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 21–30 of 338 papers

Title	Date	Tasks	Status	Score
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data	Jul 4, 2018	feature selectionGeneral Classification	CodeCode Available	5
B-tests: Low Variance Kernel Two-Sample Tests	Jul 8, 2013	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Compress Then Test: Powerful Kernel Testing in Near-linear Time	Jan 14, 2023	Two-sample testing	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
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing	Dec 14, 2018	SensitivityTwo-sample testing	CodeCode Available	5
Breaking hypothesis testing for failure rates	Jan 13, 2020	Point ProcessesTwo-sample testing	CodeCode Available	5
A Witness Two-Sample Test	Feb 10, 2021	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Credal Two-Sample Tests of Epistemic Uncertainty	Oct 16, 2024	Two-sample testing	CodeCode Available	5
Classification Logit Two-sample Testing by Neural Networks	Sep 25, 2019	ClassificationGeneral Classification	CodeCode Available	5
A U-statistic Approach to Hypothesis Testing for Structure Discovery in Undirected Graphical Models	Apr 6, 2016	Two-sample testing	CodeCode Available	5

Show:10 25 50

← PrevPage 3 of 34Next →

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