Learning causal relationships is a fundamental problem in science. Ancho...
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a
p...
Synthetic data generation has become a key ingredient for training machi...
We propose and analyse a novel statistical procedure, coined AgraSSt, to...
Non-parametric goodness-of-fit testing procedures based on kernel Stein
...
Modern kernel-based two-sample tests have shown great success in
disting...
In many applications, we encounter data on Riemannian manifolds such as ...
We propose and analyse a novel nonparametric goodness of fit testing
pro...
We consider settings in which the data of interest correspond to pairs o...
Survival Analysis and Reliability Theory are concerned with the analysis...
We propose a class of kernel-based two-sample tests, which aim to determ...
In many fields, data appears in the form of direction (unit vector) and ...
Given a publicly available pool of machine learning models constructed f...
Summarizing large-scaled directed graphs into small-scale representation...
We propose a novel adaptive test of goodness-of-fit, with computational ...