Extreme Classification: Multi-Class & Multi-Label Learning with Millions of Categories

Extreme classification, where one needs to deal with multi-class and multi-label problems involving a very large number of categories, has opened up a new research frontier in machine learning. Many challenging applications, such as photo and video annotation and web page categorization, can benefit from being formulated as supervised learning tasks with millions, or even billions, of categories. Extreme classification can also give a fresh perspective on core learning problems such as ranking and recommendation by reformulating them as multi-class/label tasks where each item to be ranked or recommended is a separate category.

Extreme classification raises a number of interesting research questions including those related to:

* Large scale learning and distributed and parallel training
* Efficient sub-linear prediction and prediction on a test-time budget
* Crowd sourcing and other efficient techniques for harvesting training data
* Dealing with training set biases and label noise
* Fine-grained classification
* Tackling label polysemy, synonymy and correlations
* Structured output prediction and multi-task learning
* Learning from highly imbalanced data
* Learning from very few data points per category
* Learning from missing and incorrect labels
* Feature extraction, feature sharing, lazy feature evaluation, etc.
* Performance evaluation
* Statistical analysis and generalization bounds

The workshop aims to bring together researchers interested in these areas to foster discussion and improve upon the state-of-the-art in extreme classification. Several leading researchers will present invited talks detailing the latest advances in the field. We also seek extended abstracts presenting work in progress which will be reviewed for acceptance as spotlight+poster or a talk. The workshop should be of interest to researchers in core supervised learning as well as application domains such as computer vision, computational advertising, information retrieval and natural language processing. We expect a healthy participation from both industry and academia.