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
Credal Two-Sample Tests of Epistemic UncertaintyCode0
Achieving Equalized Odds by Resampling Sensitive AttributesCode0
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier FeaturesCode0
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settingsCode0
A Meta-Analysis of the Anomaly Detection ProblemCode0
Fast Two-Sample Testing with Analytic Representations of Probability MeasuresCode0
Interpreting Black Box Models via Hypothesis TestingCode0
hyppo: A Multivariate Hypothesis Testing Python PackageCode0
A Test for Shared Patterns in Cross-modal Brain Activation AnalysisCode0
A powerful and efficient set test for genetic markers that handles confounders—0
Communication and Memory Efficient Testing of Discrete Distributions—0
A framework for paired-sample hypothesis testing for high-dimensional data—0
Collaborative non-parametric two-sample testing—0
Closing the AI Knowledge Gap—0
A novel family of non-parametric cumulative based divergences for point processes—0
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks—0
A Flexible Framework for Hypothesis Testing in High-dimensions—0
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing—0
Classification accuracy as a proxy for two sample testing—0
Classical Statistics and Statistical Learning in Imaging Neuroscience—0
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning—0
Can A User Anticipate What Her Followers Want?—0
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing—0
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model—0
Bottleneck Problems: Information and Estimation-Theoretic View—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