posted by user: xinghao || 5923 views || tracked by 21 users: [display]

Big Learning 2013 : NIPS 2013 Workshop on Big Learning: Advances in Algorithms and Data Management

FacebookTwitterLinkedInGoogle

Link: http://biglearn.org/
 
When Dec 9, 2013 - Dec 9, 2013
Where Lake Tahoe, NV
Submission Deadline Oct 25, 2013
Categories    big data   machine learning   database systems   artificial intelligence
 

Call For Papers

*Submission deadline extended to Oct 25, 2013*

Big Learning 2013: Advances in Algorithms and Data Management
NIPS 2013 Workshop (http://www.biglearn.org)

ORGANIZERS:

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 25th, 2013.

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

Related Resources

ML_BDA 2021   Special Issue on Machine Learning Technologies for Big Data Analytics
IJCAI 2021   30th International Joint Conference on Artificial Intelligence
ai4i 2021   Artificial Intelligence for Industries (ai4i 2021)
ICDM 2021   21th Industrial Conference on Data Mining
JCC-BD&ET 2021   9th Conference on Cloud Computing, Big Data & Emerging Topics--Springer CCIS (Ei Compendex & Scopus)
IARCE 2021-Ei Compendex & Scopus 2021   2021 5th International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2021)
CCBD--Ei Compendex & Scopus 2021   2021 The 8th International Conference on Cloud Computing and Big Data (CCBD 2021)--Ei Compendex & Scopus
BDIoT 2021   5th International Conference on Big Data and Internet of Things - BDIoT’21
MLDM 2021   17th International Conference on Machine Learning and Data Mining