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 61–70 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
Revisiting Precision and Recall Definition for Generative Model Evaluation	May 14, 2019	Two-sample testing	CodeCode Available	5
Conditional Independence Testing using Generative Adversarial Networks	Jul 9, 2019	Generative Adversarial NetworkTwo-sample testing	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
Event Outlier Detection in Continuous Time	Dec 19, 2019	Outlier DetectionPoint Processes	CodeCode Available	5
Achieving Equalized Odds by Resampling Sensitive Attributes	Jun 8, 2020	AttributeFairness	CodeCode Available	5
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks	Feb 26, 2020	Information RetrievalRetrieval	CodeCode Available	5
Credal Two-Sample Tests of Epistemic Uncertainty	Oct 16, 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
Copy Move Source-Target Disambiguation through Multi-Branch CNNs	Dec 29, 2019	Two-sample testing	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