posted by user: xinghao || 4210 views || tracked by 22 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

NIPS 2018   The Thirty-second Annual Conference on Neural Information Processing Systems
ECCV 2018   European Conference on Computer Vision
ADBIS 2018   The 22nd European Conference on Advances in Databases and Information Systems
ICML 2018   The 35th International Conference on Machine Learning
ICMLB 2018   International Conference on Machine Learning and Big Data 2018
IJCAI 2018   International Joint Conferences on Artificial Intelligence Organization
IMMM 2018   The Eighth International Conference on Advances in Information Mining and Management
ACL 2018   56th Annual Meeting of the Association for Computational Linguistics
ATBD 2018   Applications and Technologies in Big Data
BigData Congress 2018   The 7th International Congress on Big Data