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 251–260 of 338 papers

Title	Date	Tasks	Status	Hype
Reasoning with Memory Augmented Neural Networks for Language Comprehension	Oct 20, 2016	Reading ComprehensionTwo-sample testing	—Unverified	0
Two-sample testing in non-sparse high-dimensional linear models	Oct 14, 2016	regressionTwo-sample testing	—Unverified	0
Learning in Implicit Generative Models	Oct 11, 2016	Density Ratio EstimationEconometrics	—Unverified	0
Linear Hypothesis Testing in Dense High-Dimensional Linear Models	Oct 10, 2016	regressionTwo-sample testing	—Unverified	0
Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing	Oct 4, 2016	Two-sample testing	—Unverified	0
Statistical comparison of classifiers through Bayesian hierarchical modelling	Sep 28, 2016	Two-sample testing	CodeCode Available	1
Information Theoretic Structure Learning with Confidence	Sep 13, 2016	Two-sample testing	—Unverified	0
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks	Aug 2, 2016	Anomaly DetectionTime Series	—Unverified	0
A review of Gaussian Markov models for conditional independence	Jun 23, 2016	Model SelectionTwo-sample testing	—Unverified	0
Kernel Mean Embedding of Distributions: A Review and Beyond	May 31, 2016	Causal DiscoveryReinforcement Learning	—Unverified	0

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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