212 citations · 1.1k across the 42 of their papers we have counts for
51 papers
Fundamental limits of overparametrized shallow neural networks for supervised learning
Francesco Camilli, Daria Tieplova, Jean Barbier
We carry out an information-theoretical analysis of a two-layer neural network trained from input-output pairs generated by a teacher network with matching architecture, in overpar…
Matrix Inference in Growing Rank Regimes
Farzad Pourkamali, Jean Barbier, Nicolas Macris
The inference of a large symmetric signal-matrix corrupted by additive Gaussian noise, is considered for two regimes of growth of the rank $…
Capacity-Achieving Sparse Regression Codes via Vector Approximate Message Passing
Yizhou Xu, YuHao Liu, ShanSuo Liang +4
Sparse regression codes (SPARCs) are a promising coding scheme that can approach the Shannon limit over Additive White Gaussian Noise (AWGN) channels. Previous works have proven th…
Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise
Teng Fu, YuHao Liu, Jean Barbier +3
We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular…
Bayes-optimal limits in structured PCA, and how to reach them
Jean Barbier, Francesco Camilli, Marco Mondelli +1
How do statistical dependencies in measurement noise influence high-dimensional inference? To answer this, we study the paradigmatic spiked matrix model of principal components ana…
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?
Jean Barbier, TianQi Hou, Marco Mondelli +1
We consider the problem of estimating a rank-1 signal corrupted by structured rotationally invariant noise, and address the following question: how well do inference algorithms per…