Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

The standard linear regression (SLR) problem is to recover a vector x 0 from noisy linear
observations y= Ax 0+ w. The approximate message passing (AMP) algorithm proposed by …

Approximate message passing (AMP) is a low-cost iterative signal recovery algorithm for
linear system models. When the system transform matrix has independent identically …

Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse
problems by using advanced denoisers within an iterative algorithm. Recent experimental …

Approximate message passing (AMP) methods and their variants have attracted
considerable recent attention for the problem of estimating a random vector x observed …

In this paper, we present an algorithm for the sparse signal recovery problem that
incorporates damped Gaussian generalized approximate message passing (GGAMP) into …

Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse
solutions to ill-posed inverse problems. In this letter, we present a data-driven scheme for …

Local convexity of the TAP free energy and AMP convergence for Z2-synchronization Page 1
The Annals of Statistics 2023, Vol. 51, No. 2, 519–546 https://doi.org/10.1214/23-AOS2257 © …

In this paper, we investigate a sparse channel estimation problem for broadband massive
multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) …