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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 26–50 of 338 papers

TitleStatusHype
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning—0
A Flexible Framework for Hypothesis Testing in High-dimensions—0
A novel family of non-parametric cumulative based divergences for point processes—0
A framework for paired-sample hypothesis testing for high-dimensional data—0
A powerful and efficient set test for genetic markers that handles confounders—0
Adversarial learning for product recommendation—0
Closing the AI Knowledge Gap—0
Adaptive Concentration Inequalities for Sequential Decision Problems—0
Advanced Tutorial: Label-Efficient Two-Sample Tests—0
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks—0
Collaborative non-parametric two-sample testing—0
A Structured Review of the Validity of BLEU—0
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation—0
Asymptotically Optimal One- and Two-Sample Testing with Kernels—0
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms—0
A More Powerful Two-Sample Test in High Dimensions using Random Projection—0
A tutorial on MDL hypothesis testing for graph analysis—0
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience—0
A Mean-Field Theory for Kernel Alignment with Random Features in Generative and Discriminative Models—0
Bayesian Hypothesis Testing for Block Sparse Signal Recovery—0
Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation—0
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models—0
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity—0
Bottleneck Problems: Information and Estimation-Theoretic View—0
A Sparse Linear Model and Significance Test for Individual Consumption Prediction—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