  Learning to transfer considers learning solutions to tasks in a such way that
relevant knowledge can be transferred from known task solutions to new, related
tasks. This is important for general learning, as well as for improving the
efficiency of the learning process. While techniques for learning to transfer
have been studied experimentally, we still lack a foundational description of
the problem that exposes what related tasks are, and how relationships between
tasks can be exploited constructively. In this work, we introduce a framework
using the differential geometric theory of foliations that provides such a
foundation.
