AISTATS 2019poster25 citations

$HS^2$: Active learning over hypergraphs with pointwise and pairwise queries

I (Eli) Chien, Huozhi Zhou, Pan Li

Abstract

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.

BibTeX
@InProceedings{pmlr-v89-chien19a,
  title = 	 {$HS^2$: Active learning over hypergraphs with pointwise and pairwise queries},
  author =       {Chien, I (Eli) and Zhou, Huozhi and Li, Pan},
  booktitle = 	 {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
  pages = 	 {2466--2475},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Sugiyama, Masashi},
  volume = 	 {89},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {16--18 Apr},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v89/chien19a/chien19a.pdf},
  url = 	 {https://proceedings.mlr.press/v89/chien19a.html},
  abstract = 	 {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.}
}
$HS^2$: Active learning over hypergraphs with pointwise and pairwise queries · AISTATS 2019