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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 301–338 of 338 papers

TitleStatusHype
On Semiparametric Exponential Family Graphical Models—0
Visual Scene Representations: Contrast, Scaling and Occlusion—0
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives—0
Preserving Statistical Validity in Adaptive Data Analysis—0
Significant Subgraph Mining with Multiple Testing Correction—0
Mass-Univariate Hypothesis Testing on MEEG Data using Cross-Validation—0
On the Decreasing Power of Kernel and Distance based Nonparametric Hypothesis Tests in High Dimensions—0
Nonparametric Detection of Anomalous Data Streams—0
Geometric Inference for General High-Dimensional Linear Inverse Problems—0
Exact Post Model Selection Inference for Marginal Screening—0
Short-term plasticity as cause-effect hypothesis testing in distal reward learningCode0
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge Discovery—0
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models—0
Hypothesis Testing for Automated Community Detection in Networks—0
Nearly Optimal Sample Size in Hypothesis Testing for High-Dimensional Regression—0
Spatial statistics, image analysis and percolation theory—0
Nonmyopic View Planning for Active Object Detection—0
The Fundamental Learning Problem that Genetic Algorithms with Uniform Crossover Solve Efficiently and Repeatedly As Evolution Proceeds—0
B-tests: Low Variance Kernel Two-Sample TestsCode0
Epistemology of Modeling and Simulation: How can we gain Knowledge from Simulations?—0
Confidence Intervals and Hypothesis Testing for High-Dimensional Regression—0
Learning and Calibrating Per-Location Classifiers for Visual Place Recognition—0
Testing Hypotheses by Regularized Maximum Mean Discrepancy—0
Markovian models for one dimensional structure estimation on heavily noisy imagery—0
PAC Quasi-automatizability of Resolution over Restricted Distributions—0
Geometric tree kernels: Classification of COPD from airway tree geometry—0
Process, Structure, and Modularity in Reasoning with Uncertainty—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
The Perturbed Variation—0
Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination—0
Hypothesis Testing in Feedforward Networks with Broadcast Failures—0
Measures of Entropy from Data Using Infinitely Divisible Kernels—0
Equivalence of distance-based and RKHS-based statistics in hypothesis testing—0
A powerful and efficient set test for genetic markers that handles confounders—0
Minimax Localization of Structural Information in Large Noisy Matrices—0
A More Powerful Two-Sample Test in High Dimensions using Random Projection—0
A novel family of non-parametric cumulative based divergences for point processes—0
Kernel Change-point Analysis—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