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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 76–100 of 338 papers

TitleStatusHype
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation—0
Cross-situational learning of large lexicons with finite memory—0
Ctrl-Z: Recovering from Instability in Reinforcement Learning—0
Asymptotically Optimal One- and Two-Sample Testing with Kernels—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
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images—0
A More Powerful Two-Sample Test in High Dimensions using Random Projection—0
Detection of Planted Solutions for Flat Satisfiability Problems—0
A tutorial on MDL hypothesis testing for graph analysis—0
Differentially Private False Discovery Rate Control—0
Dimension-agnostic inference using cross U-statistics—0
Discovering Potential Correlations via Hypercontractivity—0
A General Framework for Distributed Inference with Uncertain Models—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
Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation—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
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity—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
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing—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