75 citations · 135 across the 19 of their papers we have counts for
4 papers · 1 filter
Nonlinear Causal Discovery via Kernel Anchor Regression
Wenqi Shi, Wenkai Xu
Learning causal relationships is a fundamental problem in science. Anchor regression has been developed to address this problem for a large class of causal graphical models, though…
On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics
Moritz Weckbecker, Wenkai Xu, Gesine Reinert
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a powerful tool for the goodness of fit tests, especially in high dimensions; however, the test performance may…
A Kernelised Stein Statistic for Assessing Implicit Generative Models
Wenkai Xu, Gesine Reinert
Synthetic data generation has become a key ingredient for training machine learning procedures, addressing tasks such as data augmentation, analysing privacy-sensitive data, or vis…
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
Wenkai Xu, Gesine Reinert
We propose and analyse a novel statistical procedure, coined AgraSSt, to assess the quality of graph generators that may not be available in explicit form. In particular, AgraSSt c…