Research
Hierarchical Models Based on Similarity of Causal Mechanisms
Given a dataset of related tasks, it is often beneficial to pool learning across tasks using techniques such as meta-learning and multi-task learning. Our new preprint studies an important setting where tasks are generated by different causal models, which occurs in medical/biological data. We discuss this problem and present a new probabilistic machine learning technique for predictive modelling in this setting, which utilises the similarity structure of the tasks.
Publication details and other resources
See below for links to the preprint and code.
Sophie Wharrie, Samuel Kaski, Meta-Learning With Hierarchical Models Based on Similarity of Causal Mechanisms, arXiv preprint, 2024, https://arxiv.org/abs/2310.12595
