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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 176–200 of 338 papers

TitleStatusHype
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
Veridical Data Science—0
Instance-Based Classification through Hypothesis Testing—0
Towards an Evolvable Cancer Treatment Simulator—0
Adversarial Sample Detection for Deep Neural Network through Model Mutation TestingCode0
Sequential Experiment Design for Hypothesis Verification—0
Testing for Families of Distributions via the Fourier Transform—0
Practical methods for graph two-sample testingCode0
Testing Changes in Communities for the Stochastic Block Model—0
The Structure of Optimal Private Tests for Simple Hypotheses—0
Variational Autoencoders for New Physics Mining at the Large Hadron ColliderCode0
Understanding Learned Models by Identifying Important Features at the Right ResolutionCode0
Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing—0
How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?—0
A tutorial on MDL hypothesis testing for graph analysis—0
Failing Loudly: An Empirical Study of Methods for Detecting Dataset ShiftCode0
Policy Design for Active Sequential Hypothesis Testing using Deep Learning—0
A Simple Way to Deal with Cherry-picking—0
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop—0
Cross-situational learning of large lexicons with finite memory—0
Distributed Chernoff Test: Optimal decision systems over networks—0
Multi-level hypothesis testing for populations of heterogeneous networks—0
A Structured Review of the Validity of BLEU—0
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain—0
A Differentially Private Kernel Two-Sample TestCode0
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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