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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 181–190 of 338 papers

TitleStatusHype
Reconstruction in the Labeled Stochastic Block Model—0
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels—0
Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis—0
Reverse Euclidean and Gaussian isoperimetric inequalities for parallel sets with applications—0
A Unified Data Representation Learning for Non-parametric Two-sample Testing—0
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption—0
Robust hypothesis testing and distribution estimation in Hellinger distance—0
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets—0
Second-Order Asymptotically Optimal Statistical Classification—0
Selective Inference Approach for Statistically Sound Predictive Pattern Mining—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