In such situations, we need methods that can leverage the labeled and unlabeled data in a past event (we refer to this as a source domain), and that can adapt to a new event (we refer to this as a target domain) without requiring any labeled data in the new event.
we need models that can do domain adaptation to deal with the distribution drift between the domains and semi-supervised learning to leverage the unlabeled data in both domains.