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Workshop on Scalable Machine Learning: Theory and Applications

posted Jun 24, 2013, 12:40 AM by Katayoun Farrahi

In conjunction with the 2013 IEEE International Conference on Big Data (IEEE Big Data 2013)

October 6, 2013, Santa Clara, CA, USA

Big Data are encountered in various areas, including Internet search, social networks, finance, business sectors, meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research. The huge volume, high velocity, significant variety, and low veracity bring challenges to current machine learning techniques. It is desirable to scale up machine learning techniques for modeling and analyzing the 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 ofscalable machine learning technologies from theory and applications.

Topics of Interest
Topics of interest include, but not limited to:
  • 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 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 multi-modality 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 such as location-based service, mobile networks, etc.
    • Smart cities
    • Astronomy
    • Biological data analysis
Important Dates
  • August 2, 2013: Due date for workshop papers submission
  • August 30, 2013: Notification of paper decision to authors
  • September 25, 2013: Camera-ready of accepted papers
  • October 6 2013: Workshop