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 41–50 of 338 papers

Title	Date	Tasks	Status	Score
Comparing distributions: _1 geometry improves kernel two-sample testing	Dec 1, 2019	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings	Apr 24, 2017	AstronomyMarketing	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
B-tests: Low Variance Kernel Two-Sample Tests	Jul 8, 2013	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks	Feb 26, 2020	Information RetrievalRetrieval	CodeCode Available	5
Efficient Nonparametric Smoothness Estimation	May 19, 2016	Two-sample testing	CodeCode Available	5
General Frameworks for Conditional Two-Sample Testing	Oct 22, 2024	Domain AdaptationFairness	CodeCode Available	5
Generative Moment Matching Networks	Feb 10, 2015	Generative Adversarial NetworkTwo-sample testing	CodeCode Available	5
Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and Quantification	Mar 29, 2016	Brain DecodingModel Selection	CodeCode Available	5
Classification Logit Two-sample Testing by Neural Networks	Sep 25, 2019	ClassificationGeneral Classification	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