Constructive Machine Learning

In many real-world applications, machine learning algorithms are employed as a tool in a ''constructive process''. These processes are similar to the general knowledge-discovery process but have a more specific goal: the construction of one-or-more domain elements with particular properties. In 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 include but are not limited to: image synthesis, drug and protein design, computational cooking, generation of art (paintings, music, poetry). Interesting approaches include but are not limited to: deep generative learning, active approaches to structured output learning, transfer or multi-task learning of generative models, active search or online optimization over relational domains, 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.