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 76–100 of 338 papers

TitleStatusHype
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model—0
The Lasso with general Gaussian designs with applications to hypothesis testing—0
The multilayer random dot product graphCode0
Learning from DPPs via Sampling: Beyond HKPV and symmetry—0
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems—0
Adversarial learning for product recommendation—0
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions—0
Optimal Statistical Hypothesis Testing for Social Choice—0
On the Learnability of Concepts: With Applications to Comparing Word Embedding Algorithms—0
Reverse Euclidean and Gaussian isoperimetric inequalities for parallel sets with applications—0
Achieving Equalized Odds by Resampling Sensitive AttributesCode0
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning—0
Marginal likelihood computation for model selection and hypothesis testing: an extensive review—0
Stopping criterion for active learning based on deterministic generalization bounds—0
Generalization Error Bounds via mth Central Moments of the Information Density—0
Sequential hypothesis testing in machine learning, and crude oil price jump size detection—0
Counterexamples to the Low-Degree Conjecture—0
Distributed Hypothesis Testing and Social Learning in Finite Time with a Finite Amount of Communication—0
Covariance-Robust Dynamic Watermarking—0
Self-Supervised Contextual Bandits in Computer Vision—0
Generalized Sliced Distances for Probability Distributions—0
PAPRIKA: Private Online False Discovery Rate ControlCode0
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasksCode0
General Framework for Binary Classification on Top Samples—0
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms—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