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04/26/2023
Bayesian Federated Learning: A Survey
Federated learning (FL) demonstrates its advantages in integrating distr...
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02/20/2023
Free-Form Variational Inference for Gaussian Process State-Space Models
Gaussian process state-space models (GPSSMs) provide a principled and fl...
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02/29/2020
Online Binary Space Partitioning Forests
The Binary Space Partitioning-Tree (BSP-Tree) process was recently propo...
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02/26/2020
Bayesian Nonparametric Space Partitions: A Survey
Bayesian nonparametric space partition (BNSP) models provide a variety o...
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02/26/2020
Supervised Categorical Metric Learning with Schatten p-Norms
Metric learning has been successful in learning new metrics adapted to n...
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02/25/2020
Smoothing Graphons for Modelling Exchangeable Relational Data
Modelling exchangeable relational data can be described by graphon theor...
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02/24/2020
Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling
The Dirichlet Belief Network (DirBN) has been recently proposed as a pro...
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11/04/2019
Scalable Deep Generative Relational Models with High-Order Node Dependence
We propose a probabilistic framework for modelling and exploring the lat...
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10/29/2019
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation
In this paper, we consider the sigmoid Gaussian Hawkes process model: th...
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06/06/2019
Fast Multi-resolution Segmentation for Nonstationary Hawkes Process Using Cumulants
The stationarity is assumed in vanilla Hawkes process, which reduces the...
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03/22/2019
Binary Space Partitioning Forests
The Binary Space Partitioning (BSP)-Tree process is proposed to produce ...
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03/22/2019
The Binary Space Partitioning-Tree Process
The Mondrian process represents an elegant and powerful approach for spa...
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03/10/2019
Rectangular Bounding Process
Stochastic partition models divide a multi-dimensional space into a numb...
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05/23/2016
Stochastic Patching Process
Stochastic partition models tailor a product space into a number of rect...
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10/06/2013
Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network
Effectively modelling hidden structures in a network is very practical b...
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06/13/2013
Non-parametric Power-law Data Clustering
It has always been a great challenge for clustering algorithms to automa...
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06/13/2013
A Convergence Theorem for the Graph Shift-type Algorithms
Graph Shift (GS) algorithms are recently focused as a promising approach...
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06/13/2013
Dynamic Infinite Mixed-Membership Stochastic Blockmodel
Directional and pairwise measurements are often used to model inter-rela...
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06/12/2013
Copula Mixed-Membership Stochastic Blockmodel for Intra-Subgroup Correlations
The Mixed-Membership Stochastic Blockmodel (MMSB) is a popular framework...
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05/24/2013
