Unsupervised Resource Allocation with Graph Neural Networks
This addresses resource allocation challenges in fields like astronomy, offering a flexible approach for problems with complex interactions, though it appears incremental as it builds on existing GNN and optimization techniques.
The paper tackles the problem of resource allocation by learning an unsupervised strategy using Graph Neural Networks (GNNs) to maximize a global utility function, achieving a method that can handle large-scale problems like selecting among 10^9 galaxies for optimal inference in astronomy.
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.