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Workshop on Sparsity in Applied Mathematics and Statistics

In recent years the acquisition of big data sets on one hand and the increasing popularity of high-dimensional models on the other hand have intensified the interdisciplinary contacts between data sciences, machine learning, statistics, applied mathematics, computer science and signal processing. The learning or estimation of patterns and structures from massive observations in complex models is closely related to the solution of possibly large, ill posed or ill conditioned inverse problems. The understanding of graphical and structured models involves expertise in the algorithmic, numerical and statistical aspects. Regularisation of ill posed or ill conditioned problems is often based on an explicit or implicit assumption of sparsity, which can be imposed already at the recovery of the data, as in compressed sensing.
When 01 June 2017 07:30 PM to
02 June 2017 07:30 PM
Where Brussels BE
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Topics include:

  • Sparsity (theory, algorithms, applications, ...)
  • High-dimensional models
  • Inverse problems
  • Compressed sensing 
  • Statistical modelling of high-dimensional data
  • Networks and graphical models
  • (Medical) Imaging
  • Model and variable selection; structured or group selection
  • Statistical learning
  • Optimization (in sparse/inverse problems or high-dimensional data)
  • Algorithms for the above mentioned problems

Invited Speakers:

  • Laure Blanc-Feraud, Université de Nice-Sophia Antipolis, France
  • Ivan Markovsky, Vrije Universiteit Brussel, Belgium
  • Richard Samworth, Cambridge University, UK
  • Goeran Kauermann, Ludwig-Maximilians-Universität München, Germany
  • Francesco Stingo, Università degli Studi di Firenze, Italy

More information about this event…