SOTAVerified

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
MMD Aggregated Two-Sample TestCode1
Safe TestingCode1
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference SettingCode1
Learning Opinion Dynamics From Social TracesCode1
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularityCode1
Towards Probabilistic Verification of Machine UnlearningCode1
Addressing Maximization Bias in Reinforcement Learning with Two-Sample TestingCode1
AutoML Two-Sample TestCode1
Statistical comparison of classifiers through Bayesian hierarchical modellingCode1
Learning Deep Kernels for Non-Parametric Two-Sample TestsCode1
Model Equality Testing: Which Model Is This API Serving?Code1
Testing Goodness of Fit of Conditional Density Models with KernelsCode1
Decision-Making with Auto-Encoding Variational BayesCode1
Expert-Supervised Reinforcement Learning for Offline Policy Learning and EvaluationCode1
Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word CategoriesCode0
A Witness Two-Sample TestCode0
Meta Two-Sample Testing: Learning Kernels for Testing with Limited DataCode0
Kernel Conditional Moment Test via Maximum Moment RestrictionCode0
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and GraphsCode0
A label-efficient two-sample testCode0
hyppo: A Multivariate Hypothesis Testing Python PackageCode0
Detecting Rewards Deterioration in Episodic Reinforcement LearningCode0
Graphon based Clustering and Testing of Networks: Algorithms and TheoryCode0
Interpreting Black Box Models via Hypothesis TestingCode0
Adversarial Sample Detection for Deep Neural Network through Model Mutation TestingCode0
Kernel-Based Tests for Likelihood-Free Hypothesis TestingCode0
Diagonal Discriminant Analysis with Feature Selection for High Dimensional DataCode0
Failing Loudly: An Empirical Study of Methods for Detecting Dataset ShiftCode0
A Permutation-free Kernel Two-Sample TestCode0
Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation StudyCode0
A Test for Shared Patterns in Cross-modal Brain Activation AnalysisCode0
Credal Two-Sample Tests of Epistemic UncertaintyCode0
Deep anytime-valid hypothesis testingCode0
Fast Two-Sample Testing with Analytic Representations of Probability MeasuresCode0
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier FeaturesCode0
A Differentially Private Kernel Two-Sample TestCode0
Conditional Independence Testing using Generative Adversarial NetworksCode0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
A Meta-Analysis of the Anomaly Detection ProblemCode0
Copy Move Source-Target Disambiguation through Multi-Branch CNNsCode0
Comparing distributions: _1 geometry improves kernel two-sample testingCode0
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settingsCode0
A U-statistic Approach to Hypothesis Testing for Structure Discovery in Undirected Graphical ModelsCode0
B-tests: Low Variance Kernel Two-Sample TestsCode0
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasksCode0
Efficient Nonparametric Smoothness EstimationCode0
General Frameworks for Conditional Two-Sample TestingCode0
Generative Moment Matching NetworksCode0
Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and QuantificationCode0
Classification Logit Two-sample Testing by Neural NetworksCode0
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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