SML 2014 - 2nd Workshop on Scalable Machine Learning: Theory and Applications

The 2nd Workshop on Scalable Machine Learning: Theory and Applications
In conjunction with the IEEE International Conference on Big Data (IEEE BigData 2014) 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 big data analysis
Security big data analysis
Astronomy big data analysis
Biological 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 Submission
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") and submitted to the Submission Website https://wi-lab.com/cyberchair/2014/bigdata14/
Keynote Talks
Mikhail Bilenko, Microsoft Research
Eric Xing, Carnegie Mellow University
Ping Li, Rutgers University
Organizing Committee
Zenglin Xu, Purdue University
Haiqin Yang, The Chinese University of Hong Kong
Irwin King, The Chinese University of Hong Kong
Michael R. Lyu, The Chinese University of Hong Kong
Lihong Li, Microsoft Research