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 301–310 of 338 papers

Title	Date	Tasks	Status
A U-statistic Approach to Hypothesis Testing for Structure Discovery in Undirected Graphical Models	Apr 6, 2016	Two-sample testing	CodeCode Available
Deep anytime-valid hypothesis testing	Oct 30, 2023	Adversarial RobustnessTwo-sample testing	CodeCode Available
hyppo: A Multivariate Hypothesis Testing Python Package	Jul 3, 2019	Two-sample testing	CodeCode Available
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data	Jul 4, 2018	feature selectionGeneral Classification	CodeCode Available
Short-term plasticity as cause-effect hypothesis testing in distal reward learning	Feb 4, 2014	Two-sample testing	CodeCode Available
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing	Dec 14, 2018	SensitivityTwo-sample testing	CodeCode Available
Revisiting Precision and Recall Definition for Generative Model Evaluation	May 14, 2019	Two-sample testing	CodeCode Available
Minimax Optimal Two-Sample Testing under Local Differential Privacy	Nov 13, 2024	Two-sample testing	CodeCode Available
PacGAN: The power of two samples in generative adversarial networks	Dec 12, 2017	DiversityTwo-sample testing	CodeCode Available
Variational Autoencoders for New Physics Mining at the Large Hadron Collider	Nov 26, 2018	Two-sample testing	CodeCode Available

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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