Erdun Gao Researcher Kernels and Information Processing Systems (KIPS) Group Australian Institute for Machine Learning (AIML) The University of Adelaide Address: 206, AIML Building, Lot Fourteen, Adelaide Email: erdun.gao AT outlook.com

About Me

I am a Researcher at the AIML, working in the KIPS Group with Prof. Dino Sejdinovic. My long-term research goal is to build trustworthy decision-making systems through the lens of causal inference. My work places a strong emphasis on uncertainty and interpretability, as these principles are crucial for developing reliable systems for high-stakes decisions. Currently, my research focuses on key areas within this domain, including adaptive experimental design, learning from imperfect data, and data valuation. My broader interests lie at the intersection of causality and machine learning.

Education Background

• Ph.D. in Applied Statistics, 2021.01 - 2025.01
  The University of Melbourne
  Supervised by Dr. Mingming Gong, and Prof. Howard Bondell

• M.S. in Signal and Information Processing, 2017.09 - 2020.07
  Xi’an Jiaotong University
  Supervised by Prof. Zhibin Pan

• B.E. in Information Engineering, 2013.09 - 2017.07
  Xi’an Jiaotong University

Main Publications [Full List]

• Domain Generalization via Content Factors Isolation: A Two-level Latent Variable Modeling Approach, MLJ, 2025
  Erdun Gao, Howard Bondell, Shaoli Huang, Mingming Gong
  

• MissScore: High-Order Score Estimation in the Presence of Missing Data, ICML, 2025.
  Wenqin Liu, Haoze Hou, Erdun Gao, Biwei Huang, Qiuhong Ke, Howard Bondell, Mingming Gong
  

• Distraction is All You Need for Multimodal Large Language Model Jailbreaking, CVPR (Highlight), 2025.
  Zuopeng Yang, Jiluan Fan, Anli Yan, Erdun Gao, Xin Lin, Tao Li, Kanghua Mo, Changyu Dong
  

• Causality-aligned Prompt Learning via Diffusion-based Counterfactual Generation, ACMMM, 2025.
  Xinshu Li, Ruoyu Wang, Erdun Gao, Mingming Gong, Lina Yao
  

• A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error, ICLR, 2024.
  Erdun Gao, Howard Bondell, Wei Huang, Mingming Gong
  

• Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach, CLeaR, 2024.
  Wenqin Liu, Biwei Huang, Erdun Gao, Qiuhong Ke, Howard Bondell, Mingming Gong
  

• FedDAG: Federated DAG Structure Learning, TMLR, 2023.
  Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard Bondell
  

• MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models, NeurIPS, 2022.
  Erdun Gao*, Ignavier Ng*, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard Bondell
  

• Reversible data hiding based on novel pairwise PVO and annular merging strategy, Information Sciences, 2019.
  Erdun Gao, Zhibin Pan, Xinyi Gao.