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 251–275 of 338 papers

TitleStatusHype
Phase Transitions for High Dimensional Clustering and Related Problems—0
Policy design in experiments with unknown interference—0
Policy Design for Active Sequential Hypothesis Testing using Deep Learning—0
Preserving Statistical Validity in Adaptive Data Analysis—0
Private False Discovery Rate Control—0
Priv’IT: Private and Sample Efficient Identity Testing—0
Proactive Message Passing on Memory Factor Networks—0
Process, Structure, and Modularity in Reasoning with Uncertainty—0
p-value peeking and estimating extrema—0
Quantum-enhanced barcode decoding and pattern recognition—0
Quickest change detection for multi-task problems under unknown parameters—0
Rapid Online Analysis of Local Feature Detectors and Their Complementarity—0
Reasoning with Memory Augmented Neural Networks for Language Comprehension—0
Reconstruction in the Labeled Stochastic Block Model—0
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels—0
Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis—0
Reverse Euclidean and Gaussian isoperimetric inequalities for parallel sets with applications—0
A Unified Data Representation Learning for Non-parametric Two-sample Testing—0
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption—0
B-tests: Low Variance Kernel Two-Sample TestsCode0
Nonzero-sum Adversarial Hypothesis Testing GamesCode0
Generative Moment Matching NetworksCode0
Priv'IT: Private and Sample Efficient Identity TestingCode0
Classification Logit Two-sample Testing by Neural NetworksCode0
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean DiscrepancyCode0
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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