BigData 2013 - New Learning Frameworks and Models for BigData

Huge amounts of data are now easily and legally available on the Web. This data is generally heterogeneous and merely structured. Machine learning models which have been developed to automatically retrieve, classify or cluster observations on large yet homogeneous data collections have to be rethought. Indeed, many challenging problems, inevitably associated to Big Data, have manifested the needs for tradeoffs between the two conflicting goals of speed and accuracy. This has led to some recent initiatives in both theory and practice and has highly motivated the interest of the Machine Learning community. Further theoretical challenges include how to tackle problems with large number of target classes, appropriate optimization techniques to handle big data problems. Structured/sequential prediction models for big data problems such as prediction in hierarchy of classes has also gained importance in recent years.
Topics of Interest
The goal of this workshop is to bring together research studies aiming at developing new machine learning tools to handle new challenges associated to Big Data mining. We are especially interested on the following topics:
Distributed on-line learning
Multi-task learning for big data
Transfer Learning for big data
Optimization techniques for large-scale learning
Handling large number of target classes in big data
Structured prediction models in big data
Speed/Accuracy tradeoffs in big data
Statistical inference for big data
Noise in Big data