NeurIPS 2020

Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping

Meta Review

The authors introduce a method for speeding up a group testing approach for multi-label classification. Thanks to this, the new algorithm can be used for problems with a large number of labels. The paper is clearly written. Some reviewers were underlining the incremental contribution, but majority of them agreed with the authors that improving complexity of the existing solution is a sufficient contribution. The authors should, however, revise their discussion on the complexity of clustering methods used by the label tree approaches. Certainly the variants of k-means used there are not scaling quadratically with the number of labels!