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CML 2016 : Constructive Machine Learning


When Dec 10, 2016 - Dec 10, 2016
Where Barcelona, Spain
Submission Deadline Nov 3, 2016
Notification Due Nov 24, 2016
Final Version Due Dec 1, 2016
Categories    machine learning   computer aided synthesis   automatic design   generative processes

Call For Papers

Call for Papers
NIPS 2016 Workshop on Constructive Machine Learning (NIPS CML)

A workshop at the Twenty-Ninth Annual Conference on Neural Information Processing Systems
(NIPS 2016)
Barcelona, Spain
Sat Dec 10th 08:00 AM -- 06:30 PM

Nov 3, 2016: Submission Deadline
Nov 24, 2016: Acceptance Notification
Dec 1, 2016: Final papers due
Dec 10, 2016: Workshop date

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.

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 use the NIPS style file, with a maximum of 4 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:

Ruslan Salakhutdinov (CMU, deep generative models)
Thorsten Joachims (Cornell, coactive learning)
Gisbert Schneider (ETH, de novo drug design)
Simon Colton (Goldsmiths University of London, computational creativity)
Douglas Eck (Google, music generation)
Ross Goodwin (NYU ITP, computational creative writing)
Florian Pinel (IBM, cognitive cooking)

Fabrizio Costa (University of Freiburg)
Thomas Gärtner (University of Nottingham)
Andrea Passerini (University of Trento)
François Pachet (SONY Computer Science Laboratory Paris)

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