“We consider semi-supervised binary classification for applications in which data points are naturally grouped … and the labeled data is biased. … The groups overlap in the feature space and consequently the input-output patterns are related across the groups. To model the inherent structure in such data, we assume the partition-projected class-conditional invariance across groups. … We demonstrate that under this assumption, the group carries additional information about the class, over the group-agnostic features, with provably improved area under the ROC curve.”

Find the paper and full list of authors in the AAAI Conference on Artificial Intelligence proceedings.