21 citations · 60 across the 20 of their papers we have counts for
20 papers
Bayesian Federated Learning: A Survey
Longbing Cao, Hui Chen, Xuhui Fan +3
Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the ro…
Free-Form Variational Inference for Gaussian Process State-Space Models
Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane +1
Gaussian process state-space models (GPSSMs) provide a principled and flexible approach to modeling the dynamics of a latent state, which is observed at discrete-time points via a…
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…
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…
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…
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…
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…
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…
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation
Feng Zhou, Zhidong Li, Xuhui Fan +3
In this paper, we consider the sigmoid Gaussian Hawkes process model: the baseline intensity and triggering kernel of Hawkes process are both modeled as the sigmoid transformation…
Fast Multi-resolution Segmentation for Nonstationary Hawkes Process Using Cumulants
Feng Zhou, Zhidong Li, Xuhui Fan +3
The stationarity is assumed in vanilla Hawkes process, which reduces the model complexity but introduces a strong assumption. In this paper, we propose a fast multi-resolution segm…
Binary Space Partitioning Forests
Xuhui Fan, Bin Li, Scott Anthony Sisson
The Binary Space Partitioning~(BSP)-Tree process is proposed to produce flexible 2-D partition structures which are originally used as a Bayesian nonparametric prior for relational…
The Binary Space Partitioning-Tree Process
Xuhui Fan, Bin Li, Scott Anthony Sisson
The Mondrian process represents an elegant and powerful approach for space partition modelling. However, as it restricts the partitions to be axis-aligned, its modelling flexibilit…
Rectangular Bounding Process
Xuhui Fan, Bin Li, Scott Anthony Sisson
Stochastic partition models divide a multi-dimensional space into a number of rectangular regions, such that the data within each region exhibit certain types of homogeneity. Due t…
Stochastic Patching Process
Xuhui Fan, Bin Li, Yi Wang +2
Stochastic partition models tailor a product space into a number of rectangular regions such that the data within each region exhibit certain types of homogeneity. Due to constrain…
Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network
Xuhui Fan, Richard Yi Da Xu, Longbing Cao +1
Effectively modelling hidden structures in a network is very practical but theoretically challenging. Existing relational models only involve very limited information, namely the b…
Non-parametric Power-law Data Clustering
Xuhui Fan, Yiling Zeng, Longbing Cao
It has always been a great challenge for clustering algorithms to automatically determine the cluster numbers according to the distribution of datasets. Several approaches have bee…
A Convergence Theorem for the Graph Shift-type Algorithms
Xuhui Fan, Longbing Cao
Graph Shift (GS) algorithms are recently focused as a promising approach for discovering dense subgraphs in noisy data. However, there are no theoretical foundations for proving th…
Dynamic Infinite Mixed-Membership Stochastic Blockmodel
Xuhui Fan, Longbing Cao, Richard Yi Da Xu
Directional and pairwise measurements are often used to model inter-relationships in a social network setting. The Mixed-Membership Stochastic Blockmodel (MMSB) was a seminal work…
Copula Mixed-Membership Stochastic Blockmodel for Intra-Subgroup Correlations
Xuhui Fan, Longbing Cao, Richard Yi Da Xu
The \emph{Mixed-Membership Stochastic Blockmodel (MMSB)} is a popular framework for modeling social network relationships. It can fully exploit each individual node's participation…
Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models
Yin Song, Longbing Cao, Xuhui Fan +2
This paper proposes a generative model, the latent Dirichlet hidden Markov models (LDHMM), for characterizing a database of sequential behaviors (sequences). LDHMMs posit that each…