We propose a series of computationally efficient, nonparametric tests for the two-sample,
independence and goodness-of-fit problems, using the Maximum Mean Discrepancy …

We propose two novel nonparametric two-sample kernel tests based on the Maximum Mean
Discrepancy (MMD). First, for a fixed kernel, we construct an MMD test using either …

We propose novel statistics which maximise the power of a two-sample test based on the
Maximum Mean Discrepancy (MMD), byadapting over the set of kernels used in defining it …

Abstract The kernel Maximum Mean Discrepancy~(MMD) is a popular multivariate distance
metric between distributions. The usual kernel-MMD test statistic (for two-sample testing) is a …

Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have
emerged to address these issues by enabling a paradigm that relies on generative AI …

Two-sample tests are important in statistics and machine learning, both as tools for scientific
discovery as well as to detect distribution shifts. This led to the development of many …

Single-cell technologies offer insights into molecular feature distributions, but comparing
them poses challenges. We propose a kernel-testing framework for non-linear cell-wise …

We investigate properties of goodness-of-fit tests based on the Kernel Stein Discrepancy
(KSD). We introduce a strategy to construct a test, called KSDAgg, which aggregates …

Modern kernel-based two-sample tests have shown great success in distinguishing
complex, high-dimensional distributions by learning appropriate kernels (or, as a special …

Single-cell technologies offer insights into molecular feature distributions, but comparing
them poses challenges. We propose a kernel-testing framework for non-linear cell-wise …