Connectionists: CFP + Deadline Extension: NIPS Workshop on Big Learning 2013

[Sameer Singh]

Big Learning 2013: Advances in Algorithms and Data Management

*NIPS 2013 Workshop (http://www.biglearn.org)*

Submission Deadline: *October 25th, 2013*.
ORGANIZERS:

   - *Xinghao Pan* <http://www.eecs.berkeley.edu/~xinghao> (Berkeley)
   - *Haijie Gu* (CMU)
   - *Joseph Gonzalez* <http://www.cs.cmu.edu/~jegonzal> (Berkeley)
   - *Sameer Singh* <http://www.cs.umass.edu/~sameer> (UMass Amherst)
   - *Yucheng Low* <http://www.cs.cmu.edu/~ylow> (CMU)

Submissions are solicited for a one day workshop on Monday, December 9th at
Lake Tahoe, Nevada.

This workshop will address algorithms, systems, and real-world problem
domains related to large-scale machine learning (“Big Learning”). Big
Learning has attracted intense interest, with active research spanning
diverse fields. In particular, the machine learning and databases have
taken distinct approaches by developing new algorithms and data management
systems. 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):

   - *Scalable Data Systems*: Systems for large-scale parallel or
   distributed learning; implementations of machine learning models and
   algorithms in database management systems (DBMS); insights and discussions
   on properties (availability, scalability, correctness, etc.), strengths,
   and limitations of databases for Big Learning.
   - *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).

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.

Please refer to the website for detailed submission instructions:
Guidelines<http://biglearn.org/index.php/AuthorInfo>
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