HS2: Active learning over hypergraphs with pointwise and pairwise queries

I (Eli) Chien, Huozhi Zhou, Pan Li
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:2466-2475, 2019.

Abstract

We propose a hypergraph-based active learning scheme which we term HS2; HS2 generalizes the previously reported algorithm S2 originally proposed for graph-based active learning with pointwise queries. Our HS2 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 HS2 for the above described generalized settings. Both the theoretical and empirical results show that HS2 requires a significantly fewer number of queries than S2 when one uses S2 over a graph obtained from the corresponding hypergraph via clique expansion.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T $HS^2$: Active learning over hypergraphs with pointwise and pairwise queries %A I (Eli) Chien %A Huozhi Zhou %A Pan Li %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-chien19a %I PMLR %P 2466--2475 %U https://proceedings.mlr.press/v89/chien19a.html %V 89 %X 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.
APA
Chien, I.(., Zhou, H. & Li, P.. (2019). $HS^2$: Active learning over hypergraphs with pointwise and pairwise queries. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:2466-2475 Available from https://proceedings.mlr.press/v89/chien19a.html.

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