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 71–80 of 338 papers

Title	Date	Tasks	Status	Score
Gaussian Differential Privacy	May 7, 2019	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
A Differentially Private Kernel Two-Sample Test	Aug 1, 2018	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	5
Credal Two-Sample Tests of Epistemic Uncertainty	Oct 16, 2024	Two-sample testing	CodeCode Available	5
Interpreting Black Box Models via Hypothesis Testing	Mar 29, 2019	Two-sample testing	CodeCode Available	5
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs	Oct 28, 2020	Two-sample testing	CodeCode Available	5
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings	Apr 24, 2017	AstronomyMarketing	CodeCode Available	5
Nonzero-sum Adversarial Hypothesis Testing Games	Sep 28, 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