May 15 – 17, 2024
Max Planck Institute for Physics
Europe/Zurich timezone

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)

 

Call for Contributions

Please see the Call for Abstracts to submit a contribution to the workshop, or get in touch with the organizers directly. 

Acknowledgement

We gratefully acknowledge support from:

 

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Max Planck Institute for Physics
Boltzmannstr. 8 85748 Garching Germany
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