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 281–290 of 338 papers

Title	Date	Tasks	Status
Understanding Learned Models by Identifying Important Features at the Right Resolution	Nov 18, 2018	Two-sample testing	CodeCode Available
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features	Jul 12, 2024	Two-sample testing	CodeCode Available
A Differentially Private Kernel Two-Sample Test	Aug 1, 2018	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available
Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study	Sep 12, 2024	Two-sample testing	CodeCode Available
Conditional Independence Testing using Generative Adversarial Networks	Jul 9, 2019	Generative Adversarial NetworkTwo-sample testing	CodeCode Available
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference	Oct 5, 2020	Computational EfficiencyImage Segmentation	CodeCode Available
Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models	Feb 27, 2017	Anomaly DetectionTwo-sample testing	CodeCode Available
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks	Feb 26, 2020	Information RetrievalRetrieval	CodeCode Available
Breaking hypothesis testing for failure rates	Jan 13, 2020	Point ProcessesTwo-sample testing	CodeCode Available
Towards Safe Policy Improvement for Non-Stationary MDPs	Oct 23, 2020	Decision Makingreinforcement-learning	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