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 271–280 of 338 papers

Title	Date	Tasks	Status
Nonzero-sum Adversarial Hypothesis Testing Games	Sep 28, 2019	ClassificationGeneral Classification	CodeCode Available
Generative Moment Matching Networks	Feb 10, 2015	Generative Adversarial NetworkTwo-sample testing	CodeCode Available
Priv'IT: Private and Sample Efficient Identity Testing	Mar 29, 2017	Two-sample testingVocal Bursts Type Prediction	CodeCode Available
Classification Logit Two-sample Testing by Neural Networks	Sep 25, 2019	ClassificationGeneral Classification	CodeCode Available
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy	Oct 6, 2020	DiversityDocument Summarization	CodeCode Available
Online Robust Principal Component Analysis with Change Point Detection	Feb 19, 2017	Change Point DetectionTwo-sample testing	CodeCode Available
Graphon based Clustering and Testing of Networks: Algorithms and Theory	Oct 6, 2021	ClassificationClustering	CodeCode Available
The Representation Jensen-Shannon Divergence	May 25, 2023	Domain AdaptationTwo-sample testing	CodeCode Available
Comparing distributions: _1 geometry improves kernel two-sample testing	Sep 19, 2019	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available
Comparing distributions: _1 geometry improves kernel two-sample testing	Dec 1, 2019	Two-sample testingVocal Bursts Valence Prediction	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