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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 221–230 of 338 papers

TitleStatusHype
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference—0
The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing—0
Universally Consistent K-Sample Tests via Dependence Measures—0
The Fundamental Learning Problem that Genetic Algorithms with Uniform Crossover Solve Efficiently and Repeatedly As Evolution Proceeds—0
The Lasso with general Gaussian designs with applications to hypothesis testing—0
The Perturbed Variation—0
The p-filter: multi-layer FDR control for grouped hypotheses—0
The Power of Batching in Multiple Hypothesis Testing—0
The power of synergy in differential privacy: Combining a small curator with local randomizers—0
The Role of Interactivity in Local Differential Privacy—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