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 201–225 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
Local minimax rates for closeness testing of discrete distributions—0
Local Private Hypothesis Testing: Chi-Square Tests—0
Local Variation as a Statistical Hypothesis Test—0
Marginal likelihood computation for model selection and hypothesis testing: an extensive review—0
Markov Boundary Discovery with Ridge Regularized Linear Models—0
Markovian models for one dimensional structure estimation on heavily noisy imagery—0
Mass-Univariate Hypothesis Testing on MEEG Data using Cross-Validation—0
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing—0
Measures of Entropy from Data Using Infinitely Divisible Kernels—0
Measuring and Modeling Language Change—0
Measuring Sample Quality with Kernels—0
Minimax Localization of Structural Information in Large Noisy Matrices—0
Minimax Lower Bounds for Linear Independence Testing—0
Minimax Nonparametric Two-sample Test under Smoothing—0
Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and 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