methodology for non-parametric deconvolution when the error distribution is unknown
Aurore Delaigle and Peter Hall · 2016
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Interpretable distribution features with maximum testing power
Wittawat Jitkrittum, Zoltán Szabó, Kacper P Chwialkowski, and Arthur Gretton · 2016
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DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
J. Mitrovic, D. Sejdinovic, and Y.W. Teh · 2016
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Kernel mean embedding of distributions: A review and beyonds
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, and Bernhard Schölkopf · 2016
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Anatoly Klypin, Gustavo Yepes, Stefan Gottlober, Francisco Prada, and Steffen Hess · 2014
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Fast two-sample testing with analytic representations of probability measures
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Michelle Ntampaka, Hy Trac, Dougal J. Sutherland, S. Fromenteau, B. Poczos, and Jeff Schneider · 2016
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Linear-time learning on distributions with approximate kernel embeddings
Dougal J. Sutherland, Junier B. Oliva, Barnabás Póczos, and Jeff G. Schneider · 2016
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Bayesian approaches to distribution regression
Ho Chung Leon Law, Dougal J. Sutherland, Dino Sejdinovic, and Seth Flaxman · 2017
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