4 citations · 5 across the 6 of their papers we have counts for
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On the phase diagram of extensive-rank symmetric matrix denoising beyond rotational invariance
Jean Barbier, Francesco Camilli, Justin Ko +1
Matrix denoising is central to signal processing and machine learning. Its statistical analysis when the matrix to infer has a factorised structure with a rank growing proportional…
Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noise
Jean Barbier, Francesco Camilli, Marco Mondelli +1
We consider a prototypical problem of Bayesian inference for a structured spiked model: a low-rank signal is corrupted by additive noise. While both information-theoretic and algor…
A multiscale cavity method for sublinear-rank symmetric matrix factorization
Jean Barbier, Justin Ko, Anas A. Rahman
We consider a statistical model for symmetric matrix factorization with additive Gaussian noise in the high-dimensional regime, where the rank of the signal matrix to infer sca…