Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

The International Classification of Diseases (ICD) is a standard for categorizing physical
conditions, which has been widely used for analyzing clinical data and monitoring health …

Extreme multi-label text classification (XMTC) is an important problem in the era of {\it big
data}, for tagging a given text with the most relevant multiple labels from an extremely large …

This paper develops the Parabel algorithm for extreme multi-label learning where the
objective is to learn classifiers that can annotate each data point with the most relevant …

This paper reformulates the problem of recommending related queries on a search engine
as an extreme multi-label learning task. Extreme multi-label learning aims to annotate each …

Scalability and accuracy are well recognized challenges in deep extreme multi-label
learning where the objective is to train architectures for automatically annotating a data point …

Extreme multi-label classification (XML) involves tagging a data point with its most relevant
subset of labels from an extremely large label set, with several applications such as product …

Deep extreme multi-label learning (XML) requires training deep architectures that can tag a
data point with its most relevant subset of labels from an extremely large label set. XML …

Due to the exponential growth of scientific publications on the Web, there is a pressing need
to tag each paper with fine-grained topics so that researchers can track their interested fields …

Abstract 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 …