7 citations · 10 across the 10 of their papers we have counts for
10 papers
Multimodal Machine Learning for Real Estate Appraisal: A Comprehensive Survey
Chenya Huang, Zhidong Li, Fang Chen +1
Real estate appraisal has undergone a significant transition from manual to automated valuation and is entering a new phase of evolution. Leveraging comprehensive attention to vari…
Navigating Towards Fairness with Data Selection
Yixuan Zhang, Zhidong Li, Yang Wang +3
Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fai…
Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods
Shuming Liang, Yu Ding, Zhidong Li +4
This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms of information for link prediction, alongside a brief review of existing link prediction m…
TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes
Zizhuo Meng, Boyu Li, Xuhui Fan +4
The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant…
De-biased Representation Learning for Fairness with Unreliable Labels
Yixuan Zhang, Feng Zhou, Zhidong Li +2
Removing bias while keeping all task-relevant information is challenging for fair representation learning methods since they would yield random or degenerate representations w.r.t.…
Bias-Tolerant Fair Classification
Yixuan Zhang, Feng Zhou, Zhidong Li +2
The label bias and selection bias are acknowledged as two reasons in data that will hinder the fairness of machine-learning outcomes. The label bias occurs when the labeling decisi…
Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis
Dilusha Weeraddana, Sudaraka MallawaArachchi, Tharindu Warnakula +2
Australian water infrastructure is more than a hundred years old, thus has begun to show its age through water main failures. Our work concerns approximately half a million pipelin…
Utilizing machine learning to prevent water main breaks by understanding pipeline failure drivers
Dilusha Weeraddana, Bin Liang, Zhidong Li +5
Data61 and Western Water worked collaboratively to apply engineering expertise and Machine Learning tools to find a cost-effective solution to the pipe failure problem in the regio…
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…
Fast Multi-resolution Segmentation for Nonstationary Hawkes Process Using Cumulants
Feng Zhou, Zhidong Li, Xuhui Fan +3
The stationarity is assumed in vanilla Hawkes process, which reduces the model complexity but introduces a strong assumption. In this paper, we propose a fast multi-resolution segm…