Random Matrices, Random Graphs and Statistical Physics for Machine Learning and Inference | (smr 3703)
Starts 16 May 2022
Ends 20 May 2022
Central European Time
Giambiagi Lecture Hall (AGH)
Strada Costiera, 11
I - 34151 Trieste (Italy)
An ICTP Hybrid Meeting. You can submit your application for participation in presence or online.
This school on random matrices and graphs, machine learning and statistical physics will emphasize the strong connections between these fields, strenghten the analytical toolbox of the students and provide a modern vision of the challenges in high-dimensional statistics.
Inference and learning systems have been studied for decades. But the richness of contemporary high-dimensional statistical models (deep neural networks, community detection and inference on graphs, tensor factorization etc) require novel ideas and analytical methods. In particular, blending methods from random matrix theory, statistical physics and information theory is particularly promising. The school aims at providing an overview of recent progresses in this interdisciplinary field by worldwide experts coming from different backgrounds, but who all share a common goal of better modelling complex systems processing large datasets, and understanding their fundamental and algorithmic limitations.
Random matrix theory
Theoretical machine learning
C. MALE, Bordeaux University, France
L. MASSOULIÉ, INRIA Paris, France
M. LELARGE, INRIA Paris & Ecole Polytechnique, France
M. POTTERS, Capital Fund Management, France
P. VIVO, King's College London, UK
Grants: A limited number of grants are available to support the attendance of selected participants. There is no registration fee.