Fueled by the recent advances of Machine Learning in the last decade, a new breed of techniques have been developed to tackle statistical inference problems for "likelihood-free" cases, where it is possible to sample from the data-generating process (i.e. via stochastic simulators) but a closed form evaluation of the density is intractable.
This group of methods is known as "simulation-based inference" (SBI) or "likelihood-free inference" (LFI) and will be the dedicated topic of this PHYSTAT Workshop taking place from May 15th - May 17th 2024 at the Max-Planck Institute for Physics (MPP) in Garching near Munich.
PHYSTAT https://phystat.github.io/Website/) is a long-running workshop series that brings together statisticians, machine learning researchers and physicists to discuss shared topics and foster collaboration among the research communities.
Confirmed Invited Speakers:
- Kyle Cranmer (U Wisconsin-Madison)
- Antoine Wehenkel (Apple)
- Gilles Louppe (U Liège)
- Laurence Perreault-Levasseur (U Montréal)
- Ann Lee (Carnegie Mellon)
- Julia Linhart (INRIA)
- Noemi Montel (U of Amsterdam)
- Jakub Tomczak (Eindhoven)
- Christoph Weniger (GRAPPA)
- Alexander Held (U Wisconsin-Madison)
- Paul Bürkner (TU Dortmund)
- Francesca Capel (MPP Munich)
- Mikael Kuusela (Carnegie Mellon)
- Aishik Ghosh (UC Irvine)
(Credit: Axel Griesch/MPP)
Acknowledgement
We gratefully acknowledge support from:
