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Displaying 18 authors out of 18
BRUNO LOUREIRO (ENS Paris, France)
Universality and feature learning in two-layer neural networks
FEDERICA GERACE (SISSA, Italy)
Gaussian Universality of Perceptrons with Random Labels
FRANCESCO CAMILLI (ICTP, Italy)
Fundamental limits of shallow neural networks with small training sets
JEAN BARBIER (ICTP, Italy)
Fundamental limits in structured PCA, and how to reach them
JUSTIN KO (ENS Lyon, France)
Learning Noisy Rank-One matrices: Bayes, Non-Bayes, and Large Deviations
MANFRED OPPER (Birmingham University, UK)
Replica method with approximate inference
MANUEL SAENZ (Universidad de La República Uruguay, Uruguay)
How much does it cost to forget noise structure in low-rank matrix estimation?
MARCO MONDELLI (IST, Austria)
From Spectral Estimators to Approximate Message Passing... And Back
MATTEO MARSILI (ICTP, Italy)
Data that “make sense”
PRAGYA SUR (Harvard University, USA)
Debiasing regularized linear estimators with "spectrum-aware adjustments"
RISHABH DUDEJA (Harvard University, USA)
Spectral Universality in High-Dimensional Statistics
SANTIAGO ACEVEDO (SISSA, Italy)
Intrinsic dimension estimation in spin systems
SEBASTIAN GOLDT (SISSA, Italy)
The Gaussian world is not enough - how data shapes neural network representations
SOLEDAD VILLAR (Johns Hopkins University, USA)
Approximately equivariant graph networks
TAKASHI TAKAHASHI (Tokyo University, Japan)
Exploring bagging with structured data: Insights from precise asymptotics
YOSHIYUKI KABASHIMA (Tokyo University, Japan)
Compressed sensing based on diffusion models
YUE LU (Harvard University, USA)
Equivalence Principles for Nonlinear Random Matrices: A Closer Look
ZHOU FAN (Yale University, USA)
On orthogonally-invariant spin glasses and linear models with invariant designs

Organizers

Manuel SAENZ (Universidad de La República Uruguay, Uruguay), Subhabrata SEN (Harvard University, USA), Pragya SUR (Harvard University, USA), Local Organiser: Jean Barbier (ICTP)

Co-sponsors