Workshop on "Regularized Inverse Problem Solving

and High-Dimensional Learning Methods"

UCLouvain, August 30th, 2017

News: 
* 6/9/17: The slides (pdf) of all the presentations are now available via the workshop programme.
* 21/8/17: The workshop programme is now available. Registration is now closed)

The aim of this single-day workshop is to gather PhD students and researchers on the general topic of "Regularized Inverse Problem Solving and High-Dimensional Learning Methods". The workshop also welcomes contributed talks (see below for details) related to the following topics:

  • Linear, quadratic (e.g., phase retrieval), and more generally non-linear, inverse problems;
  • Learning approach in inverse problem solving;
  • High-dimensional inference problem, density estimation, compressive learning;
  • Computational optimal transport and applications;
  • Super-resolution and "off-the-grid" inverse problems;
  • Compressive sensing and computational imaging applications;
  • Matrix/manifold sensing/processing (graph, low-rank approximation, ...);
  • Sparse machine learning and inference;

Plenary Speakers

The workshop will be honored to welcome the following two plenary speakers:

The title and abstract of their talks are given here.

Venue

This event will be held in (see also the "Venue" page)

UCLouvain, Louvain-La-Neuve, Belgium,

on Wednesday, August 30th, 2017,

in the Shannon seminar room (Maxwell Building).

Registration

Participation to the workshop is free but registration is mandatory and must be done before Wednesday August 16th by filling this registration form.

Contributed talks

Additionally, the workshop gladly accepts contributed talks (until all available slots are filled). If you want to propose one preferably (but not necessarily) related to the topics listed above, we welcome your proposal until August 16th 2017.

For submitting it, just fill the registration form by providing additionally to your registration data a title and a short abstract (5 to 10 lines) of your talk.


Sponsors: