SOTAVerified

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
The Perturbed Variation—0
The p-filter: multi-layer FDR control for grouped hypotheses—0
The Power of Batching in Multiple Hypothesis Testing—0
The power of synergy in differential privacy: Combining a small curator with local randomizers—0
The Role of Interactivity in Local Differential Privacy—0
The Structure of Optimal Private Tests for Simple Hypotheses—0
Veridical Data Science—0
Tight Regret Bounds for Noisy Optimization of a Brownian Motion—0
Toward Optimal Feature Selection in Naive Bayes for Text Categorization—0
Toward Optimal Run Racing: Application to Deep Learning Calibration—0
Towards an Evolvable Cancer Treatment Simulator—0
Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks—0
Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing—0
Training Neural Networks for Likelihood/Density Ratio Estimation—0
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs—0
Two-sample testing in non-sparse high-dimensional linear models—0
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions—0
Two-Sample Tests for Large Random Graphs Using Network Statistics—0
Unbiased estimators for the variance of MMD estimators—0
Understanding Classifier Mistakes with Generative Models—0
Understanding Classifiers with Generative Models—0
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit—0
Universality of Computational Lower Bounds for Submatrix Detection—0
Unsupervised Feature Construction for Improving Data Representation and Semantics—0
Unsupervised Textual Grounding: Linking Words to Image Concepts—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