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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 51–75 of 338 papers

TitleStatusHype
Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications—0
Quickest change detection for multi-task problems under unknown parameters—0
Understanding Classifiers with Generative Models—0
A General Framework for Distributed Inference with Uncertain Models—0
Policy design in experiments with unknown interference—0
Adversarially Robust Classification based on GLRT—0
Bottleneck Problems: Information and Estimation-Theoretic View—0
Dimension-agnostic inference using cross U-statistics—0
Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects—0
Robust hypothesis testing and distribution estimation in Hellinger distance—0
p-value peeking and estimating extrema—0
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and GraphsCode0
Towards Safe Policy Improvement for Non-Stationary MDPsCode0
An explainable deep vision system for animal classification and detection in trail-camera images with automatic post-deployment retraining—0
Detecting Rewards Deterioration in Episodic Reinforcement LearningCode0
How to Control the Error Rates of Binary Classifiers—0
Surprise: Result List Truncation via Extreme Value Theory—0
Quantum-enhanced barcode decoding and pattern recognition—0
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean DiscrepancyCode0
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective InferenceCode0
Understanding Classifier Mistakes with Generative Models—0
Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing—0
Statistical Query Algorithms and Low-Degree Tests Are Almost Equivalent—0
Introduction to logistic regression—0
Testing correlation of unlabeled random graphs—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