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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
Adaptive Active Hypothesis Testing under Limited Information—0
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial ExamplesCode0
Discovering Potential Correlations via Hypercontractivity—0
Hypothesis Testing based Intrinsic Evaluation of Word Embeddings—0
Priv’IT: Private and Sample Efficient Identity Testing—0
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption—0
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing—0
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs—0
Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates—0
Kernel Two-Sample Hypothesis Testing Using Kernel Set Classification—0
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation—0
Toward Optimal Run Racing: Application to Deep Learning Calibration—0
Two-Sample Tests for Large Random Graphs Using Network Statistics—0
Negative Results in Computer Vision: A Perspective—0
Sparse Representation-based Open Set RecognitionCode0
A Flexible Framework for Hypothesis Testing in High-dimensions—0
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settingsCode0
Strictly Proper Kernel Scoring Rules and Divergences with an Application to Kernel Two-Sample Hypothesis Testing—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Priv'IT: Private and Sample Efficient Identity TestingCode0
Testing and Learning on Distributions with Symmetric Noise Invariance—0
Measuring Sample Quality with Kernels—0
Statistical Anomaly Detection via Composite Hypothesis Testing for Markov ModelsCode0
Online Robust Principal Component Analysis with Change Point DetectionCode0
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease—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