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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 71–80 of 338 papers

TitleStatusHype
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images—0
Contextual Online False Discovery Rate Control—0
A Sparse Linear Model and Significance Test for Individual Consumption Prediction—0
Counterexamples to the Low-Degree Conjecture—0
Covariance-Robust Dynamic Watermarking—0
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation—0
Cross-situational learning of large lexicons with finite memory—0
Ctrl-Z: Recovering from Instability in Reinforcement Learning—0
Asymptotically Optimal One- and Two-Sample Testing with Kernels—0
A General Framework for Distributed Inference with Uncertain Models—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