How do statistical dependencies in measurement noise influence high-dimensional
inference? To answer this, we study the paradigmatic spiked matrix model of principal …

Over the last decade or so, Approximate Message Passing (AMP) algorithms have become
extremely popular in various structured high-dimensional statistical problems. Although the …

Generalised linear models for multi-class classification problems are one of the fundamental
building blocks of modern machine learning tasks. In this manuscript, we characterise the …

Universality of approximate message passing with semirandom matrices Page 1 The
Annals of Probability 2023, Vol. 51, No. 5, 1616–1683 https://doi.org/10.1214/23-AOP1628 …

We consider the problem of signal estimation in generalized linear models defined via
rotationally invariant design matrices. Since these matrices can have an arbitrary spectral …

Universality of regularized regression estimators in high dimensions Page 1 The Annals of
Statistics 2023, Vol. 51, No. 4, 1799–1823 https://doi.org/10.1214/23-AOS2309 © Institute of …

It has been observed that the performances of many high-dimensional estimation problems
are universal with respect to underlying sensing (or design) matrices. Specifically, matrices …

We study the problem of estimating a rank-1 signal in the presence of rotationally invariant
noise--a class of perturbations more general than Gaussian noise. Principal Component …

We consider the problem of estimating a rank-$1 $ signal corrupted by structured rotationally
invariant noise, and address the following question:\emph {how well do inference algorithms …

Orthogonal/vector approximate message-passing (AMP) is a powerful message-passing
(MP) algorithm for signal reconstruction in compressed sensing. This paper proves the …