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 …

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 …

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

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 …

Learning dynamics from dissipative chaotic systems is notoriously difficult due to their
inherent instability, as formalized by their positive Lyapunov exponents, which exponentially …

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 …

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 …

We describe a data-efficient, kernel-based approach to statistical testing of conditional
independence. A major challenge of conditional independence testing, absent in tests of …

We introduce credal two-sample testing, a new hypothesis testing framework for comparing
credal sets--convex sets of probability measures where each element captures aleatoric …

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 …