Two-sample testing

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53 papers with code • 5 benchmarks • 1 datasets

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.

Benchmarks

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Dataset	Best Model	Results
HIGGS Data Set	MMD-D Learning Deep Kernels for Non-Parametric Two-Sample Tests	1
MNIST vs Fake MNIST	MMD-D Learning Deep Kernels for Non-Parametric Two-Sample Tests	1
Blob (9 modes, 40 for each)	MMD-D Learning Deep Kernels for Non-Parametric Two-Sample Tests	1
HDGM (d=10, N=4000)	MMD-D Learning Deep Kernels for Non-Parametric Two-Sample Tests	1
CIFAR-10 vs CIFAR-10.1 (1000 samples)	MMD-D Learning Deep Kernels for Non-Parametric Two-Sample Tests	1

Papers

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Datasets

HIGGS Data Set

Two-sample testing Edit

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Papers NeurIPS Guidelines

Datasets

Two-sample testing

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Benchmarks

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Papers

NeurIPS Guidelines