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 191–200 of 338 papers

Title	Date	Tasks	Status
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift	Oct 29, 2018	Dimensionality ReductionTwo-sample testing	CodeCode Available
Policy Design for Active Sequential Hypothesis Testing using Deep Learning	Oct 11, 2018	Deep LearningDeep Reinforcement Learning	—Unverified
A Simple Way to Deal with Cherry-picking	Oct 11, 2018	Selection biasTwo-sample testing	—Unverified
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop	Oct 1, 2018	Two-sample testingWord Embeddings	—Unverified
Cross-situational learning of large lexicons with finite memory	Sep 28, 2018	Two-sample testing	—Unverified
Distributed Chernoff Test: Optimal decision systems over networks	Sep 12, 2018	Decision MakingQuantization	—Unverified
Multi-level hypothesis testing for populations of heterogeneous networks	Sep 7, 2018	Anomaly DetectionTwo-sample testing	—Unverified
A Structured Review of the Validity of BLEU	Sep 1, 2018	DiagnosticMachine Translation	—Unverified
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain	Aug 6, 2018	Two-sample testing	—Unverified
A Differentially Private Kernel Two-Sample Test	Aug 1, 2018	Two-sample testingVocal Bursts Valence Prediction	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