Two-sample testing

In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant. The statistics used in two-sample tests can be used to solve many machine learning problems, such as domain adaptation, covariate shift and generative adversarial networks.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 326–338 of 338 papers

Title	Date	Tasks	Status
Geometric tree kernels: Classification of COPD from airway tree geometry	Mar 29, 2013	ClassificationGeneral Classification	—Unverified
Process, Structure, and Modularity in Reasoning with Uncertainty	Mar 27, 2013	Decision MakingManagement	—Unverified
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory	Jan 17, 2013	Model Selectionregression	—Unverified
The Perturbed Variation	Dec 1, 2012	Two-sample testing	—Unverified
Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination	Dec 1, 2012	Two-sample testing	—Unverified
Hypothesis Testing in Feedforward Networks with Broadcast Failures	Nov 19, 2012	Two-sample testing	—Unverified
Measures of Entropy from Data Using Infinitely Divisible Kernels	Nov 11, 2012	ClusteringDimensionality Reduction	—Unverified
Equivalence of distance-based and RKHS-based statistics in hypothesis testing	Jul 25, 2012	Two-sample testing	—Unverified
A powerful and efficient set test for genetic markers that handles confounders	May 3, 2012	Two-sample testing	—Unverified
Minimax Localization of Structural Information in Large Noisy Matrices	Dec 1, 2011	ClusteringTwo-sample testing	—Unverified
A More Powerful Two-Sample Test in High Dimensions using Random Projection	Aug 11, 2011	Two-sample testing	—Unverified
A novel family of non-parametric cumulative based divergences for point processes	Dec 1, 2010	Point ProcessesTwo-sample testing	—Unverified
Kernel Change-point Analysis	Dec 1, 2008	Two-sample testing	—Unverified

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All datasets Blob (9 modes, 40 for each)CIFAR-10 vs CIFAR-10.1 (1000 samples)HDGM (d=10, N=4000)HIGGS Data Set MNIST vs Fake MNIST

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	98.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	74.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	65.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	57.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	91	—	Unverified