no code implementations • 20 May 2022 • Jean Barbier, Tianqi Hou, Marco Mondelli, Manuel Sáenz
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 perform when the noise statistics is unknown and hence Gaussian noise is assumed?
no code implementations • 14 Jul 2021 • Jean Barbier, Wei-Kuo Chen, Dmitry Panchenko, Manuel Sáenz
Here we consider a model in which the responses are corrupted by gaussian noise and are known to be generated as linear combinations of the covariates, but the distributions of the ground-truth regression coefficients and of the noise are unknown.
no code implementations • 27 Sep 2020 • Jean Barbier, Dmitry Panchenko, Manuel Sáenz
We consider a generic class of log-concave, possibly random, (Gibbs) measures.