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 …

Large language models (LLMs) have been recently leveraged as training data generators
for various natural language processing (NLP) tasks. While previous research has explored …

Large scale recommender models find most relevant items from huge catalogs, and they
play a critical role in modern search and recommendation systems. To model the input …

Extreme multi-label text classification~(XMC) seeks to find relevant labels from an extreme
large label collection for a given text input. Many real-world applications can be formulated …

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 …

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 …

We consider learning a multi-class classification model in the federated setting, where each
user has access to the positive data associated with only a single class. As a result, during …

Hierarchical multi-label text classification (HMTC) aims to tag each document with a set of
classes from a class hierarchy. Most existing HMTC methods train classifiers using massive …

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 …