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 301–325 of 338 papers

TitleStatusHype
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing—0
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop—0
Confidence Intervals and Hypothesis Testing for High-Dimensional Regression—0
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models—0
Contextual Online False Discovery Rate Control—0
Counterexamples to the Low-Degree Conjecture—0
Covariance-Robust Dynamic Watermarking—0
Cross-situational learning of large lexicons with finite memory—0
Ctrl-Z: Recovering from Instability in Reinforcement Learning—0
Dealing with Uncertainties in User Feedback: Strategies Between Denying and Accepting—0
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Detection of Planted Solutions for Flat Satisfiability Problems—0
Differentially Private False Discovery Rate Control—0
Dimension-agnostic inference using cross U-statistics—0
Discovering Potential Correlations via Hypercontractivity—0
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders—0
Distributed Chernoff Test: Optimal decision systems over networks—0
Distributed Hypothesis Testing and Social Learning in Finite Time with a Finite Amount of Communication—0
Distributed Information-Theoretic Clustering—0
Early Detection of Long Term Evaluation Criteria in Online Controlled Experiments—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models—0
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows—0
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model—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