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We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution.
Unbiased estimators for the variance of MMD estimators, 2019
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Classification logit two-sample testing by neural networks, 2019
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Two-sample testing using deep learning
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Modified randomization tests for nonparametric hypotheses
Dwass, M · 1957
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The Berry-Esseen theorem for u u -statistics
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Approximation Theorems of Mathematical Statistics
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Integral probability metrics and their generating classes of functions
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Gradient-based learning applied to document recognition
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Asymptotic Statistics
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On the mathematical foundations of learning
Cucker, F. and Smale, S · 2001
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Smola, A. J. and Schölkopf, B · 2001
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Reproducing Kernel Hilbert Spaces in Probability and Statistics
Berlinet, A. and Thomas-Agnan, C · 2004
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Testing for homogeneity with kernel Fisher discriminant analysis
Harchaoui, Z., Bach, F., and Moulines, E · 2007
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A test for the two-sample problem based on empirical characteristic functions
Alba Fernández, V., Jiménez Gamero, M., and Muñoz García, J · 2008
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80 million tiny images: A large data set for nonparametric object and scene recognition
Torralba, A., Fergus, R., and Freeman, W. T · 2008
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A fast, consistent kernel two-sample test
Gretton, A., Fukumizu, K., Harchaoui, Z., and Sriperumbudur, B. K · 2009
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Learning multiple layers of features from tiny images, 2009
Krizhevsky, A · 2009
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Kernel choice and classifiability for rkhs embeddings of probability distributions
Sriperumbudur, B. K., Fukumizu, K., Gretton, A., Lanckriet, G. R., and Schölkopf, B · 2009
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Multiple kernel learning algorithms
Gönen, M. and Alpaydın, E · 2011
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Székely, G. J. and Rizzo, M. L · 2013
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B-tests: Low variance kernel two-sample tests
Zaremba, W., Gretton, A., and Blaschko, M · 2013
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Baldi, P., Sadowski, P., and Whiteson, D · 2014
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Lopez-Paz, D. and Oquab, M · 2017
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Kernel mean embedding of distributions: A review and beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., and Schölkopf, B · 2017
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On Wasserstein two-sample testing and related families of nonparametric tests
Ramdas, A., García Trillos, N., and Cuturi, M · 2017
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Generative models and model criticism via optimized maximum mean discrepancy
Sutherland, D. J., Tung, H.-Y., Strathmann, H., De, S., Ramdas, A., Smola, A., and Gretton, A · 2017
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On gradient regularizers for MMD GANs
Arbel, M., Sutherland, D. J., Binkowski, M., and Gretton, A · 2018
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Demystifying MMD GANs
Binkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A · 2018
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Practical methods for graph two-sample testing
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Semi-supervised deep kernel learning: Regression with unlabeled data by minimizing predictive variance
Jean, N., Xie, S. M., and Ermon, S · 2018
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Fully distributed sequential hypothesis testing: Algorithms and asymptotic analyses
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Deep layers as stochastic solvers
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Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
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The singular values of convolutional layers
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Wenliang, L., Sutherland, D. J., Strathmann, H., and Gretton, A · 2019
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Classification accuracy as a proxy for two sample testing
Kim, I., Ramdas, A., Singh, A., and Wasserman, L · 2020
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