most citedThe Binary Space Partitioning-Tree Process

21 citations · 48 across the 10 of their papers we have counts for

collaborators

11 papers

stat.ML20201 cited

Online Binary Space Partitioning Forests

Xuhui Fan, Bin Li, Scott A. Sisson

The Binary Space Partitioning-Tree~(BSP-Tree) process was recently proposed as an efficient strategy for space partitioning tasks. Because it uses more than one dimension to partit…

cs.LG20201 cited

Supervised Categorical Metric Learning with Schatten p-Norms

Xuhui Fan, Eric Gaussier

Metric learning has been successful in learning new metrics adapted to numerical datasets. However, its development on categorical data still needs further exploration. In this pap…

stat.ML2020

Smoothing Graphons for Modelling Exchangeable Relational Data

Xuhui Fan, Yaqiong Li, Ling Chen +2

Modelling exchangeable relational data can be described by \textit{graphon theory}. Most Bayesian methods for modelling exchangeable relational data can be attributed to this frame…

stat.ML2020

Bayesian Nonparametric Space Partitions: A Survey

Xuhui Fan, Bin Li, Ling Luo +1

Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a -dimensional space into a set of blocks. In this way, the data points lie…

stat.ML20203 cited

Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel

Zheng Yu, Xuhui Fan, Marcin Pietrasik +1

The Mixed-Membership Stochastic Blockmodel~(MMSB) is proposed as one of the state-of-the-art Bayesian relational methods suitable for learning the complex hidden structure underlyi…

stat.ML2019

Scalable Deep Generative Relational Models with High-Order Node Dependence

Xuhui Fan, Bin Li, Scott Anthony Sisson +2

We propose a probabilistic framework for modelling and exploring the latent structure of relational data. Given feature information for the nodes in a network, the scalable deep ge…