State-of-the-art supervised NLP models achieve high accuracy but are also susceptible to
failures on inputs from low-data regimes, such as domains that are not represented in …

The task of annotating a data point with labels most relevant to it from a large universe of
labels is referred to as Extreme Classification (XC). State-of-the-art XC methods have …

This paper develops the MUFIN technique for extreme classification (XC) tasks with millions
of labels where datapoints and labels are endowed with visual and textual descriptors …

This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to
predict multiple labels for each instance from an extremely large label space. While existing …

Abstract The goal of Extreme Multi-label Classification (XC) is to learn representations that
enable mapping input texts to the most relevant subset of labels selected from an extremely …

Negative sampling is a common approach for making the training of deep models in
classification problems with very large output spaces, known as extreme multilabel …

Extreme Multi-label Classification (XMC) involves predicting a subset of relevant labels from
an extremely large label space, given an input query and labels with textual features …

Personalized retrieval seeks to retrieve items relevant to a user event (eg a page visit or a
query) that are adapted to the user's personal preferences. For example, two users who …

We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-
shot) items can arrive continuously at a rapid pace. Conventional Siamese-style approaches …

Extreme Multi-label Learning (XMC) is a task that allocates the most relevant labels for an
instance from a predefined label set. Extreme Zero-shot XMC (EZ-XMC) is a special setting …