research
∙
10/30/2022
Nonlinear Causal Discovery via Kernel Anchor Regression
Learning causal relationships is a fundamental problem in science. Ancho...
share
research
∙
10/11/2022
On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a p...
research
∙
05/31/2022
A Kernelised Stein Statistic for Assessing Implicit Generative Models
Synthetic data generation has become a key ingredient for training machi...
research
∙
03/07/2022
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
We propose and analyse a novel statistical procedure, coined AgraSSt, to...
research
∙
06/23/2021
Generalised Kernel Stein Discrepancy(GKSD): A Unifying Approach for Non-parametric Goodness-of-fit Testing
Non-parametric goodness-of-fit testing procedures based on kernel Stein ...
research
∙
06/14/2021
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
Modern kernel-based two-sample tests have shown great success in disting...
research
∙
03/01/2021
Interpretable Stein Goodness-of-fit Tests on Riemannian Manifolds
In many applications, we encounter data on Riemannian manifolds such as ...
research
∙
02/28/2021
A Stein Goodness of fit Test for Exponential Random Graph Models
We propose and analyse a novel nonparametric goodness of fit testing pro...
research
∙
11/17/2020
A kernel test for quasi-independence
We consider settings in which the data of interest correspond to pairs o...
research
∙
08/19/2020
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Survival Analysis and Reliability Theory are concerned with the analysis...
research
∙
02/21/2020
Learning Deep Kernels for Non-Parametric Two-Sample Tests
We propose a class of kernel-based two-sample tests, which aim to determ...
research
∙
02/17/2020
A Stein Goodness-of-fit Test for Directional Distributions
In many fields, data appears in the form of direction (unit vector) and ...
research
∙
01/20/2020
Model Reuse with Reduced Kernel Mean Embedding Specification
Given a publicly available pool of machine learning models constructed f...
research
∙
07/22/2019
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs
Summarizing large-scaled directed graphs into small-scale representation...
research
∙
05/22/2017
