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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 276–300 of 338 papers

TitleStatusHype
Online Robust Principal Component Analysis with Change Point DetectionCode0
Graphon based Clustering and Testing of Networks: Algorithms and TheoryCode0
The Representation Jensen-Shannon DivergenceCode0
Comparing distributions: _1 geometry improves kernel two-sample testingCode0
Comparing distributions: _1 geometry improves kernel two-sample testingCode0
Understanding Learned Models by Identifying Important Features at the Right ResolutionCode0
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier FeaturesCode0
A Differentially Private Kernel Two-Sample TestCode0
Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation StudyCode0
Conditional Independence Testing using Generative Adversarial NetworksCode0
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective InferenceCode0
Statistical Anomaly Detection via Composite Hypothesis Testing for Markov ModelsCode0
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasksCode0
Breaking hypothesis testing for failure ratesCode0
Towards Safe Policy Improvement for Non-Stationary MDPsCode0
Event Outlier Detection in Continuous TimeCode0
Copy Move Source-Target Disambiguation through Multi-Branch CNNsCode0
On Wasserstein Two Sample Testing and Related Families of Nonparametric TestsCode0
The multilayer random dot product graphCode0
Credal Two-Sample Tests of Epistemic UncertaintyCode0
Sequential Nonparametric Testing with the Law of the Iterated LogarithmCode0
Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word CategoriesCode0
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settingsCode0
Achieving Equalized Odds by Resampling Sensitive AttributesCode0
Meta Two-Sample Testing: Learning Kernels for Testing with Limited DataCode0
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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