Extreme Classification 2015: 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 an extremely large number of labels, has opened up a new research frontier in machine learning. Many challenging applications, such as photo, video and tweet annotation and web page categorization, can benefit from being formulated as supervised learning tasks with millions of labels. Extreme classification can also lead to a fresh perspective on other 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 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 foster discussion and improve upon the state-of-the-art in extreme classification. We also aim to bring researchers from the recommender systems, information retrieval, data mining and computer vision communities to discuss real world application scenarios, evaluation metrics, best practices, etc. 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 recommender systems, computer vision, computational advertising, information retrieval and natural language processing. We expect a healthy participation from both industry and academia.