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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 26–50 of 338 papers

TitleStatusHype
Network two-sample test for block models—0
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances—0
Collaborative non-parametric two-sample testing—0
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison—0
Deep anytime-valid hypothesis testingCode0
A framework for paired-sample hypothesis testing for high-dimensional data—0
On the Exploration of Local Significant Differences For Two-Sample Test—0
Kernel-Based Tests for Likelihood-Free Hypothesis TestingCode0
Adaptive learning of density ratios in RKHS—0
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data SplittingCode0
The Representation Jensen-Shannon DivergenceCode0
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity—0
Multimodal Multi-User Surface Recognition with the Kernel Two-Sample TestCode0
Active Sequential Two-Sample Testing—0
Compress Then Test: Powerful Kernel Testing in Near-linear TimeCode0
A Permutation-free Kernel Two-Sample TestCode0
MMD-B-Fair: Learning Fair Representations with Statistical TestingCode0
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows—0
A label-efficient two-sample testCode0
Graphon based Clustering and Testing of Networks: Algorithms and TheoryCode0
Limit Distribution Theory for the Smooth 1-Wasserstein Distance with Applications—0
Generalized Multivariate Signs for Nonparametric Hypothesis Testing in High Dimensions—0
Meta Two-Sample Testing: Learning Kernels for Testing with Limited DataCode0
Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective—0
A Witness Two-Sample TestCode0
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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