Two-sample testing
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Benchmarks
Most implemented
PacGAN: The power of two samples in generative adversarial networks
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
hyppo: A Multivariate Hypothesis Testing Python Package
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
AutoML Two-Sample Test
MMD Aggregated Two-Sample Test
Papers
Zero-Flow Two-Sample Tests
We propose a new approach to two-sample testing for deciding whether two sets of samples are drawn from the same distribution. The test is built on a statistical discrepancy based on the zero-flow criterion, termed zero-…
Two-sample testingPost Hoc Inference for Component Attribution in Multivariate Change-Point Detection
We consider the post-detection analysis of change-points for multivariate time series, with the goal of identifying which coordinates are responsible for a detected change. After a change-point has been located by an off…
Two-sample testingA nonparametric two-sample test using a parametric integral probability metric
Detecting distributional differences between two independent samples is a fundamental problem in statistics and machine learning. Nonparametric two-sample testing provides a principled framework for determining whether t…
Two-sample testingLOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.g., via kernel-based feature representations). Such methods typically rely on data sp…
Two-sample testingCounterfactual Explanations for Deep Two-Sample Testing
Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-based tests) can be ineffective on high-dimensional structured data such …
Two-sample testingA Martingale Kernel Independence Test
The Hilbert-Schmidt Independence Criterion (HSIC) and its joint-independence extension $d\mathrm{HSIC}$ are degenerate $V$-statistics whose data-dependent weighted-$χ^2$ null limits force a permutation calibration that m…
Two-sample testing