Unsupervised Resource Allocation with Graph Neural Networks
  We present an approach for maximizing a global utility function by learning
how to allocate resources in an unsupervised way. We expect interactions
between allocation targets to be important and therefore propose to learn the
reward structure for near-optimal allocation policies with a GNN. By relaxing
the resource constraint, we can employ gradient-based optimization in contrast
to more standard evolutionary algorithms. Our algorithm is motivated by a
problem in modern astronomy, where one needs to select-based on limited initial
information-among $10^9$ galaxies those whose detailed measurement will lead to
optimal inference of the composition of the universe. Our technique presents a
way of flexibly learning an allocation strategy by only requiring forward
simulators for the physics of interest and the measurement process. We
anticipate that our technique will also find applications in a range of
resource allocation problems.
