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10/12/2022
Rigorous dynamical mean field theory for stochastic gradient descent methods
We prove closed-form equations for the exact high-dimensional asymptotic...
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05/26/2022
Multi-layer State Evolution Under Random Convolutional Design
Signal recovery under generative neural network priors has emerged as a ...
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03/22/2022
Learning curves for the multi-class teacher-student perceptron
One of the most classical results in high-dimensional learning theory pr...
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01/31/2022
Fluctuations, Bias, Variance Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension
From the sampling of data to the initialisation of parameters, randomnes...
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09/24/2021
Graph-based Approximate Message Passing Iterations
Approximate-message passing (AMP) algorithms have become an important el...
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06/07/2021
Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions
Generalised linear models for multi-class classification problems are on...
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02/16/2021
Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model
Teacher-student models provide a powerful framework in which the typical...
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06/11/2020
Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (or : How to Prove Kabashima's Replica Formula)
There has been a recent surge of interest in the study of asymptotic rec...
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02/11/2020