Extreme Classification: Multi-class and Multi-label Learning in Extremely Large Label Spaces

Extreme classification, where one needs to deal with multi-class and multi-label problems involving a very large number of labels, has opened up a new research frontier in machine learning. Many challenging applications, such as photo or video annotation, web page categorization, gene function prediction, language modeling can benefit from being formulated as supervised learning tasks with millions, or even billions, of labels. 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 label.

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

* Large scale learning and distributed and parallel training
* Log-time and log-space prediction and prediction on a test-time budget
* Label embedding and tree-based approaches
* Crowd sourcing, preference elicitation and other data gathering techniques
* Bandits, semi-supervised learning and other approaches for dealing with training set biases and label noise
* Bandits with an extremely large number of arms
* Fine-grained classification
* Zero shot learning and extensible output spaces
* Tackling label polysemy, synonymy and correlations
* Structured output prediction and multi-task learning
* Learning from highly imbalanced data
* Dealing with tail labels and learning from very few data points per label
* PU learning and learning from missing and incorrect labels
* Feature extraction, feature sharing, lazy feature evaluation, etc.
* Performance evaluation
* Statistical analysis and generalization bounds
* Applications to ranking, recommendation, knowledge graph construction and other domains

The workshop aims to bring together researchers interested in these areas to encourage discussion and improve upon the state-of-the-art in extreme classification. In particular, we aim to bring together researchers from the natural language processing, computer vision and core machine learning communities to foster interaction and collaboration. Several leading researchers will present invited talks detailing the latest advances in the area. 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 recommender systems, computer vision, computational advertising, information retrieval and natural language processing. We expect a healthy participation from both industry and academia.

http://www.manikvarma.org/events/XC16/schedule.html