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Random Matrices, Random Graphs and Statistical Physics for Machine Learning and Inference | (smr 3703)

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. 




  • High-dimensional statistics
  • Random matrix theory
  • Statistical physics
  • Information theory
  • Theoretical machine learning
  • Graph theory

Country Italy
LocationGiambiagi Lecture Hall (AGH) Strada Costiera, 11 I - 34151 Trieste
Starts on16/05/2022
Registration closes on01/04/2022

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