4 citations · 4 across the 1 of their papers we have counts for
3 papers
stat.ML2020★ 4 cited
Unbiased Loss Functions for Extreme Classification With Missing Labels
Erik Schultheis, Mohammadreza Qaraei, Priyanshu Gupta +1
The goal in extreme multi-label classification (XMC) is to tag an instance with a small subset of relevant labels from an extremely large set of possible labels. In addition to the…
cs.LG2019
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
Sujay Khandagale, Han Xiao, Rohit Babbar
Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a suite of a…
stat.ML2018
Adversarial Extreme Multi-label Classification
Rohit Babbar, Bernhard Schölkopf
The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labe…