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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 101–150 of 338 papers

TitleStatusHype
Classical Statistics and Statistical Learning in Imaging Neuroscience—0
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
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing—0
Equivalence of distance-based and RKHS-based statistics in hypothesis testing—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
Equitability, interval estimation, and statistical power—0
Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice—0
Epistemology of Modeling and Simulation: How can we gain Knowledge from Simulations?—0
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model—0
Bottleneck Problems: Information and Estimation-Theoretic View—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
An explainable deep vision system for animal classification and detection in trail-camera images with automatic post-deployment retraining—0
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics—0
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows—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
Goodness-of-Fit Tests for Inhomogeneous Random Graphs—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models—0
A New Framework for Distance and Kernel-based Metrics in High Dimensions—0
Adversarially Robust Classification based on GLRT—0
Adaptive learning of density ratios in RKHS—0
Active Sequential Two-Sample Testing—0
Early Detection of Long Term Evaluation Criteria in Online Controlled Experiments—0
Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation—0
Distributed Information-Theoretic Clustering—0
Distributed Hypothesis Testing and Social Learning in Finite Time with a Finite Amount of Communication—0
Bayesian Hypothesis Testing for Block Sparse Signal Recovery—0
A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience—0
Distributed Chernoff Test: Optimal decision systems over networks—0
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders—0
Discovering Potential Correlations via Hypercontractivity—0
Dimension-agnostic inference using cross U-statistics—0
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience—0
Adversarial learning for product recommendation—0
Differentially Private False Discovery Rate Control—0
A tutorial on MDL hypothesis testing for graph analysis—0
Detection of Planted Solutions for Flat Satisfiability Problems—0
A More Powerful Two-Sample Test in High Dimensions using Random Projection—0
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms—0
Dealing with Uncertainties in User Feedback: Strategies Between Denying and Accepting—0
Asymptotically Optimal One- and Two-Sample Testing with Kernels—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