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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 276–300 of 338 papers

Title	Date	Tasks	Status
The p-filter: multi-layer FDR control for grouped hypotheses	Dec 10, 2015	Two-sample testing	—Unverified
Statistical Topological Data Analysis - A Kernel Perspective	Dec 1, 2015	Topological Data AnalysisTwo-sample testing	—Unverified
Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation	Nov 18, 2015	compressed sensingTwo-sample testing	—Unverified
Private False Discovery Rate Control	Nov 12, 2015	Privacy PreservingTwo-sample testing	—Unverified
A Sparse Linear Model and Significance Test for Individual Consumption Prediction	Nov 5, 2015	PredictionTwo-sample testing	—Unverified
Rapid Online Analysis of Local Feature Detectors and Their Complementarity	Oct 17, 2015	Two-sample testing	—Unverified
How to Formulate and Solve Statistical Recognition and Learning Problems	Sep 29, 2015	Two-sample testing	—Unverified
Markov Boundary Discovery with Ridge Regularized Linear Models	Sep 14, 2015	Causal InferenceDimensionality Reduction	—Unverified
On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests	Sep 8, 2015	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available
Iterative hypothesis testing for multi-object tracking in presence of features with variable reliability	Sep 1, 2015	Multi-Object TrackingObject Tracking	—Unverified
Wald-Kernel: Learning to Aggregate Information for Sequential Inference	Aug 31, 2015	Decision MakingTwo-sample testing	—Unverified
Bayesian Hypothesis Testing for Block Sparse Signal Recovery	Aug 22, 2015	Two-sample testing	—Unverified
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing	Aug 4, 2015	Two-sample testing	—Unverified
Fast Two-Sample Testing with Analytic Representations of Probability Measures	Jun 15, 2015	Two-sample testingVocal Bursts Valence Prediction	CodeCode Available
Sequential Nonparametric Testing with the Law of the Iterated Logarithm	Jun 10, 2015	Two-sample testing	CodeCode Available
Equitability, interval estimation, and statistical power	May 9, 2015	Two-sample testing	—Unverified
Local Variation as a Statistical Hypothesis Test	Apr 24, 2015	Two-sample testing	—Unverified
A Meta-Analysis of the Anomaly Detection Problem	Mar 3, 2015	Anomaly DetectionBenchmarking	CodeCode Available
Phase Transitions for High Dimensional Clustering and Related Problems	Feb 24, 2015	ClusteringTwo-sample testing	—Unverified
Improved Sum-of-Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems	Feb 23, 2015	Two-sample testing	—Unverified
Detection of Planted Solutions for Flat Satisfiability Problems	Feb 21, 2015	Two-sample testing	—Unverified
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing	Feb 15, 2015	Two-sample testing	—Unverified
Speeding up Permutation Testing in Neuroimaging	Feb 12, 2015	Matrix CompletionTwo-sample testing	—Unverified
Reconstruction in the Labeled Stochastic Block Model	Feb 11, 2015	modelStochastic Block Model	—Unverified
Generative Moment Matching Networks	Feb 10, 2015	Generative Adversarial NetworkTwo-sample testing	CodeCode Available

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All datasets Blob (9 modes, 40 for each)CIFAR-10 vs CIFAR-10.1 (1000 samples)HDGM (d=10, N=4000)HIGGS Data Set MNIST vs Fake MNIST

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	98.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	74.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	65.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	57.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMD-D	Avg accuracy	91	—	Unverified