5 citations · 10 across the 6 of their papers we have counts for
6 papers
ELMO: Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces
Jinbin Zhang, Nasib Ullah, Erik Schultheis +1
Large output spaces, also referred to as Extreme multilabel classification (XMC), is a setting that arises, e.g., in large-scale tagging and product-to-product recommendation, and…
Learning label-label correlations in Extreme Multi-label Classification via Label Features
Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis +3
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent w…
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis +1
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent a…
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…
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…