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 Australian Institute for Machine Learning (AIML), where I am a member of the Kernels and Information Processing Systems (KIPS) Group led by Prof. Dino Sejdinovic. I received my Ph.D. in Applied Statistics (2025) from The University of Melbourne, where I was part of the Machine Learning and Reasoning (MLR) Group, advised by Dr. Mingming Gong and Prof. Howard Bondell. I also hold an M.S. in Signal and Information Processing (2020), supervised by Prof. Zhibin Pan, and a B.E. in Information Engineering (2017), both from Xi’an Jiaotong University.

Research Interests

My long-term research goal is to build reliable decision-making systems, with a focus on causal inference and uncertainty quantification. I believe that uncovering cause-and-effect relationships and properly accounting for uncertainty are key to making AI systems both robust and interpretable in high-stakes scenarios. I also occasionally work on broader machine learning problems related to data and models.

My recent specific interests include:

• Core Causality: Causal Discovery, Causal Effect Estimation, Causal Experimental Design.
• Causality & Machine Learning: Causal Foundation Models, Causal Generative Models.
• Machine Learning: Data Valuation, Context-Informed Learning, Uncertainty in LLMs.

Main Papers

[Full List] [Google Scholar]

• ActiveCQ: Active Estimation of Causal Quantities, Preprint, 2025.
  Erdun Gao, Dino Sejdinovic
  

• Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation, Preprint, 2025.
  Erdun Gao, Jake Fawkes, Dino Sejdinovic
  

• 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
  

• 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