21 citations · 60 across the 20 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…