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 51–60 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
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing	Dec 14, 2018	SensitivityTwo-sample testing	CodeCode Available	5
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks	Feb 26, 2020	Information RetrievalRetrieval	CodeCode Available	5
Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study	Sep 12, 2024	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
A Witness Two-Sample Test	Feb 10, 2021	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creep	Dec 28, 2019	BIG-bench Machine LearningMarketing	CodeCode Available	5
MMD-B-Fair: Learning Fair Representations with Statistical Testing	Nov 15, 2022	Representation LearningTwo-sample testing	CodeCode Available	5
A Permutation-free Kernel Two-Sample Test	Nov 27, 2022	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Achieving Equalized Odds by Resampling Sensitive Attributes	Jun 8, 2020	AttributeFairness	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