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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 141–150 of 338 papers

TitleStatusHype
Stock Price Forecasting and Hypothesis Testing Using Neural Networks—0
Asymptotically Optimal One- and Two-Sample Testing with Kernels—0
Minimum Description Length Revisited—0
Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publications—0
Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word CategoriesCode0
Notes on Computational Hardness of Hypothesis Testing: Predictions using the Low-Degree Likelihood Ratio—0
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Kernel Hypothesis Testing with Set-valued Data—0
Conditional Independence Testing using Generative Adversarial NetworksCode0
A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience—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