NIPS CML ws 2013 : NIPS 2013 Workshop on Constructive Machine Learning
Call For Papers
It is our pleasure to invite submissions on all aspects of Constructive Machine Learning.
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. Topics of interest (though not exhaustive) include:
Active approaches for structured output learning
Transfer and multi-task learning of generative models
Active search and online optimization in relational domains
Learning with constraints
Integrating learning and search
De novo drug design
Generation of art (e.g., music composition)
Construction of game levels
Generation of novel food recipes
Creation of music playlists, travel itineraries, etc.
We welcome work-in-progress contributions, 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 NIPS 2013 format, 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 as pdf via email to firstname.lastname@example.org.
Submission deadline (tentative): October 9th, 2013
Acceptance decisions (tentative): October 23th, 2013
Ross King (University of Manchester, confirmed)
Bob Keller (Harvey Mudd College, confirmed)
Doug Turnbull (Ithaca College, confirmed)
Josh Tenenbaum (MIT, tentative)
Andrea Passerini (University of Trento)
Roman Garnett (University of Bonn)
Thomas Gärtner (University of Bonn and Fraunhofer IAIS)