In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We
release a new dataset of 57k legislative documents from EURLEX, annotated with~ 4.3 k …

Efficient distributed numerical word representation models (word embeddings) combined
with modern machine learning algorithms have recently yielded considerable improvement …

The choice of the loss function is critical in extreme multi-label learning where the objective
is to annotate each data point with the most relevant subset of labels from an extremely large …

We consider the extreme multi-label text classification (XMC) problem: given an input text,
return the most relevant labels from a large label collection. For example, the input text could …

Extreme multi-label classification refers to supervised multi-label learning involving
hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit …

Large multi-label datasets contain labels that occur thousands of times (frequent group),
those that occur only a few times (few-shot group), and labels that never appear in the …

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving
hundreds of thousands or even millions of labels. In this paper, we develop a suite of …

Abstract We consider Multiclass and Multilabel classification with extremely large number of
classes, of which only few are labeled to each instance. In such setting, standard methods …