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Big Learning 2012 : NIPS 2012 Workshop on Big Learning: Algorithms, Systems, and Tools

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Link: http://biglearn.org/
 
When Dec 7, 2012 - Dec 8, 2012
Where Lake Tahoe, Nevada, USA
Submission Deadline Oct 17, 2012
Notification Due Oct 27, 2012
Final Version Due Nov 14, 2012
Categories    machine learning
 

Call For Papers

Big Learning 2012: Algorithms, Systems, and Tools

NIPS 2012 Workshop (http://www.biglearn.org)

ORGANIZERS:
- Sameer Singh (UMass Amherst)
- John Duchi (UC Berkeley)
- Yucheng Low (Carnegie Mellon University)
- Joseph Gonzalez (UC Berkeley)

Submissions are solicited for a one day workshop on December 7-8 in Lake Tahoe, Nevada.

This workshop will address algorithms, systems, and real-world problem domains related to large-scale machine learning (“Big Learning”). With active research spanning machine learning, databases, parallel and distributed systems, parallel architectures, programming languages and abstractions, and even the sciences, Big Learning has attracted intense interest. This workshop will bring together experts across these diverse communities to discuss recent progress, share tools and software, identify pressing new challenges, and to exchange new ideas. Topics of interest include (but are not limited to):

- Big Data: Methods for managing large, unstructured, and/or streaming data; cleaning, visualization, interactive platforms for data understanding and interpretation; sketching and summarization techniques; sources of large datasets.
- Models & Algorithms: Machine learning algorithms for parallel, distributed, GPGPUs, or other novel architectures; theoretical analysis; distributed online algorithms; implementation and experimental evaluation; methods for distributed fault tolerance.
- Applications of Big Learning: Practical application studies and challenges of real-world system building; insights on end-users, common data characteristics (stream or batch); trade-offs between labeling strategies (e.g., curated or crowd-sourced).
- Tools, Software & Systems: Languages and libraries for large-scale parallel or distributed learning which leverage cloud computing, scalable storage (e.g. RDBMs, NoSQL, graph databases), and/or specialized hardware.

Submissions should be written as extended abstracts, no longer than 4 pages (excluding references) in the NIPS latex style. Relevant work previously presented in non-machine-learning conferences is strongly encouraged, though submitters should note this in their submission.

Submission Deadline: October 17th, 2012.

Please refer to the website for detailed submission instructions: www.biglearn.org

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