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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
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