Constructive machine learning describes a class of related machine learning problems where the ultimate goal of learning is not to find a good model of the data but instead to find one or more particular instances of the domain which are likely to exhibit desired properties. While traditional approaches choose these domain instances from a given set/databases of unlabeled domain instances, constructive machine learning is typically iterative and searches an infinite or exponentially large instance space.

With this workshop we want to bring together domain experts employing machine learning tools in constructive processes and machine learners investigating novel approaches or theories concerning constructive processes as a whole. Interesting applications are in the domains of chemistry (e.g. de novo drug design), biology (e.g. gene design, metabolic path design, RNA polymer design), computer science (e.g. automatic software generation, resource network allocation), art (e.g. music or poetry composition), gaming (e.g. character or level construction), education (e.g. personalized curricula design), services (e.g. personalized travel itinerary or insurance policy composition), layout design (e.g. urban planning, furniture arrangement, advertisement composition), alimentary (e.g. generation of novel food recipes or cocktails). Interesting approaches include but are not limited to: structured output learning, transfer or multi-task learning of generative models, active search or online optimisation over relational domains, adaptive sampling in structured domains, bayesian optimization, preference elicitation, constraints acquisition and learning with constraints.

Many of the applications of constructive machine learning, including the ones mentioned above, are primarily considered in their respective application domain research area but are hardly present at machine learning conferences. By bringing together domain experts and machine learners working on constructive ML, we hope to bridge this gap between the communities.