Connectionists: IEEE BigData 2014 Workshop on Scalable Machine Learning: Theory and Applications

[Zenglin Xu]

 ------- Apology for multiple postings --------

IEEE BigData 2014 Workshop on Scalable Machine Learning: Theory and
Applications
*In conjunction with the **IEEE International Conference on Big Data** (IEEE
BigData 2014 <http://icdm2014.sfu.ca/home.html>) on **October  27,
2014,  **Washington
DC, USA*
Big data are encountered in various areas, including Internet search,
social networks, finance, business sectors, meteorology, genomics, complex
physics simulations, biological and environmental research. Machine
learning as an important tool of big data analytics is playing more and
more important roles in the big data era. However, the characteristics of
large volume, high velocity, variety and veracity bring challenges to
current machine learning techniques. It is therefore desirable to discuss
 (1) how to scale up existing machine learning techniques for modeling and
analyzing big data from various domains;
 (2) how to design new machine learning algorithms for various
parallel/distributed machine learning platforms (such as Hadoop, GraphLab,
Spark, etc.); and
 (3) how to design universal machine learning interfaces for GPUs or cloud
computing architectures, and so on.

*Topics of Interest*

   - *Distributed data analytics architectures*
      - Data separation and integration techniques
      - Machine learning algorithms for GPUs
      - Machine learning algorithms for clouds
      - Machine learning algorithms for clusters
   - *Theory and algorithms of data reduction techniques for big data*
      - Online/incremental/stochastic learning algorithms
      - Random projection
      - Hashing techniques
      - Data sampling algorithms
   - *Theory and algorithms of large-scale matrix approximation*
      - Bound analysis of matrix approximation algorithms
      - Distributed matrix factorization
      - Distributed multiway array analysis
      - Online dictionary learning
      - Distributed topic modeling algorithms
   - *Heterogeneous learning on big multimodal data*
      - Multiview learning
      - Multitask learning
      - Transfer learning
      - Semi-supervised learning
      - Active learning
   - *Temporal analysis and spatial analysis in big data*
      - Real-time analysis for data stream
      - Trend prediction in financial data
      - Topic detection in instant message systems
      - Real time modeling of events in dynamic networks
      - Spatial modeling on maps
   - *Scalable machine learning in large graphs*
      - Communities discovery and analysis in social networks
      - Link prediction in networks
      - Anomaly detection in social networks
      - Fusion of information from multiple blogs, rating systems, and
      social networks
      - Integration of text, videos, images, sounds in social networks
      - Recommender systems
   - *Novel applications of scalable machine learning in big data*
      - Decision making with big data
      - Counterfactual reasoning with big data
      - Medical/health informatics big data analysis
      - Security big data analysis
      - Astronomy big data analysis
      - Biological big data analysis
      - Urban/smart city big data analysis
      - Education big data analysis

*Important Dates*

   - August 30, 2014: Due date for workshop paper submission
   - September 20, 2014: Notification of paper decision to authors
   - October 5, 2014: Camera-ready of accepted papers
   - October 27-30, 2014: Workshop

*Paper Sub**mission*
      We call for original and unpublished research contributions of (up to
8 pages and IEEE double-column format) manuscripts to the workshop. Papers
should be formatted to IEEE Computer Society Proceedings Manuscript
Formatting Guidelines (see the link to "Paper Format
<http://www.ieee.org/conferences_events/conferences/publishing/templates.html>")
and submitted to the Submission Website
<https://wi-lab.com/cyberchair/2014/bigdata14/scripts/submit.php?subarea=S2&undisplay_detail=1&wh=/cyberchair/2014/bigdata14/scripts/ws_submit.php>
.

*Keynote Talks*

   - Mikhail Bilenko
   <http://research.microsoft.com/en-us/um/people/mbilenko/>, Microsoft
   Research
   - Eric Xing <http://www.cs.cmu.edu/~epxing/>, Carnegie Mellow University
   - Ping Li, <http://www.stat.rutgers.edu/home/pingli/> Rutgers University

*Organizing Committee*

   - Zenglin Xu <https://www.cs.purdue.edu/homes/xu218/>, University of
   Electronic Science and Technology of China & Purdue University
   - Haiqin Yang <http://appsrv.cse.cuhk.edu.hk/~hqyang/doku.php>, The
   Chinese University of Hong Kong
   - Irwin King <http://www.cse.cuhk.edu.hk/~king>, The Chinese University
   of Hong Kong
   - Michael R. Lyu <http://www.cse.cuhk.edu.hk/~lyu>, The Chinese
   University of Hong Kong
   - Lihong Li <http://research.microsoft.com/en-us/people/lihongli/>,
Microsoft
   Research
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20140724/c6be896b/attachment.html>