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The reproducing kernel Hilbert space (RKHS) embedding of distributions offers a general and flexible framework for testing problems in arbitrary domains and has attracted considerable amount of attention in recent years.
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On the rate of convergence in the central limit theorem for martingales with discrete and continuous time
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Exact mean integrated squared error
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Asymptotically minimax hypothesis testing for nonparametric alternatives. i, ii, iii
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Minimax testing of hypotheses on the distribution density for ellipsoids in l_p
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Comprehensive identification of cell cycle–regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization
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The transcriptional program in the response of human fibroblasts to serum
V. R. Iyer, M. B. Eisen, D. T. Ross, G. Schuler, T. Moore, J. Lee, J. M. Trent, L. M. Staudt, J. Hudson, and M. S. Boguski · 1999
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Functional discovery via a compendium of expression profiles
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Adaptive chi-square tests
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Model-based clustering, discriminant analysis, and density estimation
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Diametrical clustering for identifying anti-correlated gene clusters
I. S. Dhillon, E. M. Marcotte, and U. Roshan · 2003
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Nonparametric Goodness-of-Fit Testing under Gaussian Models
Yu. I. Ingster and I. A. Suslina · 2003
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An extension of MATLAB to continuous functions and operators
L. N. Trefethen and Z. Battles · 2004
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Clustering on the unit hypersphere using von mises-fisher distributions
A. Banerjee, I. S. Dhillon, J. Ghosh, and S. Sra · 2005
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Sobolev tests of goodness of fit of distributions on compact riemannian manifolds
P. E. Jupp · 2005
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Mercer’s theorem, feature maps, and smoothing
H. Q. Minh, P. Niyogi, and Y. Yao · 2006
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Learning Theory: an Approximation Theory Viewpoint
F. Cucker and D. Zhou · 2007
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A kernel method for the two-sample-problem
A. Gretton, K. M. Borgwardt, M. Rasch, B. Schölkopf, and A. J. Smola · 2007
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On combinatorial testing problems
L. Addario-Berry, N. Broutin, L. Devroye, and G. Lugosi · 2010
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A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
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Distance covariance in metric spaces
R. Lyons · 2013
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Equivalence of distance-based and RKHS-based statistics in hypothesis testing
D. Sejdinovic, B. Sriperumbudur, A. Gretton, and K. Fukumizu · 2013
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The multivariate Watson distribution: maximum-likelihood estimation and other aspects
S. Sra and D. Karp · 2013
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B-test: a non-parametric, low variance kernel two-sample test
W. Zaremba, A. Gretton, and M. Blaschko · 2013
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Testing for homogeneity with kernel fisher discriminant analysis
Z. Harchaoui, F. Bach, and E. Moulines · 2007
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A Hilbert space embedding for distributions”
A. J. Smola, A. Gretton, L. Song, and B. Schölkopf · 2007
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Measuring and testing dependence by correlation of distances
G. J. Székely, M. L. Rizzo, and N. K. Bakirov · 2007
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Aggregating inconsistent information: ranking and clustering
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Testing Statistical Hypotheses
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Introduction to Nonparametric Estimation
A. B. Tsybakov · 2008
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Chebfun Guide
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Training generative neural networks via maximum mean discrepancy optimization
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Generative moment matching networks
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A kernel test of goodness of fit
K. Chwialkowski, H. Strathmann, and A. Gretton · 2016
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A kernelized stein discrepancy for goodness-of-fit tests
Q. Liu, J. Lee, and M. Jordan · 2016
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Measuring sample quality with kernels
J. Gorham and L. Mackey · 2017
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A linear-time kernel goodness-of-fit test
W. Jitkrittum, W. Xu, Z. Szabo, K. Fukumizu, and A. Gretton · 2017
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Kernel mean embedding of distributions: a review and beyond
K. Muandet, K. Fukumizu, B. Sriperumbudur, and B. Schölkopf · 2017
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