“Big Learn” Workshop at NIPS Call for Papers

AMPLab researchers Xinghao Pan and Joey Gonzalez are helping to organize a one day workshop on scalable machine learning at the upcoming NIPS 2013 conference.   The paper deadline is Oct 9th.   The call for papers is below:


Call For Papers

Big Learning 2013: Advances in Algorithms and Data Management

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


  • Xinghao Pan (UC Berkeley)
  • Haijie Gu (Carnegie Mellon University)
  • Joseph Gonzalez (UC Berkeley)
  • Sameer Singh (University of Washington)
  • Yucheng Low (GraphLab)

Submissions are solicited for a one day workshop on 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.

Submission Deadline: October 9th, 2013.

Please refer to the workshop website for detailed submission instructions: Guidelines