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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 151–175 of 338 papers

TitleStatusHype
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models—0
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows—0
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model—0
Epistemology of Modeling and Simulation: How can we gain Knowledge from Simulations?—0
Equitability, interval estimation, and statistical power—0
Equivalence of distance-based and RKHS-based statistics in hypothesis testing—0
Exact Post Model Selection Inference for Marginal Screening—0
Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publications—0
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing—0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications—0
From Two Sample Testing to Singular Gaussian Discrimination—0
Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice—0
General Framework for Binary Classification on Top Samples—0
Generalization Error Bounds via mth Central Moments of the Information Density—0
Generalized Binary Search For Split-Neighborly Problems—0
Generalized Multivariate Signs for Nonparametric Hypothesis Testing in High Dimensions—0
Generalized Sliced Distances for Probability Distributions—0
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics—0
Geometric Inference for General High-Dimensional Linear Inverse Problems—0
Geometric tree kernels: Classification of COPD from airway tree geometry—0
Goodness-of-Fit Tests for Inhomogeneous Random Graphs—0
Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures—0
How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?—0
How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?—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