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 81–90 of 338 papers

TitleStatusHype
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Kernel Hypothesis Testing with Set-valued Data—0
A More Powerful Two-Sample Test in High Dimensions using Random Projection—0
Detection of Planted Solutions for Flat Satisfiability Problems—0
A tutorial on MDL hypothesis testing for graph analysis—0
Differentially Private False Discovery Rate Control—0
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images—0
Discovering Potential Correlations via Hypercontractivity—0
A General Framework for Distributed Inference with Uncertain Models—0
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing—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