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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 1–50 of 338 papers

TitleStatusHype
Leveraging Optimal Transport for Distributed Two-Sample Testing: An Integrated Transportation Distance-based Framework—0
Signature Maximum Mean Discrepancy Two-Sample Statistical Tests—0
From Two Sample Testing to Singular Gaussian Discrimination—0
Advanced Tutorial: Label-Efficient Two-Sample Tests—0
Optimal Algorithms for Augmented Testing of Discrete Distributions—0
A Unified Data Representation Learning for Non-parametric Two-sample Testing—0
Minimax Optimal Two-Sample Testing under Local Differential PrivacyCode0
Model Equality Testing: Which Model Is This API Serving?Code1
General Frameworks for Conditional Two-Sample TestingCode0
Credal Two-Sample Tests of Epistemic UncertaintyCode0
Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation StudyCode0
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier FeaturesCode0
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
AutoML Two-Sample TestCode1
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows—0
Addressing Maximization Bias in Reinforcement Learning with Two-Sample TestingCode1
A label-efficient two-sample testCode0
MMD Aggregated Two-Sample TestCode1
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
Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective—0
Meta Two-Sample Testing: Learning Kernels for Testing with Limited DataCode0
A Witness Two-Sample TestCode0
Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications—0
Understanding Classifiers with Generative Models—0
Quickest change detection for multi-task problems under unknown parameters—0
A General Framework for Distributed Inference with Uncertain Models—0
Adversarially Robust Classification based on GLRT—0
Policy design in experiments with unknown interference—0
Bottleneck Problems: Information and Estimation-Theoretic View—0
Dimension-agnostic inference using cross U-statistics—0
Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects—0
Robust hypothesis testing and distribution estimation in Hellinger distance—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