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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 126–150 of 338 papers

TitleStatusHype
Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures—0
A New Framework for Distance and Kernel-based Metrics in High Dimensions—0
Adversarially Robust Classification based on GLRT—0
Adaptive learning of density ratios in RKHS—0
Active Sequential Two-Sample Testing—0
Early Detection of Long Term Evaluation Criteria in Online Controlled Experiments—0
Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation—0
Distributed Information-Theoretic Clustering—0
Distributed Hypothesis Testing and Social Learning in Finite Time with a Finite Amount of Communication—0
Bayesian Hypothesis Testing for Block Sparse Signal Recovery—0
A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience—0
Distributed Chernoff Test: Optimal decision systems over networks—0
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders—0
Discovering Potential Correlations via Hypercontractivity—0
Dimension-agnostic inference using cross U-statistics—0
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience—0
Adversarial learning for product recommendation—0
Differentially Private False Discovery Rate Control—0
A tutorial on MDL hypothesis testing for graph analysis—0
Detection of Planted Solutions for Flat Satisfiability Problems—0
A More Powerful Two-Sample Test in High Dimensions using Random Projection—0
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms—0
Dealing with Uncertainties in User Feedback: Strategies Between Denying and Accepting—0
Asymptotically Optimal One- and Two-Sample Testing with Kernels—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