9h00 Opening
9h10 Invited talk Charles Elkan, Massive sparse efficient multi-label learning
9h50 Greg Tsoumakas: Drawing parallels between multi-label classification and multi-target regression
10h15 Jesse Read: Classifier Chains, Trees and Graphs for Multi-target learning
10h40 coffee break

11h00 Invited talk Christoph Lampert: Predicting multiple structured outputs
11h40 Andrea Passerini: Structured learning modulo theories
12h05 Katrin Ullrich: Kernel Corresponding Projections for Orphan Targets
12h30 Lunch break

14h00 Invited talk Cedric Archambeau: Multi-task learning, a Bayesian approach
14h40 Hongyu Su: structured prediction of network response
15h05 Hossein Amirkhani: Improving Bayesian network learning using heterogeneous experts
15h30 Coffee break

16h00 Invited talk Pierre Geurts: Biological network inference with multi-target prediction methods
16h40 Wouter Duivesteijn: A Short Survey of Exceptional Model Mining --- Exploring Unusual Interactions Between Multiple Targets
17h05 Jan Verwaeren: Incorporating domain knowledge in multivariate regression models
17h30 Closing remarks