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cs.IT2026
Information-Theoretic Limits for Rectangular Spiked Matrix Models with Rotationally Invariant Noise
Qun Chen, Rudy Skerk, Jean Barbier +3
We study the information-theoretic limits of inference in rectangular rank-one spiked matrix models with additive rotationally invariant noise, thereby going beyond the standard as…
cs.IT2024
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…
cs.IT2024★ 4 cited
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…