ORCID Profile
0000-0002-8367-6908
Current Organisation
University of Technology Sydney
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Pattern Recognition and Data Mining | Decision Support and Group Support Systems | Neural, Evolutionary and Fuzzy Computation | Artificial Intelligence and Image Processing | Information Systems | Information Retrieval and Web Search
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in the Information and Computing Sciences | Application Software Packages (excl. Computer Games) | Information Processing Services (incl. Data Entry and Capture) |
Related Links
Publications
Online hot event discovery based on Association Link Network
Publisher: Wiley
Date: 26-09-2014
DOI: 10.1002/CPE.3374
Cognition-Based Semantic Annotation for Web Images
Publisher: IEEE
Date: 12-2014
Topic model for graph mining
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Release ‘Bag-of-Words’ Assumption of Latent Dirichlet Allocation
Publisher: Springer Berlin Heidelberg
Date: 2014
Discovering small-world in association link networks for association learning
Publisher: Springer Science and Business Media LLC
Date: 12-06-2012
Direct Learning with Multi-task Neural Networks for Treatment Effect Estimation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Discovering small-world in association link networks for web-based learning
Publisher: ACM
Date: 12-2011
Extension of similarity measures in VSM: From orthogonal coordinate system to affine coordinate system
Publisher: IEEE
Date: 07-2014
A Fuzzy Word Similarity Measure for Selecting Top-$k$ Similar Words in Query Expansion
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Crowdsourcing-based timeline description of urban emergency events using social media
Publisher: Inderscience Publishers
Date: 2017
Graph Convolutional Neural Networks With Diverse Negative Samples via Decomposed Determinant Point Processes
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Bayesian Nonparametric Unsupervised Concept Drift Detection for Data Stream Mining
Publisher: Association for Computing Machinery (ACM)
Date: 13-11-2020
DOI: 10.1145/3420034
Abstract: Online data stream mining is of great significance in practice because of its ubiquity in many real-world scenarios, especially in the big data era. Traditional data mining algorithms cannot be directly applied to data streams due to (1) the possible change of underlying data distribution over time (i.e., concept drift ) and (2) delayed, short, or even no labels for streaming data in practice. A new research area, named unsupervised concept drift detection , has emerged to tackle this difficulty mainly based on two-s le hypothesis tests, such as the Kolmogorov–Smirnov test. However, it is surprising that none of the existing methods in this area exploit the Bayesian nonparametric hypothesis test, which has clear interpretability and straightforward prior knowledge encoding ability and no strict or unrealistic requirement of prefixing the form for the underlying data distribution. In this article, we present a Bayesian nonparametric unsupervised concept drift detection method based on the Polya tree hypothesis test. The basic idea is to decompose the underlying data distribution into a multi-resolution representation that transforms the whole distribution hypothesis test into recursive and simple binomial tests. Also, an incremental mechanism is especially designed to improve its efficiency in the stream setting. The method effectively detect drifts, and it also locates where a drift happens and the posteriors of hypotheses. The experiments on synthetic data verify the desired properties of the proposed method, and the experiments on real-world data show the better performance of the method for data stream mining compared with its frequentist counterpart in the literature.
Cognitive memory-inspired sentence ordering model
Publisher: Elsevier BV
Date: 07-2016
Bayesian Deep Reinforcement Learning via Deep Kernel Learning
Publisher: Springer Science and Business Media LLC
Date: 2018
Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization
Publisher: Elsevier BV
Date: 06-2019
Causal inference with Gaussian processes for support of terminating or maintaining an existing program
Publisher: WORLD SCIENTIFIC
Date: 30-07-2018
Mining websites preferences on web events in big data environment
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/CSE.2013.152
Measuring the semantic uncertainty of news events for evolution potential estimation
Publisher: Association for Computing Machinery (ACM)
Date: 09-06-2016
DOI: 10.1145/2903719
Abstract: The evolution potential estimation of news events can support the decision making of both corporations and governments. For ex le, a corporation could manage its public relations crisis in a timely manner if a negative news event about this corporation is known with large evolution potential in advance. However, existing state-of-the-art methods are mainly based on time series historical data, which are not suitable for the news events with limited historical data and bursty properties. In this article, we propose a purely content-based method to estimate the evolution potential of the news events. The proposed method considers a news event at a given time point as a system composed of different keywords, and the uncertainty of this system is defined and measured as the Semantic Uncertainty of this news event. At the same time, an uncertainty space is constructed with two extreme states: the most uncertain state and the most certain state. We believe that the Semantic Uncertainty has correlation with the content evolution of the news events, so it can be used to estimate the evolution potential of the news events. In order to verify the proposed method, we present detailed experimental setups and results measuring the correlation of the Semantic Uncertainty with the Content Change of news events using collected news events data. The results show that the correlation does exist and is stronger than the correlation of value from the time-series-based method with the Content Change. Therefore, we can use the Semantic Uncertainty to estimate the evolution potential of news events.
Bayesian Nonparametric Relational Topic Model through Dependent Gamma Processes
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Building Hierarchical Keyword Level Association Link Networks for Web Events Semantic Analysis
Publisher: IEEE
Date: 12-2011
Web event evolution trend prediction based on its computational social context
Publisher: Springer Science and Business Media LLC
Date: 14-03-2020
Infinite author topic model based on mixed gamma-negative binomial process
Publisher: IEEE
Date: 11-2016
DOI: 10.1109/ICDM.2015.19
A survey on Bayesian nonparametric learning
Publisher: Association for Computing Machinery (ACM)
Date: 25-01-2019
DOI: 10.1145/3291044
Abstract: Bayesian (machine) learning has been playing a significant role in machine learning for a long time due to its particular ability to embrace uncertainty, encode prior knowledge, and endow interpretability. On the back of Bayesian learning’s great success, Bayesian nonparametric learning (BNL) has emerged as a force for further advances in this field due to its greater modelling flexibility and representation power. Instead of playing with the fixed-dimensional probabilistic distributions of Bayesian learning, BNL creates a new “game” with infinite-dimensional stochastic processes. BNL has long been recognised as a research subject in statistics, and, to date, several state-of-the-art pilot studies have demonstrated that BNL has a great deal of potential to solve real-world machine-learning tasks. However, despite these promising results, BNL has not created a huge wave in the machine-learning community. Esotericism may account for this. The books and surveys on BNL written by statisticians are overcomplicated and filled with tedious theories and proofs. Each is certainly meaningful but may scare away new researchers, especially those with computer science backgrounds. Hence, the aim of this article is to provide a plain-spoken, yet comprehensive, theoretical survey of BNL in terms that researchers in the machine-learning community can understand. It is hoped this survey will serve as a starting point for understanding and exploiting the benefits of BNL in our current scholarly endeavours. To achieve this goal, we have collated the extant studies in this field and aligned them with the steps of a standard BNL procedure—from selecting the appropriate stochastic processes through manipulation to executing the model inference algorithms. At each step, past efforts have been thoroughly summarised and discussed. In addition, we have reviewed the common methods for implementing BNL in various machine-learning tasks along with its erse applications in the real world as ex les to motivate future studies.
Website Interaction Network
Publisher: Informa UK Limited
Date: 03-04-2014
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 28-06-2022
Abstract: Graph Convolutional Neural Networks (GCNs) have been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its neighbours (also known as positive s les). However, beyond these neighbouring nodes, graphs have a large, dark, all-but forgotten world in which we find the non-neighbouring nodes (negative s les). In this paper, we show that this great dark world holds a substantial amount of information that might be useful for representation learning. Most specifically, it can provide negative information about the node representations. Our overall idea is to select appropriate negative s les for each node and incorporate the negative information contained in these s les into the representation updates. Moreover, we show that the process of selecting the negative s les is not trivial. Our theme therefore begins by describing the criteria for a good negative s le, followed by a determinantal point process algorithm for efficiently obtaining such s les. A GCN, boosted by erse negative s les, then jointly considers the positive and negative information when passing messages. Experimental evaluations show that this idea not only improves the overall performance of standard representation learning but also significantly alleviates over-smoothing problems.
DAFS: a domain aware few shot generative model for event detection
Publisher: Springer Science and Business Media LLC
Date: 04-09-2022
Structural property-aware multilayer network embedding for latent factor analysis
Publisher: Elsevier BV
Date: 04-2018
Building knowledge base of urban emergency events based on crowdsourcing of social media
Publisher: Wiley
Date: 28-01-2016
DOI: 10.1002/CPE.3780
Uncertainty Analysis for the Keyword System of Web Events
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Building spatial temporal relation graph of concepts pair using web repository
Publisher: Springer Science and Business Media LLC
Date: 13-07-2016
Crowdsourcing based social media data analysis of urban emergency events
Publisher: Springer Science and Business Media LLC
Date: 27-06-2015
Discovering the core semantics of event from social media
Publisher: Elsevier BV
Date: 11-2016
Negative Samples-enhanced Graph Convolutional Neural Networks
Publisher: IEEE
Date: 26-11-2021
Doubly Nonparametric Sparse Nonnegative Matrix Factorization Based on Dependent Indian Buffet Processes
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
A Causal Dirichlet Mixture Model for Causal Inference from Observational Data
Publisher: Association for Computing Machinery (ACM)
Date: 29-04-2020
DOI: 10.1145/3379500
Abstract: Estimating causal effects by making causal inferences from observational data is common practice in scientific studies, business decision-making, and daily life. In today’s data-driven world, causal inference has become a key part of the evaluation process for many purposes, such as examining the effects of medicine or the impact of an economic policy on society. However, although the literature contains some excellent models, there is room to improve their representation power and their ability to capture complex relationships. For these reasons, we propose a novel prior called Causal DP and a model called CDP. The prior captures the complex relationships between covariates, treatments, and outcomes in observational data using a rational probabilistic dependency structure. The model is Bayesian, nonparametric, and generative and is not based on the assumption of any parametric distribution. CDP is designed to estimate various kinds of causal effects—average, conditional average, average treated, quantile, and so on. It performs well with missing covariates and does not suffer from overfitting. Comparative experiments on synthetic datasets against several state-of-the-art methods demonstrate that CDP has a superior ability to capture complex relationships. Further, a simple evaluation to infer the effect of a job training program on trainee earnings from real-world data shows that CDP is both effective and useful for causal inference.
Temporal Learning of Semantic Relations between Concepts Using Web Repository
Publisher: IEEE
Date: 08-2015
DOI: 10.1109/SKG.2015.18
Explicitly and implicitly exploiting the hierarchical structure for mining website interests on news events
Publisher: Elsevier BV
Date: 12-2017
Sentiment Computing for the News Event Based on the Social Media Big Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
One-Stage Deep Instrumental Variable Method for Causal Inference from Observational Data
Publisher: IEEE
Date: 11-2019
Path integral based convolution and pooling for graph neural networks*
Publisher: IOP Publishing
Date: 12-2021
Abstract: Graph neural networks (GNNs) extend the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success. Borrowing ideas from physics, we propose path integral-based GNNs (PAN) for classification and regression tasks on graphs. Specifically, we consider a convolution operation that involves every path linking the message sender and receiver with learnable weights depending on the path length, which corresponds to the maximal entropy random walk. It generalizes the graph Laplacian to a new transition matrix that we call the maximal entropy transition (MET) matrix derived from a path integral formalism. Importantly, the diagonal entries of the MET matrix are directly related to the subgraph centrality, thus leading to a natural and adaptive pooling mechanism. PAN provides a versatile framework that can be tailored for different graph data with varying sizes and structures. We can view most existing GNN architectures as special cases of PAN. Experimental results show that PAN achieves state-of-the-art performance on various graph classification/regression tasks, including a new benchmark dataset from statistical mechanics that we propose to boost applications of GNN in physical sciences.
Building the Profile of Web Events Based on Website Measurement
Publisher: Springer Singapore
Date: 27-09-2017
A Bayesian nonparametric model for multi-label learning
Publisher: Springer Science and Business Media LLC
Date: 25-08-2017
Concept Drift Detection Delay Index
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Map-based medical practice behavior analysis: Methodology and a case study on Australia’s medical practices
Publisher: WORLD SCIENTIFIC
Date: 30-07-2018
Open Set Domain Adaptation: Theoretical Bound and Algorithm
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Related Organisations
Related Funding Activities
Discovery Projects - Grant ID: DP220102635
Start Date: 10-2022
End Date: 10-2025
Amount: $494,500.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP190101733
Start Date: 06-2019
End Date: 09-2022
Amount: $467,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Early Career Researcher Award - Grant ID: DE200100245
Start Date: 05-2020
End Date: 04-2024
Amount: $410,518.00
Funder: Australian Research Council
View Funded Activity