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Two-sample testing
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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.
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Signature Maximum Mean Discrepancy Two-Sample Statistical Tests
From Two Sample Testing to Singular Gaussian Discrimination
Advanced Tutorial: Label-Efficient Two-Sample Tests
Optimal Algorithms for Augmented Testing of Discrete Distributions
A Unified Data Representation Learning for Non-parametric Two-sample Testing
Minimax Optimal Two-Sample Testing under Local Differential Privacy
Model Equality Testing: Which Model Is This API Serving?
General Frameworks for Conditional Two-Sample Testing
Credal Two-Sample Tests of Epistemic Uncertainty
Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features
Network two-sample test for block models
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances
Collaborative non-parametric two-sample testing
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
Deep anytime-valid hypothesis testing
A framework for paired-sample hypothesis testing for high-dimensional data
Kernel-Based Tests for Likelihood-Free Hypothesis Testing
Adaptive learning of density ratios in RKHS
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
The Representation Jensen-Shannon Divergence
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity
Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test
Active Sequential Two-Sample Testing
Compress Then Test: Powerful Kernel Testing in Near-linear Time
A Permutation-free Kernel Two-Sample Test
MMD-B-Fair: Learning Fair Representations with Statistical Testing
AutoML Two-Sample Test
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
Addressing Maximization Bias in Reinforcement Learning with Two-Sample Testing
A label-efficient two-sample test
MMD Aggregated Two-Sample Test
Graphon based Clustering and Testing of Networks: Algorithms and Theory
Limit Distribution Theory for the Smooth 1-Wasserstein Distance with Applications
Generalized Multivariate Signs for Nonparametric Hypothesis Testing in High Dimensions
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