Tutorials

1. Trustworthy and Efficient Machine Reasoning with Foundation Models
   Presenters: Zhanke Zhou, Chentao Cao, Jiangchao Yao
   Contact email: cszkzhou@comp.hkbu.edu.hk
   Webpage: https://trustworthy-machine-reasoning.github.io/acml26-tutorial.html

2. α-Divergence for Robust Statistical Learning: Theory, Algorithms and Applications
   Presenters: Abd-Krim Seghouane, Hangfei Zheng
   Contact email: Abd-Krim.Seghouane@unimelb.edu.au, Hangfei.Zheng@unimelb.edu.au
   Webpage: TBA

3. Topological Deep Learning for Robust Representation Learning: Foundations, Architectures, and Applications
   Presenters: Ali Zia, Abdelwahed Khamis
   Contact email: a.zia@latrobe.edu.au
   Webpage: https://alizia369.github.io/acml2026-tdl/

4. Advances in Multi-Modal Time Series: From Forecasting to Reasoning
   Presenters: Hung Le, Hiep Nguyen, Van Dai Doy
   Contact email: s225250685@deakin.edu.au
   Webpage: https://acml2026-mmts.github.io/

5. Generative Distributional Learning for Causal Inference
   Presenters: Xinwei Shen, Wei Huang
   Contact email: xwshen@uw.edu
   Webpage: TBA

6. Detecting Distribution Shifts in the Wild: Techniques and Future Perspectives
   Presenters: Bo Peng
   Contact email: Bo.Peng-7@student.uts.edu.au
   Webpage: TBA

7. From Continual Learning to Self-Evolving Agents
   Presenters: Tongtong Wu, Dong Gong, Reza Haffari
   Contact email: tongtong.wu@monash.edu, dong.gong@unsw.edu.au, Gholamreza.Haffari@monash.edu
   Webpage: TBA