Erdun Gao Postdoctoral Researcher Centre for Augmented Reasoning Australian Institute for Machine Learning (AIML) The University of Adelaide Address: 206, AIML Building, Lot Fourteen, Adelaide E-mail: erdun.gao@adelaide.edu.au

Profile

I am currently a Postdoctoral Researcher at the Australian Institute for Machine Learning (AIML), working in the KIPS group with Prof. Dino Sejdinovic. Additionally, I am a final-year Ph.D. student at the School of Mathematics and Statistics, The University of Melbourne, working with the Causal Learning & Reasoning Group. My research is supervised by Dr. Mingming Gong and co-supervised by Prof. Howard Bondell.

My long-term research interests focus on ensuring reliable decision-making, driven by my concern about systems that make confident yet incorrect predictions and conclusions. Recently, my work has concentrated on the intersection of causality and machine learning, exploring areas such as causal discovery, treatment effect estimation, and causal machine learning.

Education Background

• Ph.D. in Applied Statistics, 2021.01 - Present
  The University of Melbourne

• M.S. in Signal and Information Processing, 2017.09 - 2020.07
  Xi’an Jiaotong University

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

Main PUBS [Full PUBS]

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

• 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.