Domain Generalisation via Imprecise Learning
Anurag Singh Siu Lun Chau Shahine Bouabid Krikamol Muandet
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 machine learners due to these deployment
uncertainties. We introduce the Imprecise Domain Generalisation framework to
mitigate this, featuring an imprecise risk optimisation that allows learners to
stay imprecise by optimising against a continuous spectrum of generalisation
strategies during training, and a model framework that allows operators to
specify their generalisation preference at deployment. Supported by both
theoretical and empirical evidence, our work showcases the benefits of
integrating imprecision into domain generalisation.