Search Results for author: Xuhui Fan

Found 21 papers, 2 papers with code

Continuous-time edge modelling using non-parametric point processes

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.

Gaussian Processes Point Processes +1

Online Binary Space Partitioning Forests

no code implementations29 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.

General Classification

Supervised Categorical Metric Learning with Schatten p-Norms

no code implementations26 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.

Metric Learning

Bayesian Nonparametric Space Partitions: A Survey

no code implementations26 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.

Smoothing Graphons for Modelling Exchangeable Relational Data

no code implementations25 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.

Link Prediction Stochastic Block Model

Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling

no code implementations24 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.

Link Prediction

Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel

no code implementations17 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).

Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation

no code implementations29 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).

Bayesian Inference Gaussian Processes +1

Efficient EM-Variational Inference for Hawkes Process

no code implementations29 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.

Variational Inference

Binary Space Partitioning Forests

no code implementations22 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.

The Binary Space Partitioning-Tree Process

no code implementations22 Mar 2019 Xuhui Fan, Bin Li, Scott Anthony Sisson

The Mondrian process represents an elegant and powerful approach for space partition modelling.

Rectangular Bounding Process

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.

Stochastic Patching Process

no code implementations23 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.

Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network

no code implementations6 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.

Dynamic Infinite Mixed-Membership Stochastic Blockmodel

no code implementations13 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.

A Convergence Theorem for the Graph Shift-type Algorithms

no code implementations13 Jun 2013 Xuhui Fan, Longbing Cao

Graph Shift (GS) algorithms are recently focused as a promising approach for discovering dense subgraphs in noisy data.

Non-parametric Power-law Data Clustering

no code implementations13 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.

Variational Inference

Copula Mixed-Membership Stochastic Blockmodel for Intra-Subgroup Correlations

no code implementations12 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.

Link Prediction

Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models

no code implementations24 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.

General Classification

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