The supplementary appendix contains additional details about AMP algorithms for
rectangular matrices and the rectangular generalized invariant universality class of …

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 …

We consider the problem of estimating an unknown parameter vector θ∈ R n, given noisy
observations Y= θ θ T/n+ Z of the rank-one matrix θ θ T, where Z has independent Gaussian …

We prove closed-form equations for the exact high-dimensional asymptotics of a family of
first-order gradient-based methods, learning an estimator (eg, M-estimator, shallow neural …

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 a spiked Wigner problem with an inhomogeneous noise profile. Our aim in this
problem is to recover the signal passed through an inhomogeneous low-rank matrix …

Factorization of matrices where the rank of the two factors diverges linearly with their sizes
has many applications in diverse areas such as unsupervised representation learning …

Uncertainty quantification is a central challenge in reliable and trustworthy machine
learning. Naive measures such as last-layer scores are well-known to yield overconfident …

This article introduces a novel concatenated coding scheme called sparse regression LDPC
(SR-LDPC) codes. An SR-LDPC code consists of an outer non-binary LDPC code and an …

This paper investigates the asymptotic distribution of the maximum-likelihood estimate
(MLE) in multinomial logistic models in the high-dimensional regime where dimension and …