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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 31–40 of 338 papers

Title	Date	Tasks	Status	Score
Credal Two-Sample Tests of Epistemic Uncertainty	Oct 16, 2024	Two-sample testing	CodeCode Available	5
Conditional Independence Testing using Generative Adversarial Networks	Jul 9, 2019	Generative Adversarial NetworkTwo-sample testing	CodeCode Available	5
A Meta-Analysis of the Anomaly Detection Problem	Mar 3, 2015	Anomaly DetectionBenchmarking	CodeCode Available	5
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features	Jul 12, 2024	Two-sample testing	CodeCode Available	5
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings	Apr 24, 2017	AstronomyMarketing	CodeCode Available	5
Classification Logit Two-sample Testing by Neural Networks	Sep 25, 2019	ClassificationGeneral Classification	CodeCode Available	5
A Differentially Private Kernel Two-Sample Test	Aug 1, 2018	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Comparing distributions: _1 geometry improves kernel two-sample testing	Sep 19, 2019	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm	Jun 9, 2019	3D Medical Imaging SegmentationBrain Segmentation	CodeCode Available	5
B-tests: Low Variance Kernel Two-Sample Tests	Jul 8, 2013	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5

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All datasets Blob (9 modes, 40 for each)CIFAR-10 vs CIFAR-10.1 (1000 samples)HDGM (d=10, N=4000)HIGGS Data Set MNIST vs Fake MNIST

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	98.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	74.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	65.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	57.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	91	—	Unverified