12 citations · 39 across the 9 of their papers we have counts for
17 papers · 1 filter
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 $…
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…
Sparse superposition codes under VAMP decoding with generic rotational invariant coding matrices
TianQi Hou, YuHao Liu, Teng Fu +1
Sparse superposition codes were originally proposed as a capacity-achieving communication scheme over the gaussian channel, whose coding matrices were made of i.i.d. gaussian entri…
Information theoretic limits of learning a sparse rule
Clément Luneau, Jean Barbier, Nicolas Macris
We consider generalized linear models in regimes where the number of nonzero components of the signal and accessible data points are sublinear with respect to the size of the signa…
All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation
Jean Barbier, Nicolas Macris, Cynthia Rush
We determine statistical and computational limits for estimation of a rank-one matrix (the spike) corrupted by an additive gaussian noise matrix, in a sparse limit, where the under…
Information-theoretic limits of a multiview low-rank symmetric spiked matrix model
Jean Barbier, Galen Reeves
We consider a generalization of an important class of high-dimensional inference problems, namely spiked symmetric matrix models, often used as probabilistic models for principal c…