114 citations · 199 across the 23 of their papers we have counts for
5 papers · 1 filter
Adversarial Examples for Extreme Multilabel Text Classification
Mohammadreza Qaraei, Rohit Babbar
Extreme Multilabel Text Classification (XMTC) is a text classification problem in which, (i) the output space is extremely large, (ii) each data point may have multiple positive la…
Propensity-scored Probabilistic Label Trees
Marek Wydmuch, Kalina Jasinska-Kobus, Rohit Babbar +1
Extreme multi-label classification (XMLC) refers to the task of tagging instances with small subsets of relevant labels coming from an extremely large set of all possible labels. R…
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization
Erik Schultheis, Rohit Babbar
In this paper we show that a simple, data dependent way of setting the initial vector can be used to substantially speed up the training of linear one-versus-all (OVA) classifiers…
Unbiased Loss Functions for Multilabel Classification with Missing Labels
Erik Schultheis, Rohit Babbar
This paper considers binary and multilabel classification problems in a setting where labels are missing independently and with a known rate. Missing labels are a ubiquitous phenom…
InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification
Siddhant Kharbanda, Atmadeep Banerjee, Devaansh Gupta +2
Automatic annotation of short-text data to a large number of target labels, referred to as Short Text Extreme Classification, has found numerous applications including prediction o…