Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this tutorial, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.

Presenters

Willem Waegeman, Ghent University, Belgium
Krzysztof Dembczynski, Poznan Uviversity of Technology, Poland
Eyke Hüllermeier, University of Paderborn, Germany

Target audience

The tutorial intends to cover an overview of existing methods, while focussing on cross-domain methodologies. With the tutorial we aim to attract both researchers that are already active in one of the above domains, as well as researchers with little or no prior experience in multi-target prediction. As such, we believe that the tutorial will attract ECML attendees from diverse subfields of machine learning and with different background.

Slides

Can be found here.

Previous initiatives

International Workshop on Big Multi-Target Prediction, ECML/PKDD 2015 link
International Workshop on Multi-Target Prediction, ECML/PKDD 2014 link
Tutorial on Multi-Target Prediction, ICML 2013 link