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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 101–125 of 338 papers

TitleStatusHype
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning—0
Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publications—0
Exact Post Model Selection Inference for Marginal Screening—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
Equivalence of distance-based and RKHS-based statistics in hypothesis testing—0
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing—0
Equitability, interval estimation, and statistical power—0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications—0
Epistemology of Modeling and Simulation: How can we gain Knowledge from Simulations?—0
Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice—0
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model—0
Bottleneck Problems: Information and Estimation-Theoretic View—0
An explainable deep vision system for animal classification and detection in trail-camera images with automatic post-deployment retraining—0
Generalization Error Bounds via mth Central Moments of the Information Density—0
Generalized Binary Search For Split-Neighborly Problems—0
Generalized Multivariate Signs for Nonparametric Hypothesis Testing in High Dimensions—0
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows—0
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics—0
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity—0
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Goodness-of-Fit Tests for Inhomogeneous Random Graphs—0
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models—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