Connectionists: CALL FOR PAPERS NIPS 2013 Workshop on Constructive Machine Learning

[andrea passerini]

Dear Colleagues,


it is our pleasure to invite submissions to the
*NIPS 2013 Workshop on Constructive Machine Learning*
9 or 10 December 2013 (to be determined)
Lake Tahoe, Nevada, USA

http://www.kdml-bonn.de/cml/
cml.nips2013 at gmail.com

Detailed information is below.

Do not hesitate to contact us for further information.


Best Regards,

 Andrea Passerini, Roman Garnett, and Thomas Gärtner

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*Overview*

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. The most common use of machine learning algorithms in this context is to predict the properties of candidate domain elements.

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. The concerned machine learning approaches are typically interactive (e.g., online- or active-learning algorithms) and have to deal with huge, relational in- and/or output spaces.

Many of the applications of constructive machine learning 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.

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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. Topics of interest (though not exhaustive) include:

Theory:
* 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

Applications:
* De novo drug design
* Generation of art (e.g., music composition)
* Synthetic biology
* 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 cml.nips2013 at gmail.com.

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*Important dates*

Submission deadline (tentative): October 9th, 2013
Acceptance decisions (tentative): October 23th, 2013

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*Invited speakers*

Ross King (University of Manchester, confirmed)
Bob Keller (Harvey Mudd College, confirmed)
Doug Turnbull (Ithaca College, confirmed)
Josh Tenenbaum (MIT, tentative)

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*Organizers*

Andrea Passerini (University of Trento)
Roman Garnett (University of Bonn)
Thomas Gärtner (University of Bonn and Fraunhofer IAIS)


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Andrea Passerini

Dipartimento di Ingegneria e Scienza dell'Informazione
Universita' degli Studi di Trento
Via Sommarive 5
38100, Povo di Trento - Italy
http://www.disi.unitn.it/~passerini
Phone: +39 0461 28 5224
Fax:   +39 0461 88 3935
email: passerini at disi.unitn.it
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