Cross-Space Active Learning on Graph Convolutional Networks

Yufei Tao · Hao WU · Shiyuan Deng

Ballroom 3 & 4
[ Abstract ] [ Livestream: Visit Theory ]
Wed 20 Jul 11:40 a.m. — 11:45 a.m. PDT
[ Paper PDF

This paper formalizes {\em cross-space} active learning on a graph convolutional network (GCN). The objective is to attain the most accurate hypothesis available in any of the instance spaces generated by the GCN. Subject to the objective, the challenge is to minimize the {\em label cost}, measured in the number of vertices whose labels are requested. Our study covers both {\em budget algorithms} which terminate after a designated number of label requests, and {\em verifiable algorithms} which terminate only after having found an accurate hypothesis. A new separation in label complexity between the two algorithm types is established. The separation is unique to GCNs.

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