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SML@IEEE BigData 2013 : International Workshop on Scalable Machine Learning: Theory and Applications


When Oct 6, 2013 - Oct 6, 2013
Where Santa Clara, CA, USA
Submission Deadline Aug 22, 2013
Notification Due Aug 30, 2013
Final Version Due Sep 25, 2013
Categories    machine learning   scalable algorithms   big data   theory and applications

Call For Papers

We thank the following experts for accepting our invitation to give plenary talks:
* Mikhail Bilenko, Microsoft research
* Carlos Guestrin, University of Washington
* Alek Kolcz, Twitter
* Alex Smola, Carnegie Mellon University

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 of scalable machine learning technologies from theory and applications. The workshop is in conjunction with the 2013 IEEE International Conference on Big Data (IEEE Big Data 2013).

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

* 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

We call for original and unpublished research paper contribution of short (2-4 pages) and full (6-8 pages) manuscripts to the workshop using IEEE Computer Society Proceedings Manuscript Formatting (see submission information in

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. Also, one of the authors (or a qualified substitute) must give a presentation of the paper at the workshop.

The workshop papers will be part of the conference proceedings. They will be indexed by ieee explore.

* Irwin King, The Chinese University of Hong Kong
* Michael R. Lyu, The Chinese University of Hong Kong
* Michael Mahoney, Stanford University
* Zenglin Xu, Purdue University
* Haiqin Yang, The Chinese University of Hong Kong

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