The main goal of this program is to gather the community of researchers working on questions that relate in some way statistical physics and high dimensional statistical inference. The format will be several (~10) 3h introductory lectures, and about twice as many contributed invited talks. The topics include:
** Energy/loss landscapes in disordered systems, machine learning and inference problems
** Computational and statistical thresholds and trade-offs
** Theory of artificial multilayer neural networks
** Rigorous approaches to spin glasses and related models of statistical inference
** Parallels between optimisation algorithms and dynamics in physics
** Vindicating the replica and cavity method rigorously
** Current trends in variational Bayes inference
** Message passing algorithms
** Applications on machine learning in condensed matter physics
** Information processing in biological systems

Deadline Registration : March 31, 2018

  Lecturers

  • Gerard Ben Arous (Courant Institute)
  • Giulio Biroli (CEA Saclay, France)
  • Nicolas Brunel (Duke University)
  • Yann LeCun (Courant Institute and Facebook)
  • Michael Jordan (UC Berkeley)
  • Stephane Mallat (ENS et college de France)
  • Andrea Montanari (Stanford)
  • Dmitry Panchenko (University of Toronto, Canada)
  • Sundeep Rangan (New York University)
  • Riccardo Zecchina (Politecnico Turin, Italy)

  Organizing Committee

  • Florent Krzakala (ENS & UPMC, Paris)
  • Lenka Zdeborova (CEA & CNRS, Saclay)