Traditional methods in machine learning and statistics provide data-driven models for predicting one-dimensional targets, such as binary outputs in classification and real-valued outputs in regression. In recent years, novel application domains have triggered fundamental research on more complicated problems where multi-target predictions are required. Such problems arise in diverse application domains, such as document categorization, tag recommendation of images, video and music, information retrieval, natural language processing, drug discovery, biology, etc.

Specific multi-target prediction problems have been studied in a variety of subfields of machine learning and statistics, such as multi-label classification, multivariate regression, pairwise learning, structured output prediction, recommender systems, preference learning, multi-task learning, dyadic prediction and collective learning. Despite their commonalities, work on solving problems in the above domains has typically been performed in isolation, without much interaction between the different sub-communities. The main goal of the workshop is to construct a unifying discussion platform for the above-mentioned subfields of machine learning, by focusing on the simultaneous prediction of multiple, mutually dependent output variables. Contributions might concern (but are not limited to) the following topics:

- Multi-label classification
- Multivariate regression / Multi-output regression
- Structured output prediction
- Multi-task learning and transfer learning
- Constructive machine learning
- Pairwise learning / dyadic prediction
- Label ranking
- Matrix factorization and collaborative filtering methods
- Recommender systems
- Sequence learning, time series prediction and data stream mining
- Collective classification and inference
- Conditional random fields, structured SVMs and graphical models
- Evaluation of multi-target prediction systems
- Data sampling in multi-target prediction
- Efficient inference and large-scale learning in multi-target pred.
- Theoretical results on multi-target prediction
- Incorporation of domain knowledge in multi-target prediction

Full papers can take up to 8 pages and they need to report original work that has not been published yet. Extended abstracts have a maximum of 3 pages and can also concern a discussion of a given topic or past published work, if the precise references to the original publication are mentioned. Instructions can be found at the workshop website.