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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 301–338 of 338 papers

TitleStatusHype
Deep anytime-valid hypothesis testingCode0
hyppo: A Multivariate Hypothesis Testing Python PackageCode0
Diagonal Discriminant Analysis with Feature Selection for High Dimensional DataCode0
Short-term plasticity as cause-effect hypothesis testing in distal reward learningCode0
Adversarial Sample Detection for Deep Neural Network through Model Mutation TestingCode0
Revisiting Precision and Recall Definition for Generative Model EvaluationCode0
Minimax Optimal Two-Sample Testing under Local Differential PrivacyCode0
PacGAN: The power of two samples in generative adversarial networksCode0
Variational Autoencoders for New Physics Mining at the Large Hadron ColliderCode0
Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and QuantificationCode0
Detecting Rewards Deterioration in Episodic Reinforcement LearningCode0
Interpreting Black Box Models via Hypothesis TestingCode0
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and GraphsCode0
A Permutation-free Kernel Two-Sample TestCode0
Efficient Nonparametric Smoothness EstimationCode0
MMD-B-Fair: Learning Fair Representations with Statistical TestingCode0
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data SplittingCode0
Kernel-Based Tests for Likelihood-Free Hypothesis TestingCode0
A Witness Two-Sample TestCode0
Kernel Conditional Moment Test via Maximum Moment RestrictionCode0
PAPRIKA: Private Online False Discovery Rate ControlCode0
A Test for Shared Patterns in Cross-modal Brain Activation AnalysisCode0
Two-sample Testing Using Deep LearningCode0
Failing Loudly: An Empirical Study of Methods for Detecting Dataset ShiftCode0
Multimodal Multi-User Surface Recognition with the Kernel Two-Sample TestCode0
Fast Two-Sample Testing with Analytic Representations of Probability MeasuresCode0
A label-efficient two-sample testCode0
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial ExamplesCode0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
Sparse Representation-based Open Set RecognitionCode0
Gaussian Differential PrivacyCode0
Two-Sample Testing for Event Impacts in Time SeriesCode0
General Frameworks for Conditional Two-Sample TestingCode0
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creepCode0
Practical methods for graph two-sample testingCode0
A Meta-Analysis of the Anomaly Detection ProblemCode0
Scalable and Efficient Hypothesis Testing with Random ForestsCode0
Nonzero-sum Adversarial Hypothesis Testing GamesCode0
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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