SML 2013 - International Workshop on Scalable Machine Learning: Theory and Applications

Workshop on Scalable Machine Learning: Theory and Applications
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 of scalable 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
Submission Information
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.
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.