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Two-sample tests are important areas aiming to determine whether two collections of observations follow the same distribution or not.
The generalization of student’s ratio
Hotelling, H · 1931
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The kolmogorov-smirnov test for goodness of fit
Massey Jr, F. J · 1951
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Asymptotic theory of certain” goodness of fit” criteria based on stochastic processes
Anderson, T. W · 1952
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An introduction to multivariate statistical analysis
Anderson, T. W · 1962
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Kolmogorov-Smirnov Two-Sample Tests
Pratt, J. W · 1981
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On the method of bounded differences
McDiarmid, C · 1989
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A Consistent Goodness of Fit Test Based on the Total Variation Distance
Györfi, L · 1991
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Effect of high dimension: by an example of a two sample problem
Bai, Z · 1996
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Studies in the history of probability and statistics xliv a forerunner of the t-distribution
Pfanzagl, J · 1996
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Weak Convergence and Empirical Processes: With Applications to Statistics
van der Vaart, A · 1996
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A multivariate kolmogorov-smirnov test of goodness of fit
Justel, A · 1997
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Integral probability metrics and their generating classes of functions
Müller, A · 1997
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Gradient-based learning applied to document recognition
LeCun, Y · 1998
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Tests of goodness of fit based on the l 2 l_{2} -wasserstein distance
del Barrio, E · 1999
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Nonlinear dimensionality reduction by locally linear embedding
Roweis, S. T · 2000
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A global geometric framework for nonlinear dimensionality reduction
Tenenbaum, J. B · 2000
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Statistical guarantees of generative adversarial networks for distribution estimation
Chen, M · 2002
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Inequalities for the l1 deviation of the empirical distribution
Weissman, T · 2003
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Testing statistical hypotheses
Lehmann, E. L · 2005
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Reducing the dimensionality of data with neural networks
Hinton, G. E · 2006
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Riemannian manifolds: an introduction to curvature
Lee, J. M · 2006
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Bracketing metric entropy rates and empirical central limit theorems for function classes of besov- and sobolev-type
Nickl, R · 2007
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Finding the homology of submanifolds with high confidence from random samples
Niyogi, P · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J · 2009
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A fast, consistent kernel two-sample test
Gretton, A · 2009
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Aspects of multivariate statistical theory
Muirhead, R. J · 2009
Cited alongside, same era.
Rectified linear units improve restricted boltzmann machines
Nair, V · 2010
Cited alongside, same era.
An Introduction to Manifolds
Tu, L · 2010
Cited alongside, same era.
Deep sparse rectifier neural networks
Glorot, X · 2011
Change detection via affine and quadratic detectors
Cao, Y · 2018
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Revisiting classifier two-sample tests
Lopez-Paz, D · 2018
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Efficient approximation of deep relu networks for functions on low dimensional manifolds
Chen, M · 2019
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On the intrinsic dimensionality of image representations
Gong, S · 2019
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Detecting adversarial examples in deep neural networks using normalizing filters
Gu, S · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Wainwright, M. J · 2019
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Convolutional neural networks applied to house numbers digit classification
Sermanet, P · 2012
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Rectifier nonlinearities improve neural network acoustic models
Maas, A. L · 2013
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Sequential multi-sensor change-point detection
Xie, Y · 2013
Cited alongside, same era.
On the rate of convergence in wasserstein distance of the empirical measure
Fournier, N · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
Goodfellow, I. J · 2014
Cited alongside, same era.
Estimation of smooth densities in wasserstein distance
Weed, J · 2019
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Projection robust wasserstein distance and riemannian optimization
Lin, T · 2020
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Learning deep kernels for non-parametric two-sample tests
Liu, F · 2020
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Nonparametric regression using deep neural networks with relu activation function
Schmidt-Hieber, J · 2020
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Asymptotics of smoothed wasserstein distances
Chen, H.-B · 2021
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On projection robust optimal transport: Sample complexity and model misspecification
Lin, T · 2021
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The intrinsic dimension of images and its impact on learning
Pope, P · 2021
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Provably robust classification of adversarial examples with detection
Sheikholeslami, F · 2021
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Two-sample test using projected wasserstein distance
Wang, J · 2021
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Sequential change detection by optimal weighted ℓ 2 \ell_{2} divergence
Xie, L · 2021
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Nonparametric regression on low-dimensional manifolds using deep relu networks: function approximation and statistical recovery
Chen, M · 2022
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Classification logit two-sample testing by neural networks for differentiating near manifold densities
Cheng, X · 2022
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Estimation of wasserstein distances in the spiked transport model
Niles-Weed, J · 2022
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Detecting adversarial examples is (nearly) as hard as classifying them
Tramer, F · 2022
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Two-sample test with kernel projected wasserstein distance
Wang, J · 2022
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