SOTAVerified

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 61–70 of 338 papers

TitleStatusHype
A powerful and efficient set test for genetic markers that handles confounders—0
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders—0
A Sparse Linear Model and Significance Test for Individual Consumption Prediction—0
Adaptive Active Hypothesis Testing under Limited Information—0
Covariance-Robust Dynamic Watermarking—0
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models—0
A Simple Way to Deal with Cherry-picking—0
Confidence Intervals and Hypothesis Testing for High-Dimensional Regression—0
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop—0
Kernel Hypothesis Testing with Set-valued Data—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