In conjunction with the 2013 IEEE International Conference on Big Data (IEEE Big Data 2013)
Workshop on Scalable Machine Learning: Theory and Applications
October 6, 2013, Ballroom D (Ba-D), Hyatt Regency Santa ClaraSanta Clara, CA, USA

Big data are encountered in various areas, including Internet search, social networks, finance, business sectors, meteorology, genomics, connectomics, complex physics simulations, biological and environmental research.  The characteristics of volume, velocity, variety and veracity bring challenges to current machine learning techniques.  It is desirable to scale up machine learning techniques for modeling and analyzing big data from various domains.

The workshop aims to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art theories and applications of scalable machine learning technologies.

We plan to have an exciting and dynamic technical program, which may include plenary talks, oral presentations, posters, and panel discussions. The plenary speakers include:

  • Topics of Interest
    • Distributed machine learning 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
      • Parallel matrix factorization
      • Parallel multiway array factorization
      • 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
      • Spacial 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
      • Authority identification and influence measurement in social networks
      • Fusion of information from multiple blogs, rating systems, and social networks
      • Integration of text, videos, images, sounds in social media
      • Recommender systems
    • Novel applications of scalable machine learning in
      • Healthcare
      • Cybersecurity
      • Mobile computing
      • Smart cities
      • Astronomy
      • Biological data analysis

  • Important Dates
  • August 22, 2013:   Due date for workshop papers submission
    August 30, 2013:   Notification of paper decision to authors
    September 10, 2013:   Camera-ready of accepted papers
    October 6, 2013:   Workshop
  • Submission Information
  • We call for original and unpublished research contributions of short (2-4 pages) and full (6-8 pages) manuscripts to the workshop using IEEE Computer Society Proceedings Manuscript Formatting: 8.5" x 11" (DOCPDFLaTex Macro

    Papers should be submitted via the online submission system.  If you do not have an account, you will be asked to sign up for an account.   Please select "Workshop/Scalable Machine Learning: Theory and Algorithms" when you submit papers.  

    Each accepted paper is required at least a workshop registration regardless of the status of the registered author.  One of the authors (or a qualified substitute) must give a presentation of the paper at the workshop. 

    All accepted workshop papers will be included in the conference proceedings and indexed by IEEE Xplore.