Extreme Classification

Extreme classification is a rapidly growing research area within machine learning focusing on
multi-class and multi-label problems involving an extremely large number of labels (even more
than a million). Many applications of extreme classification have been found in diverse areas
ranging from language modeling to document tagging in NLP, face recognition to learning universal feature representations in computer vision, gene function prediction in bioinformatics, etc.
Extreme classification has also opened up a new paradigm for key industrial applications such
as ranking and recommendation by reformulating them as multi-label learning tasks where each
item to be ranked or recommended is treated as a separate label. Such reformulations have led to
significant gains over traditional collaborative filtering and content-based recommendation techniques. Consequently, extreme classifiers have been deployed in many real-world applications in
industry.

Extreme classification has raised many new research challenges beyond the pale of traditional
machine learning including developing log-time and log-space algorithms, deriving theoretical
bounds that scale logarithmically with the number of labels, learning from biased training data,
developing performance metrics, etc. The seminar aimed at bringing together experts in machine
learning, NLP, computer vision, web search and recommendation from academia and industry
to make progress on these problems. We believe that this seminar has encouraged the interdisciplinary collaborations in the area of extreme classification, started discussion on identification
of thrust areas and important research problems, motivated to improve the algorithms upon the
state-of-the-art, as well to work on the theoretical foundations of extreme classification.