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 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 …

Local permutation tests for conditional independence Page 1 The Annals of Statistics 2022, Vol.
50, No. 6, 3388–3414 https://doi.org/10.1214/22-AOS2233 © Institute of Mathematical Statistics …

Spectral regularized kernel two-sample tests Page 1 The Annals of Statistics 2024, Vol. 52,
No. 3, 1076–1101 https://doi.org/10.1214/24-AOS2383 © Institute of Mathematical Statistics …

Normalizing flow is a class of deep generative models for efficient sampling and likelihood
estimation, which achieves attractive performance, particularly in high dimensions. The flow …

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 …

In nonparametric independence testing, we observe iid data {(Xi, Yi)} ni= 1, where X∈ Χ,
Y∈ Y lie in any general spaces, and we wish to test the null that X is independent of Y …

Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-
of-fit tests. It can be applied even when the target distribution has an unknown normalising …