  The Document Set Expansion (DSE) task involves identifying relevant documents
from large collections based on a limited set of example documents. Previous
research has highlighted Positive and Unlabeled (PU) learning as a promising
approach for this task. However, most PU methods rely on the unrealistic
assumption of knowing the class prior for positive samples in the collection.
To address this limitation, this paper introduces a novel PU learning framework
that utilizes intractable density estimation models. Experiments conducted on
PubMed and Covid datasets in a transductive setting showcase the effectiveness
of the proposed method for DSE. Code is available from
https://github.com/Beautifuldog01/Document-set-expansion-puDE.
