1 code implementation • 10 Jul 2024 • Jase Clarkson, Wenkai Xu, Mihai Cucuringu, Gesine Reinert

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.

1 code implementation • 27 Mar 2024 • 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.

1 code implementation • 30 Oct 2022 • Wenqi Shi, Wenkai Xu

Anchor regression has been developed to address this problem for a large class of causal graphical models, though the relationships between the variables are assumed to be linear.

1 code implementation • 11 Oct 2022 • 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 depend on the choice of kernels in an underlying reproducing kernel Hilbert space (RKHS).

1 code implementation • 31 May 2022 • Wenkai Xu, Gesine Reinert

Assessing the quality of such synthetic data generators hence has to be addressed.

1 code implementation • 7 Mar 2022 • 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.

no code implementations • 23 Jun 2021 • 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 scenarios.

1 code implementation • NeurIPS 2021 • Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland

In realistic scenarios with very limited numbers of data samples, however, it can be challenging to identify a kernel powerful enough to distinguish complex distributions.

1 code implementation • 28 Feb 2021 • 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 observed.

no code implementations • NeurIPS 2020 • Tamara Fernández, Wenkai Xu, Marc Ditzhaus, Arthur Gretton

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 an account and makes the first purchase on a website, and the entry and survival times of patients in a clinical trial.

no code implementations • ICML 2020 • Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton

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 death from a particular disease or failure of a component in a mechanical system.

1 code implementation • ICML 2020 • Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland

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.

Ranked #1 on Two-sample testing on HIGGS Data Set

no code implementations • 20 Jan 2020 • Xi-Zhu Wu, Wenkai Xu, Song Liu, Zhi-Hua Zhou

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 possible to build upon models in the pool such that the previous efforts on these existing models can be reused rather than starting from scratch?

no code implementations • 22 Jul 2019 • Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama

On the other hand, compressing the vertices while preserving the directed edge information provides a way to learn the small-scale representation of a directed graph.

4 code implementations • NeurIPS 2017 • Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton

We propose a novel adaptive test of goodness-of-fit, with computational cost linear in the number of samples.

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