ATLAS workshop — 23-24 May 2016 @ LJK, Grenoble

The ATLAS conference is an interdisciplinary workshop on mathematical and algorithmimcal approaches for high dimensional problems in data sciences. This year’s event is particularly dedicated to signal processing and applications in different fields as medical imaging, neurosciences, astrophysics…. with a particular emphasis on the use of innovative optimization methods. The workshop’s program will feature plenary talks given by experts in the field, as well as short talk.


Special Session “Statistical and Mathematical Tools for Data Mining” at DSAA 2016

M. Clausel is co-organizing a special session at the IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016) held at in Montreal, Canada;  October 17-19, 2016.

Special Session on Statistical and Mathematical Tools for Data Mining


Massih-Reza Amini Marianne Clausel Eric Gaussier
University of Grenoble, France University of Grenoble, France University of Grenoble, France

Aims and Scope
Huge amounts of data are now easily and legally available on the Web. This data is generally heterogeneous and merely structured. Data mining and 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 from different communities as machine learning, data mining and statsitics. The goal of this special session is to bring together research studies aiming at developing new data mining and machine learning tools to handle new challenges associated to data science.

Topics of Interest:

  • 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