The workshop seeks to bring together leading researchers in computer science, statistics, machine learning, signal processing and relatedApril 29 - Deadline for submission
fields with a common interest in modeling and exploring structure in high-dimensional data. The workshop will be a mix of invited talks, selected research presentations, posters and a panel discussion, with plenty of time to discuss open questions, new ideas, new collaborations, etc. The workshop is part of the 28th International Conference on Machine Learning (http://www.icml-2011.org).
The aim of the workshop is to bring together theory and practice in modeling and exploring structure in high-dimensional data. Participation of researchers working on methodology, theory and applications, both from the frequentist and Bayesian point of view is strongly encouraged in order to discuss different approaches for tackling challenging high-dimensional problems. Furthermore, the workshop will link with the signal processing community, which has worked on similar topics and with whom exchanges of ideas will be very fruitful. We encourage genuine interaction between proponents of different approaches and hope to better understand possibilities for modeling of structure in high dimensional data.
We invite submissions on various aspects of structured sparse modeling in high-dimensions. Here is an example of two key questions:
ICML style). Abstracts should be sent by email to firstname.lastname@example.org, not later than Friday April 29, 2011. Organizers will review and select submissions by May 20, 2011. Accepted submissions will be presented as a talk or posters. Please indicate your preference for oral or poster presentation in the submission.
May 20 - Notification of acceptance
July 2 - Workshop day
http://www.icml-2011.org/) for information on how to register for the workshop.
If you have any questions or comments please send an e-mail to email@example.com
Mladen Kolar, Carnegie Mellon University
Han Liu, Johns Hopkins University
Guillaume Obozinski, INRIA
Eric P. Xing, Carnegie Mellon University