no code implementations • 24 Apr 2024 • Hui Chen, Hengyu Liu, Zhangkai Wu, Xuhui Fan, Longbing Cao
While deep neural networks (DNNs) based personalized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for federated learning (FL) suffer from efficient systematic uncertainty quantification.
no code implementations • 26 Apr 2023 • Longbing Cao, Hui Chen, Xuhui Fan, Joao Gama, Yew-Soon Ong, Vipin Kumar
This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives.
1 code implementation • 20 Feb 2023 • Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson
However, inference in GPSSMs is computationally and statistically challenging due to the large number of latent variables in the model and the strong temporal dependencies between them.
no code implementations • NeurIPS 2021 • Xuhui Fan, Bin Li, Feng Zhou, Scott Sisson
The mutually-exciting Hawkes process (ME-HP) is a natural choice to model reciprocity, which is an important attribute of continuous-time edge (dyadic) data.
no code implementations • 29 Feb 2020 • 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.
no code implementations • 26 Feb 2020 • Xuhui Fan, Eric Gaussier
In this paper, we propose a method, called CPML for \emph{categorical projected metric learning}, that tries to efficiently~(i. e. less computational time and better prediction accuracy) address the problem of metric learning in categorical data.
no code implementations • 26 Feb 2020 • Xuhui Fan, Bin Li, Ling Luo, Scott A. Sisson
Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a $D$-dimensional space into a set of blocks.
no code implementations • 25 Feb 2020 • Xuhui Fan, Yaqiong Li, Ling Chen, Bin Li, Scott A. Sisson
We initially propose the Integrated Smoothing Graphon (ISG) which introduces one smoothing parameter to the SBM graphon to generate continuous relational intensity values.
no code implementations • 24 Feb 2020 • Yaqiong Li, Xuhui Fan, Ling Chen, Bin Li, Zheng Yu, Scott A. Sisson
In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data.
no code implementations • 17 Jan 2020 • Zheng Yu, Xuhui Fan, Marcin Pietrasik, Marek Reformat
Besides, the proposed model infers the network structure and models community evolution, manifested by appearances and disappearances of communities, using the discrete fragmentation coagulation process (DFCP).
1 code implementation • NeurIPS 2019 • Xuhui Fan, Bin Li, Caoyuan Li, Scott Sisson, Ling Chen
In this work, we propose a probabilistic framework for relational data modelling and latent structure exploring.
no code implementations • 4 Nov 2019 • Xuhui Fan, Bin Li, Scott Anthony Sisson, Caoyuan Li, Ling Chen
We propose a probabilistic framework for modelling and exploring the latent structure of relational data.
no code implementations • 29 Oct 2019 • Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen
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 of random trajectories drawn from Gaussian processes (GP).
no code implementations • 29 May 2019 • Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen
In classical Hawkes process, the baseline intensity and triggering kernel are assumed to be a constant and parametric function respectively, which limits the model flexibility.
no code implementations • 22 Mar 2019 • Xuhui Fan, Bin Li, Scott Anthony Sisson
The Mondrian process represents an elegant and powerful approach for space partition modelling.
no code implementations • 22 Mar 2019 • 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 modelling.
1 code implementation • NeurIPS 2018 • 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.
no code implementations • 23 May 2016 • Xuhui Fan, Bin Li, Yi Wang, Yang Wang, Fang Chen
Due to constraints of partition strategy, existing models may cause unnecessary dissections in sparse regions when fitting data in dense regions.
no code implementations • 6 Oct 2013 • Xuhui Fan, Richard Yi Da Xu, Longbing Cao, Yin Song
In this work, we propose an informative relational model (InfRM) framework to adequately involve rich information and its granularity in a network, including metadata information about each entity and various forms of link data.
no code implementations • 13 Jun 2013 • Xuhui Fan, Longbing Cao
Graph Shift (GS) algorithms are recently focused as a promising approach for discovering dense subgraphs in noisy data.
no code implementations • 13 Jun 2013 • Xuhui Fan, Yiling Zeng, Longbing Cao
However, several problems remains unsolved in this pioneering work, including the power-law data applicability, mechanism to merge centers to avoid the over-fitting problem, clustering order problem, e. t. c.. To address these issues, the Pitman-Yor Process based k-means (namely \emph{pyp-means}) is proposed in this paper.
no code implementations • 13 Jun 2013 • Xuhui Fan, Longbing Cao, Richard Yi Da Xu
Directional and pairwise measurements are often used to model inter-relationships in a social network setting.
no code implementations • 12 Jun 2013 • Xuhui Fan, Longbing Cao, Richard Yi Da Xu
To this end, we introduce a \emph{Copula Mixed-Membership Stochastic Blockmodel (cMMSB)} where an individual Copula function is employed to jointly model the membership pairs of those nodes within the subgroup of interest.
no code implementations • 24 May 2013 • Yin Song, Longbing Cao, Xuhui Fan, Wei Cao, Jian Zhang
These sequence-level latent parameters for each sequence are modeled as latent Dirichlet random variables and parameterized by a set of deterministic database-level hyper-parameters.