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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

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Showing 321–330 of 338 papers

TitleStatusHype
Confidence Intervals and Hypothesis Testing for High-Dimensional Regression—0
Learning and Calibrating Per-Location Classifiers for Visual Place Recognition—0
Testing Hypotheses by Regularized Maximum Mean Discrepancy—0
Markovian models for one dimensional structure estimation on heavily noisy imagery—0
PAC Quasi-automatizability of Resolution over Restricted Distributions—0
Geometric tree kernels: Classification of COPD from airway tree geometry—0
Process, Structure, and Modularity in Reasoning with Uncertainty—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
The Perturbed Variation—0
Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination—0
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All datasetsBlob (9 modes, 40 for each)CIFAR-10 vs CIFAR-10.1 (1000 samples)HDGM (d=10, N=4000)HIGGS Data SetMNIST vs Fake MNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy98.5—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy74.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy65.9—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy57.9—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy91—Unverified