$HS^2$: Active learning over hypergraphs with pointwise and pairwise queries
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:2466-2475, 2019.
We propose a hypergraph-based active learning scheme which we term $HS^2$; $HS^2$ generalizes the previously reported algorithm $S^2$ originally proposed for graph-based active learning with pointwise queries. Our $HS^2$ method can accommodate hypergraph structures and allows one to ask both pointwise queries and pairwise queries. Based on a novel parametric system particularly designed for hypergraphs, we derive theoretical results on the query complexity of $HS^2$ for the above described generalized settings. Both the theoretical and empirical results show that $HS^2$ requires a significantly fewer number of queries than $S^2$ when one uses $S^2$ over a graph obtained from the corresponding hypergraph via clique expansion.