Workshop on kernel and sampling methods for design and quantization
When & where
The workshop will (hopefully) take place IRL, on November 15, 2021 at Institut Henri Poincaré, Paris.
Room: amphithéâtre Hermite (ground floor).
Presentation of the workshop
The recent period has seen a considerable increase in the use of kernel methods in various fields of applied mathematics and statistics, with a flourishing expansion of applications in many areas of engineering. This includes in particular, but not limited to, function approximation and integration, and covers many topics of interest to the MASCOT-NUM network. In recent years, minimisation of a kernel discrepancy (the maximum mean discrepancy) has been popularised as a general tool for quantifying a probability distribution and constructing space filling designs, and determinantal point processes have emerged as a very promising tool for generating random sets of points with appropriate repulsion properties.
The workshop aims to introduce these modern topics to the MASCOT-NUM community through a series of presentations by key researchers in the field.
Organizers: Luc Pronzato, Julien Bect.
Registration is free but appreciated.
- 9:30–9:40: Luc Pronzato (introduction)
- 9:40–11:05: Pierre Olivier Amblard
- coffee break (15')
- 11:20–12:15: Jean-François Coeurjolly
- lunch break
- 14:15–15:10: Rémi Bardenet
- 15:10–16:05: Onur Teymur
- coffee break (15')
- 16:20–17:15: Marina Riabiz
Luc Pronzato (CNRS & Univ. Côte d'Azur, France)
Pierre Olivier Amblard (CNRS & Univ. Grenoble Alpes, France)
♣ An introduction to determinantal point processes
♣ GP regression in the flat limit
Jean-François Coeurjolly (Univ. Grenoble Alpes, France)
♣ Repulsiveness for integration (not my social program)
Rémi Bardenet (CNRS & Univ. Lille, France)
♣ Interpolation and experimental design with volume sampling
Onur Teymur (Newcastle Univ. & the Alan Turing Institute, UK)
♣ Optimal quantisation of probability measures using maximum mean discrepancy
Marina Riabiz (King's College London & the Alan Turing Institute, UK)
♣ Kernel Stein discrepancy minimization for MCMC thinning, with application to cardiac electrophysiology