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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 121–130 of 338 papers

TitleStatusHype
Minimax Nonparametric Two-sample Test under Smoothing—0
Training Neural Networks for Likelihood/Density Ratio Estimation—0
Noiseless Privacy—0
Sequential Controlled Sensing for Composite Multihypothesis Testing—0
Universally Consistent K-Sample Tests via Dependence Measures—0
Identification of Model Uncertainty via Optimal Design of Experiments Applied to a Mechanical Press—0
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing—0
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics—0
Two-sample Testing Using Deep LearningCode0
The Power of Batching in Multiple Hypothesis Testing—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