Research
My research interest is to apply Machine Learning to interdisciplinary (medical image analysis, environmental science, material science etc.)
In particluar, I am interested in:
- Interpretable ML (Concept-Based Explanations, Rule-Based Models, etc.)
- AutoML (Neural Architecture Search, Hyperparameter Optimization, etc.)
- Clustering Analysis (Label Spreading/Propagation)
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Publications
"*" indicates equal contribution, "_" indicates equal advising.
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Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
Xinyue Xu,
Yi Qin,
Lu Mi,
Hao Wang,
Xiaomeng Li
ICLR, 2024
Paper/Code
We introduce Energy-Based Concept Bottleneck Models (ECBM) as a unified framework
for concept-based prediction, concept correction, and fine-grained interpretations based on conditional probabilities.
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Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation
Xinyue Xu*,
Yuhan Hsi*,
Haonan Wang,
Xiaomeng Li
ICONIP, 2023
Paper/Code
Medical image data are often limited due to the expensive acquisition and annotation process. Hence, training a deep-learning model with only raw data can easily lead to overfitting.
To this end, we present a novel method, called Dynamic Data Augmentation (DDAug), which is efficient and has negligible computation cost.
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Exploring the Complexities of Dissolved Organic Matter Photochemistry from the Molecular Level by Using Machine Learning Approaches
Chen Zhao*,
Xinyue Xu*,
Hongmei Chen,
Fengwen Wang,
Penghui Li,
Chen He,
Quan Shi,
Yuanbi Yi,
Xiaomeng Li,
Siliang Li,
Ding He
Environmental Science & Technology (ES&T), 2023
Paper
Photochemical reactions are essential components altering dissolved organic matter (DOM) chemistry.
We first used machine learning approaches to compatibly integrate existing irradiation experiments
and provide novel insights into the estuarine DOM transformation processes.
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Towards Faster Hyperparameter-free Clustering using Enhanced Iterative Label Spreading
Xinyue Xu
Honours Thesis, 2022
ANU Database.
This thesis develops an enhanced version based on the original Iterative Label
Spreading Clustering which is specially designed for materials science, and aim to
become faster and have fewer hyperparameters.
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Subgraph frequency distribution estimation using graph neural network
Zhongren Chen*,
Xinyue Xu*,
Shengyi Jiang,
Hao Wang,
Lu Mi
KDD Deep Learning on Graphs, 2022
arXiv
Small subgraphs (graphlets) are important features to describe fundamental units of a large network.
Unfortunately due to the inherent complexity of this task, most of the existing methods are computationally intensive and inefficient.
In this work, we propose GNNS, a novel representational learning framework
that utilizes graph neural networks to sample subgraphs efficiently for estimating their frequency distribution.
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Classification Models for Medical Data with Interpretative Rules
Xinyue Xu,
Xiang Ding,
Zhenyue Qin,
Yang Liu
ICONIP, 2021
Paper
We use a variety of classification models to recognize the positive cases of SARS.
We conduct evaluation with two types of SARS datasets, numerical and categorical types.
For the sake of more clear interpretability, we also generate explanatory rules for the models.
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Hybrid model for network anomaly detection with gradient boosting decision trees
and tabtransformer
Xinyue Xu,
Xiaolu Zheng
ICASSP, 2021
Paper
In this paper, we present our solution for the ICASSP 2021 Network Anomaly Detection Challenge (NAD) challenge.
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Honours, Membership and Sevices
- Hong Kong PhD Fellowship, 2022 - 2026
- Conference Travel Allowance of HKPFS, 2023 - 2024
- HKUST RedBird PhD Award, 2022 - 2023
- IEEE Member, since 2021
- Australian Computer Society Member, since 2021
- Asia Pacific Neural Network Society Member, since 2021
- ICONIP 2023 PC Member
- Teaching Assistant for ELEC 1200 A System View of Communications: from Signals to Packets, 2023
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The source code is from Jon Barron's public academic website.
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