Phase retrieval consists in the recovery of a complex-valued signal from intensity-only
measurements. As it pervades a broad variety of applications, many researchers have …

Approximate message passing (AMP) algorithms have become an important element of high-
dimensional statistical inference, mostly due to their adaptability and concentration …

We consider the phase retrieval problem of reconstructing a $ n $-dimensional real or
complex signal $\mathbf {X}^\star $ from $ m $(possibly noisy) observations $ Y_\mu=|\sum …

We consider the problem of compressed sensing and of (real-valued) phase retrieval with
random measurement matrix. We derive sharp asymptotics for the information-theoretically …

This paper proposes a theoretical analysis of a Lasso-based classification algorithm.
Leveraging on a realistic regime where the dimension of the data $ p $ and their number $ n …

Phase retrieval, a nonlinear problem prevalent in imaging applications, has been
extensively studied using random models, some of which with iid sensing matrix …

Signal recovery under generative neural network priors has emerged as a promising
direction in statistical inference and computational imaging. Theoretical analysis of …

The main difficulty that arises in the analysis of most machine learning algorithms is to
handle, analytically and numerically, a large number of interacting random variables. In this …