Xinyue XU

I am currently a PhD Candidate at The Hong Kong University of Science and Technology, advised by Prof. Xiaomeng Li and co-advised by Prof. Hao Wang. Prior of this, I completed my Bachelor degree (with Honours) at The Australian National University, where I had the privilege of being advised by Senior Prof. Amanda Barnard and supervised by Dr. Amanda Parker. My research journey includes experiences as a Research Assistant at MIT's Computational Connectomics Group lead by Prof. Nir Shavit, where I had the opportunity to collaborate with Prof. Hao Wang and Dr. Lu Mi. Furthermore, I had served as a Research Assistant under the supervision of Dr. Gang Guo at NUS.

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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)

Publications

"*" indicates equal contribution, "_" indicates equal advising.

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.

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.

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.

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.

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.

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.

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.

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

The source code is from Jon Barron's public academic website.