The project Advanced nonsmooth optimization methods for stochastic programming received a grant from the Gaspard Monge Program for Optimization and operational research (PGMO) of Fondation Hadamard.
It is a collaboration between J. Malick (PI), F. Iutzeler, Welington de Oliveira (Rio de Janeiro, Brasil), and Wim van Ackooij (EDF Saclay).
The project ON FIRE – Calibration of future large interferometers was awarded a “Jeunes Chercheurs” (young researchers) grant from the GdR ISIS.
It is a collaboration between F. Iutzeler, Mohammed Nabil El Korso (LEME, Paris X), Arnaud Breloy (LEME, Paris X), and Rémi Flamary (Laboratoire Lagrange, Nice).
June 23 saw the awarding ceremony of the Challenge Data organized by our Parisian colleagues.
Le campus de Grenoble accueille les Journées MAS 2016 .
J. Lelong organizes a mini-symposium on stochastic algorithms at the International Conference on Monte Carlo techniques 2016 that takes place in Paris (France) July 5-8.
J. Malick organizes an Optimization-themed Mini-Symposium at Picof 2016 conference on mathematical and numerical issues in the fields of inverse problems, control and shape optimization that takes place in Autrans (France) June 1-3.
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.
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.
|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
J. Lelong organizes a minisymposium on stochastic algorithms and bandits at CANUM 2016 that takes place in Obernai (France) May 9-13.