  Multiple Instance Learning (MIL) is a weakly supervised learning problem
where the aim is to assign labels to sets or bags of instances, as opposed to
traditional supervised learning where each instance is assumed to be
independent and identically distributed (IID) and is to be labeled
individually. Recent work has shown promising results for neural network models
in the MIL setting. Instead of focusing on each instance, these models are
trained in an end-to-end fashion to learn effective bag-level representations
by suitably combining permutation invariant pooling techniques with neural
architectures. In this paper, we consider modelling the interactions between
bags using a graph and employ Graph Neural Networks (GNNs) to facilitate
end-to-end learning. Since a meaningful graph representing dependencies between
bags is rarely available, we propose to use a Bayesian GNN framework that can
generate a likely graph structure for scenarios where there is uncertainty in
the graph or when no graph is available. Empirical results demonstrate the
efficacy of the proposed technique for several MIL benchmark tasks and a
distribution regression task.
