*Overview*

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), 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 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.

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*Contributions*

We welcome contributions on both theory and applications related to constructive machine learning problems. We also welcome submissions containing previously published content in fields related to machine learning, especially descriptions of real-world problems and applications. We welcome work-in-progress contributions, demo and position papers, as well as papers discussing potential research directions. Submission of previously published work or work under review is allowed. However, preference will be given to novel work or work that was not yet presented elsewhere. All double submissions must be clearly declared as such!

Submissions will be reviewed on the basis of relevance, significance, technical quality, and clarity. All accepted papers will be presented as posters and among them a few will be selected for the oral presentation.

Submissions should be in the ICML 2015 format, with a maximum of 3 pages (excluding references). 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 via easychair at the following link https://easychair.org/conferences/?conf=cml2015

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*Important dates*

Submission deadline: May 1, 2015
Acceptance notification: May 10, 2015
Early registration deadline ICML: May 15, 2015
Late breaking[*] papers submission deadline: June 1, 2015
Late breaking[*] papers acceptance notification: June 10, 2015
Camera ready: June 20, 2015
Workshop: July 10, 2015

[*]: late-breaking papers will go through the same review process as normal submissions, but authors will not have the chance to benefit from ICML early registration.


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*Confirmed invited speakers*

Michele Sebag (Université Paris-Sud, The Expert in the Loop).
Francois Pachet (Sony, Flow Machines).

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*Organizers*

Fabrizio Costa (University of Freiburg)
Roman Garnett (Washington University in St. Louis)
Thomas Gärtner (University of Bonn and Fraunhofer IAIS)
Andrea Passerini (University of Trento)