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 101–125 of 338 papers

TitleStatusHype
Sequence Preserving Network Traffic Generation—0
Locally Private Hypothesis Selection—0
Kernel Conditional Moment Test via Maximum Moment RestrictionCode0
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models—0
Limits of Detecting Text Generated by Large-Scale Language Models—0
Two-Sample Testing for Event Impacts in Time SeriesCode0
Modelling and Quantifying Membership Information Leakage in Machine Learning—0
Tight Regret Bounds for Noisy Optimization of a Brownian Motion—0
Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice—0
Breaking hypothesis testing for failure ratesCode0
Goodness-of-Fit Tests for Inhomogeneous Random Graphs—0
Copy Move Source-Target Disambiguation through Multi-Branch CNNsCode0
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creepCode0
Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities—0
Event Outlier Detection in Continuous TimeCode0
Statistical Testing on ASR Performance via Blockwise Bootstrap—0
The power of synergy in differential privacy: Combining a small curator with local randomizers—0
Comparing distributions: _1 geometry improves kernel two-sample testingCode0
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models—0
Line-based Camera Pose Estimation in Point Cloud of Structured Environments—0
Minimax Nonparametric Two-sample Test under Smoothing—0
Training Neural Networks for Likelihood/Density Ratio Estimation—0
Noiseless Privacy—0
Sequential Controlled Sensing for Composite Multihypothesis Testing—0
Universally Consistent K-Sample Tests via Dependence Measures—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