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 201–210 of 338 papers

TitleStatusHype
Learning and Calibrating Per-Location Classifiers for Visual Place Recognition—0
Learning from DPPs via Sampling: Beyond HKPV and symmetry—0
Learning in Implicit Generative Models—0
Leveraging Optimal Transport for Distributed Two-Sample Testing: An Integrated Transportation Distance-based Framework—0
Limit Distribution Theory for the Smooth 1-Wasserstein Distance with Applications—0
Limits of Detecting Text Generated by Large-Scale Language Models—0
Linear Hypothesis Testing in Dense High-Dimensional Linear Models—0
Line-based Camera Pose Estimation in Point Cloud of Structured Environments—0
Locally Private Hypothesis Selection—0
Locally Private 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