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
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs	Oct 28, 2020	Two-sample testing	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
A label-efficient two-sample test	Nov 17, 2021	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
A Permutation-free Kernel Two-Sample Test	Nov 27, 2022	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Deep anytime-valid hypothesis testing	Oct 30, 2023	Adversarial RobustnessTwo-sample testing	CodeCode Available	5
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings	Apr 24, 2017	AstronomyMarketing	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
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift	Oct 29, 2018	Dimensionality ReductionTwo-sample testing	CodeCode Available	5
Compress Then Test: Powerful Kernel Testing in Near-linear Time	Jan 14, 2023	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
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features	Jul 12, 2024	Two-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
Comparing distributions: _1 geometry improves kernel two-sample testing	Dec 1, 2019	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Event Outlier Detection in Continuous Time	Dec 19, 2019	Outlier DetectionPoint Processes	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
Conditional Independence Testing using Generative Adversarial Networks	Jul 9, 2019	Generative Adversarial NetworkTwo-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
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data	Jul 4, 2018	feature selectionGeneral Classification	CodeCode Available	5
Detecting Rewards Deterioration in Episodic Reinforcement Learning	Oct 22, 2020	reinforcement-learningReinforcement Learning	CodeCode Available	5
Gaussian Differential Privacy	May 7, 2019	Two-sample testing	CodeCode Available	5
General Frameworks for Conditional Two-Sample Testing	Oct 22, 2024	Domain AdaptationFairness	CodeCode Available	5
Graphon based Clustering and Testing of Networks: Algorithms and Theory	Oct 6, 2021	ClassificationClustering	CodeCode Available	5
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks	Feb 26, 2020	Information RetrievalRetrieval	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