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 151–160 of 338 papers

Title	Date	Tasks	Status
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems	Jul 7, 2020	reinforcement-learningReinforcement Learning (RL)	—Unverified
Iterative hypothesis testing for multi-object tracking in presence of features with variable reliability	Sep 1, 2015	Multi-Object TrackingObject Tracking	—Unverified
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation	Jun 14, 2017	Active Learningcompressed sensing	—Unverified
Kernel Change-point Analysis	Dec 1, 2008	Two-sample testing	—Unverified
Cross-situational learning of large lexicons with finite memory	Sep 28, 2018	Two-sample testing	—Unverified
Kernel Mean Embedding Based Hypothesis Tests for Comparing Spatial Point Patterns	May 31, 2019	Point ProcessesTwo-sample testing	—Unverified
Kernel Mean Embedding of Distributions: A Review and Beyond	May 31, 2016	Causal DiscoveryReinforcement Learning	—Unverified
Kernel Two-Sample Hypothesis Testing Using Kernel Set Classification	Jun 18, 2017	ClassificationGeneral Classification	—Unverified
Asymptotically Optimal One- and Two-Sample Testing with Kernels	Aug 27, 2019	Change DetectionTwo-sample testing	—Unverified
Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects	Nov 9, 2020	MarketingTwo-sample testing	—Unverified

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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