Meta-Learning With Hierarchical Models Based on Similarity of Causal Mechanisms

arXiv:2310.12595v2 Announce Type: replace
Abstract: In this work the goal is to generalise to new data in a non-iid setting where datasets from related tasks are observed, each generated by a different causal mechanism, and the test dataset comes from the same task distribution. This setup is motivated by personalised medicine, where a patient is a task and complex diseases are heterogeneous across patients in cause and progression. The difficulty is that there usually is not enough data in one …

abstract arxiv cs.lg data dataset datasets distribution generated hierarchical meta meta-learning personalised setup stat.ml tasks test type work