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 41–50 of 338 papers

Title	Date	Tasks	Status
A More Powerful Two-Sample Test in High Dimensions using Random Projection	Aug 11, 2011	Two-sample testing	—Unverified
A tutorial on MDL hypothesis testing for graph analysis	Oct 31, 2018	Two-sample testing	—Unverified
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience	Mar 14, 2019	Two-sample testing	—Unverified
A Mean-Field Theory for Kernel Alignment with Random Features in Generative and Discriminative Models	Sep 25, 2019	Two-sample testing	—Unverified
Bayesian Hypothesis Testing for Block Sparse Signal Recovery	Aug 22, 2015	Two-sample testing	—Unverified
Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation	Nov 18, 2015	compressed sensingTwo-sample testing	—Unverified
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models	Jul 1, 2019	ChunkingTwo-sample testing	—Unverified
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity	Apr 26, 2023	Two-sample testingVocal Bursts Valence Prediction	—Unverified
Bottleneck Problems: Information and Estimation-Theoretic View	Nov 12, 2020	LEMMATwo-sample testing	—Unverified
A Sparse Linear Model and Significance Test for Individual Consumption Prediction	Nov 5, 2015	PredictionTwo-sample testing	—Unverified

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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