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, 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 prediction
• Theoretical results on multi-target prediction
• Incorporation of domain knowledge in multi-target prediction methods
• Applications

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 2 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. We strongly encourage people that want to give a talk to submit an abstract. Full papers are rather meant for researchers that need a publication for travel funding or for people that want to obtain more detailed feedback about their work. This way we aim to provide a broad overview, and make it attractive and easy to attend by both senior as well as junior researchers, from both academia and industry. Full papers and extended abstracts should be formatted according to the official ECML/PKDD style files. Accepted papers will be made available online at the workshop website, but the workshop proceedings can be considered non-archival. Submissions need not be anonymous. All papers should be submitted as pdf via email to multitargetprediction@gmail.com .

The program committee will make a selection for oral presentations among full papers and abstracts to ensure a program that has academic quality but is also interesting and inspiring for the attendees. The remainder of the accepted submissions will be presented during a poster session and poster spotlight presentations.