Domain Generalisation via Imprecise Learning

arXiv:2404.04669v1 Announce Type: new
Abstract: Out-of-distribution (OOD) generalisation is challenging because it involves not only learning from empirical data, but also deciding among various notions of generalisation, e.g., optimising the average-case risk, worst-case risk, or interpolations thereof. While this choice should in principle be made by the model operator like medical doctors, this information might not always be available at training time. The institutional separation between machine learners and model operators leads to arbitrary commitments to specific generalisation strategies by …

abstract arxiv case cs.lg data distribution doctors domain information medical risk type via