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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 311–320 of 338 papers

TitleStatusHype
Short-term plasticity as cause-effect hypothesis testing in distal reward learningCode0
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge Discovery—0
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models—0
Hypothesis Testing for Automated Community Detection in Networks—0
Nearly Optimal Sample Size in Hypothesis Testing for High-Dimensional Regression—0
Spatial statistics, image analysis and percolation theory—0
Nonmyopic View Planning for Active Object Detection—0
The Fundamental Learning Problem that Genetic Algorithms with Uniform Crossover Solve Efficiently and Repeatedly As Evolution Proceeds—0
B-tests: Low Variance Kernel Two-Sample TestsCode0
Epistemology of Modeling and Simulation: How can we gain Knowledge from Simulations?—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