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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
How to Control the Error Rates of Binary Classifiers—0
How to Formulate and Solve Statistical Recognition and Learning Problems—0
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models—0
Hypothesis Testing based Intrinsic Evaluation of Word Embeddings—0
Hypothesis Testing for Automated Community Detection in Networks—0
Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates—0
Hypothesis Testing for High-Dimensional Multinomials: A Selective Review—0
Hypothesis Testing in Feedforward Networks with Broadcast Failures—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
Hypothesis Testing Interpretations and Renyi Differential Privacy—0
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease—0
Identification of Model Uncertainty via Optimal Design of Experiments Applied to a Mechanical Press—0
Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching—0
Improved Differentially Private Analysis of Variance—0
Improved Sum-of-Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems—0
Information Recovery in Shuffled Graphs via Graph Matching—0
Information Theoretic Structure Learning with Confidence—0
Instance-Based Classification through Hypothesis Testing—0
Introduction to logistic regression—0
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems—0
Iterative hypothesis testing for multi-object tracking in presence of features with variable reliability—0
Kernel Change-point Analysis—0
Kernel Mean Embedding Based Hypothesis Tests for Comparing Spatial Point Patterns—0
Kernel Mean Embedding of Distributions: A Review and Beyond—0
Kernel Two-Sample Hypothesis Testing Using Kernel Set Classification—0
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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