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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1–25 of 338 papers

Title	Date	Tasks	Status	Hype	Score
Towards Probabilistic Verification of Machine Unlearning	Mar 9, 2020	backdoor defenseMachine Unlearning	CodeCode Available	1	5
Decision-Making with Auto-Encoding Variational Bayes	Feb 17, 2020	Decision MakingTwo-sample testing	CodeCode Available	1	5
Model Equality Testing: Which Model Is This API Serving?	Oct 26, 2024	modelTwo-sample testing	CodeCode Available	1	5
AutoML Two-Sample Test	Jun 17, 2022	AutoMLscientific discovery	CodeCode Available	1	5
Learning Opinion Dynamics From Social Traces	Jun 2, 2020	Graph MiningLink Sign Prediction	CodeCode Available	1	5
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation	Jun 23, 2020	reinforcement-learningReinforcement Learning	CodeCode Available	1	5
Learning Deep Kernels for Non-Parametric Two-Sample Tests	Feb 21, 2020	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	1	5
MMD Aggregated Two-Sample Test	Oct 28, 2021	TranslationTwo-sample testing	CodeCode Available	1	5
Testing Goodness of Fit of Conditional Density Models with Kernels	Feb 24, 2020	Two-sample testing	CodeCode Available	1	5
Addressing Maximization Bias in Reinforcement Learning with Two-Sample Testing	Jan 20, 2022	Q-Learningreinforcement-learning	CodeCode Available	1	5
Statistical comparison of classifiers through Bayesian hierarchical modelling	Sep 28, 2016	Two-sample testing	CodeCode Available	1	5
Safe Testing	Jun 18, 2019	Two-sample testing	CodeCode Available	1	5
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity	Jun 18, 2020	regressionSymbolic Regression	CodeCode Available	1	5
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting	Feb 24, 2020	parameter estimationTwo-sample testing	CodeCode Available	1	5
Conditional Independence Testing using Generative Adversarial Networks	Jul 9, 2019	Generative Adversarial NetworkTwo-sample testing	CodeCode Available	0	5
Event Outlier Detection in Continuous Time	Dec 19, 2019	Outlier DetectionPoint Processes	CodeCode Available	0	5
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing	Dec 14, 2018	SensitivityTwo-sample testing	CodeCode Available	0	5
Compress Then Test: Powerful Kernel Testing in Near-linear Time	Jan 14, 2023	Two-sample testing	CodeCode Available	0	5
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features	Jul 12, 2024	Two-sample testing	CodeCode Available	0	5
Copy Move Source-Target Disambiguation through Multi-Branch CNNs	Dec 29, 2019	Two-sample testing	CodeCode Available	0	5
A U-statistic Approach to Hypothesis Testing for Structure Discovery in Undirected Graphical Models	Apr 6, 2016	Two-sample testing	CodeCode Available	0	5
Breaking hypothesis testing for failure rates	Jan 13, 2020	Point ProcessesTwo-sample testing	CodeCode Available	0	5
Comparing distributions: _1 geometry improves kernel two-sample testing	Sep 19, 2019	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	0	5
Comparing distributions: _1 geometry improves kernel two-sample testing	Dec 1, 2019	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available	0	5
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm	Jun 9, 2019	3D Medical Imaging SegmentationBrain Segmentation	CodeCode Available	0	5

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All datasets Blob (9 modes, 40 for each)CIFAR-10 vs CIFAR-10.1 (1000 samples)HDGM (d=10, N=4000)HIGGS Data Set MNIST vs Fake MNIST

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	98.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	74.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	65.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	57.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	91	—	Unverified