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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 226–250 of 338 papers

TitleStatusHype
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Modelling and Quantifying Membership Information Leakage in Machine Learning—0
Multi-level hypothesis testing for populations of heterogeneous networks—0
Nearly Optimal Sample Size in Hypothesis Testing for High-Dimensional Regression—0
Negative Results in Computer Vision: A Perspective—0
Network two-sample test for block models—0
Noiseless Privacy—0
Nonmyopic View Planning for Active Object Detection—0
Nonparametric Detection of Anomalous Data Streams—0
Notes on Computational Hardness of Hypothesis Testing: Predictions using the Low-Degree Likelihood Ratio—0
A review of Gaussian Markov models for conditional independence—0
Online Rules for Control of False Discovery Rate and False Discovery Exceedance—0
On Semiparametric Exponential Family Graphical Models—0
On the Decreasing Power of Kernel and Distance based Nonparametric Hypothesis Tests in High Dimensions—0
On the Exploration of Local Significant Differences For Two-Sample Test—0
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives—0
On the Learnability of Concepts: With Applications to Comparing Word Embedding Algorithms—0
On the Self-Similarity of Natural Stochastic Textures—0
Optimal Algorithms for Augmented Testing of Discrete Distributions—0
Optimal Nonparametric Inference via Deep Neural Network—0
Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing—0
Optimal Statistical Hypothesis Testing for Social Choice—0
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data—0
Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations—0
PAC Quasi-automatizability of Resolution over Restricted Distributions—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