75 citations · 135 across the 19 of their papers we have counts for
19 papers
A Kernel Nonconformity Score for Multivariate Conformal Prediction
Louis Meyer, Wenkai Xu
Multivariate conformal prediction requires nonconformity scores that compress residual vectors into scalars while preserving certain implicit geometric structure of the residual di…
Stein's method of moment estimators for local dependency exponential random graph models
Adrian Fischer, Gesine Reinert, Wenkai Xu
Providing theoretical guarantees for parameter estimation in exponential random graph models is a largely open problem. While maximum likelihood estimation has theoretical guarante…
Split Conformal Prediction under Data Contamination
Jase Clarkson, Wenkai Xu, Mihai Cucuringu +2
Conformal prediction is a non-parametric technique for constructing prediction intervals or sets from arbitrary predictive models under the assumption that the data is exchangeable…
SteinGen: Generating Fidelitous and Diverse Graph Samples
Gesine Reinert, Wenkai Xu
Generating graphs that preserve characteristic structures while promoting sample diversity can be challenging, especially when the number of graph observations is small. Here, we t…
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…
Standardisation-function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit
Wenkai Xu
Non-parametric goodness-of-fit testing procedures based on kernel Stein discrepancies (KSD) are promising approaches to validate general unnormalised distributions in various scena…
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
Feng Liu, Wenkai Xu, Jie Lu +1
Modern kernel-based two-sample tests have shown great success in distinguishing complex, high-dimensional distributions with appropriate learned kernels. Previous work has demonstr…
Interpretable Stein Goodness-of-fit Tests on Riemannian Manifolds
Wenkai Xu, Takeru Matsuda
In many applications, we encounter data on Riemannian manifolds such as torus and rotation groups. Standard statistical procedures for multivariate data are not applicable to such…
A Stein Goodness of fit Test for Exponential Random Graph Models
Wenkai Xu, Gesine Reinert
We propose and analyse a novel nonparametric goodness of fit testing procedure for exchangeable exponential random graph models (ERGMs) when a single network realisation is observe…
A kernel test for quasi-independence
Tamara Fernández, Wenkai Xu, Marc Ditzhaus +1
We consider settings in which the data of interest correspond to pairs of ordered times, e.g, the birth times of the first and second child, the times at which a new user creates a…
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Tamara Fernandez, Nicolas Rivera, Wenkai Xu +1
Survival Analysis and Reliability Theory are concerned with the analysis of time-to-event data, in which observations correspond to waiting times until an event of interest such as…
A Stein Goodness-of-fit Test for Directional Distributions
Wenkai Xu, Takeru Matsuda
In many fields, data appears in the form of direction (unit vector) and usual statistical procedures are not applicable to such directional data. In this study, we propose non-para…
Learning Deep Kernels for Non-Parametric Two-Sample Tests
Feng Liu, Wenkai Xu, Jie Lu +3
We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution. Our tests are constructed from kernels…
Model Reuse with Reduced Kernel Mean Embedding Specification
Xi-Zhu Wu, Wenkai Xu, Song Liu +1
Given a publicly available pool of machine learning models constructed for various tasks, when a user plans to build a model for her own machine learning application, is it possibl…
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs
Wenkai Xu, Gang Niu, Aapo Hyvärinen +1
Summarizing large-scaled directed graphs into small-scale representations is a useful but less studied problem setting. Conventional clustering approaches, which based on "Min-Cut"…
A Linear-Time Kernel Goodness-of-Fit Test
Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo +2
We propose a novel adaptive test of goodness-of-fit, with computational cost linear in the number of samples. We learn the test features that best indicate the differences between…