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 176–200 of 338 papers

TitleStatusHype
p-value peeking and estimating extrema—0
Quantum-enhanced barcode decoding and pattern recognition—0
Quickest change detection for multi-task problems under unknown parameters—0
Rapid Online Analysis of Local Feature Detectors and Their Complementarity—0
Reasoning with Memory Augmented Neural Networks for Language Comprehension—0
Reconstruction in the Labeled Stochastic Block Model—0
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels—0
Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis—0
Reverse Euclidean and Gaussian isoperimetric inequalities for parallel sets with applications—0
A Unified Data Representation Learning for Non-parametric Two-sample Testing—0
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption—0
Robust hypothesis testing and distribution estimation in Hellinger distance—0
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets—0
Second-Order Asymptotically Optimal Statistical Classification—0
Selective Inference Approach for Statistically Sound Predictive Pattern Mining—0
Self-Supervised Contextual Bandits in Computer Vision—0
Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective—0
Sequence Preserving Network Traffic Generation—0
Sequential Controlled Sensing for Composite Multihypothesis Testing—0
Sequential Experiment Design for Hypothesis Verification—0
Sequential hypothesis testing in machine learning, and crude oil price jump size detection—0
Sharp Computational-Statistical Phase Transitions via Oracle Computational Model—0
Signature Maximum Mean Discrepancy Two-Sample Statistical Tests—0
Significant Subgraph Mining with Multiple Testing Correction—0
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks—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