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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 201–210 of 338 papers

TitleStatusHype
Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations—0
Differentially Private False Discovery Rate Control—0
Diagonal Discriminant Analysis with Feature Selection for High Dimensional DataCode0
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data—0
Local Private Hypothesis Testing: Chi-Square Tests—0
Locally Private Hypothesis Testing—0
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference—0
Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures—0
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels—0
The Exact Equivalence of Distance and Kernel Methods for 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