Cross-Space Active Learning on Graph Convolutional Networks
Abstract
This paper formalizes 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 label cost, measured in the number of vertices whose labels are requested. Our study covers both budget algorithms which terminate after a designated number of label requests, and 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.
Cite
Text
Tao et al. "Cross-Space Active Learning on Graph Convolutional Networks." International Conference on Machine Learning, 2022.Markdown
[Tao et al. "Cross-Space Active Learning on Graph Convolutional Networks." International Conference on Machine Learning, 2022.](https://mlanthology.org/icml/2022/tao2022icml-crossspace/)BibTeX
@inproceedings{tao2022icml-crossspace,
title = {{Cross-Space Active Learning on Graph Convolutional Networks}},
author = {Tao, Yufei and Wu, Hao and Deng, Shiyuan},
booktitle = {International Conference on Machine Learning},
year = {2022},
pages = {21133-21145},
volume = {162},
url = {https://mlanthology.org/icml/2022/tao2022icml-crossspace/}
}