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 51–75 of 338 papers

Title	Date	Tasks	Status	Score
B-tests: Low Variance Kernel Two-Sample Tests	Jul 8, 2013	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
PacGAN: The power of two samples in generative adversarial networks	Dec 12, 2017	DiversityTwo-sample testing	CodeCode Available	5
PAPRIKA: Private Online False Discovery Rate Control	Feb 27, 2020	Two-sample testing	CodeCode Available	5
Classification Logit Two-sample Testing by Neural Networks	Sep 25, 2019	ClassificationGeneral Classification	CodeCode Available	5
A Witness Two-Sample Test	Feb 10, 2021	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Revisiting Precision and Recall Definition for Generative Model Evaluation	May 14, 2019	Two-sample testing	CodeCode Available	5
Efficient Nonparametric Smoothness Estimation	May 19, 2016	Two-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
Comparing distributions: _1 geometry improves kernel two-sample testing	Sep 19, 2019	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Comparing distributions: _1 geometry improves kernel two-sample testing	Dec 1, 2019	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Sparse Representation-based Open Set Recognition	May 6, 2017	ClassificationGeneral Classification	CodeCode Available	5
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data	Jul 4, 2018	feature selectionGeneral Classification	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
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings	Apr 24, 2017	AstronomyMarketing	CodeCode Available	5
Achieving Equalized Odds by Resampling Sensitive Attributes	Jun 8, 2020	AttributeFairness	CodeCode Available	5
Deep anytime-valid hypothesis testing	Oct 30, 2023	Adversarial RobustnessTwo-sample testing	CodeCode Available	5
Detecting Rewards Deterioration in Episodic Reinforcement Learning	Oct 22, 2020	reinforcement-learningReinforcement Learning	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
Credal Two-Sample Tests of Epistemic Uncertainty	Oct 16, 2024	Two-sample testing	CodeCode Available	5
Event Outlier Detection in Continuous Time	Dec 19, 2019	Outlier DetectionPoint Processes	CodeCode Available	5
Copy Move Source-Target Disambiguation through Multi-Branch CNNs	Dec 29, 2019	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
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift	Oct 29, 2018	Dimensionality ReductionTwo-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