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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

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Showing 301–310 of 338 papers

TitleStatusHype
On Semiparametric Exponential Family Graphical Models—0
Visual Scene Representations: Contrast, Scaling and Occlusion—0
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives—0
Preserving Statistical Validity in Adaptive Data Analysis—0
Significant Subgraph Mining with Multiple Testing Correction—0
Mass-Univariate Hypothesis Testing on MEEG Data using Cross-Validation—0
On the Decreasing Power of Kernel and Distance based Nonparametric Hypothesis Tests in High Dimensions—0
Nonparametric Detection of Anomalous Data Streams—0
Geometric Inference for General High-Dimensional Linear Inverse Problems—0
Exact Post Model Selection Inference for Marginal Screening—0
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All datasetsBlob (9 modes, 40 for each)CIFAR-10 vs CIFAR-10.1 (1000 samples)HDGM (d=10, N=4000)HIGGS Data SetMNIST vs Fake MNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy98.5—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy74.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy65.9—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy57.9—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy91—Unverified