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 26–50 of 338 papers

Title	Date	Tasks	Status	Score
Kernel-Based Tests for Likelihood-Free Hypothesis Testing	Aug 17, 2023	Binary ClassificationTwo-sample testing	CodeCode Available	5
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data	Jul 4, 2018	feature selectionGeneral Classification	CodeCode Available	5
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift	Oct 29, 2018	Dimensionality ReductionTwo-sample testing	CodeCode Available	5
A Permutation-free Kernel Two-Sample Test	Nov 27, 2022	Two-sample testingVocal Bursts Valence Prediction	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
A Test for Shared Patterns in Cross-modal Brain Activation Analysis	Oct 8, 2019	Two-sample testing	CodeCode Available	5
Credal Two-Sample Tests of Epistemic Uncertainty	Oct 16, 2024	Two-sample testing	CodeCode Available	5
Deep anytime-valid hypothesis testing	Oct 30, 2023	Adversarial RobustnessTwo-sample testing	CodeCode Available	5
Fast Two-Sample Testing with Analytic Representations of Probability Measures	Jun 15, 2015	Two-sample testingVocal Bursts Valence Prediction	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 Differentially Private Kernel Two-Sample Test	Aug 1, 2018	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Conditional Independence Testing using Generative Adversarial Networks	Jul 9, 2019	Generative Adversarial NetworkTwo-sample testing	CodeCode Available	5
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm	Jun 9, 2019	3D Medical Imaging SegmentationBrain Segmentation	CodeCode Available	5
A Meta-Analysis of the Anomaly Detection Problem	Mar 3, 2015	Anomaly DetectionBenchmarking	CodeCode Available	5
Copy Move Source-Target Disambiguation through Multi-Branch CNNs	Dec 29, 2019	Two-sample testing	CodeCode Available	5
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