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 271–280 of 338 papers

Title	Date	Tasks	Status
Classification accuracy as a proxy for two sample testing	Feb 6, 2016	ClassificationGeneral Classification	—Unverified
Minimax Lower Bounds for Linear Independence Testing	Jan 23, 2016	Two-sample testing	—Unverified
Proactive Message Passing on Memory Factor Networks	Jan 18, 2016	Two-sample testing	—Unverified
Sharp Computational-Statistical Phase Transitions via Oracle Computational Model	Dec 30, 2015	Two-sample testing	—Unverified
Unsupervised Feature Construction for Improving Data Representation and Semantics	Dec 17, 2015	Two-sample testing	—Unverified
The p-filter: multi-layer FDR control for grouped hypotheses	Dec 10, 2015	Two-sample testing	—Unverified
Statistical Topological Data Analysis - A Kernel Perspective	Dec 1, 2015	Topological Data AnalysisTwo-sample testing	—Unverified
Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation	Nov 18, 2015	compressed sensingTwo-sample testing	—Unverified
Private False Discovery Rate Control	Nov 12, 2015	Privacy PreservingTwo-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