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 …

Several recent trends in machine learning theory and practice, from the design of state-of-
the-art Gaussian Process to the convergence analysis of deep neural nets (DNNs) under …

A denoising algorithm seeks to remove noise, errors, or perturbations from a signal.
Extensive research has been devoted to this arena over the last several decades, and as a …

We develop a broadband channel estimation algorithm for millimeter wave (mmWave)
multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters …

We consider the estimation of a random vector observed through a linear transform followed
by a componentwise probabilistic measurement channel. Although such linear mixing …

When recovering a sparse signal from noisy compressive linear measurements, the
distribution of the signal's non-zero coefficients can have a profound effect on recovery …

Abstract We consider the Principal Component Analysis problem for large tensors of
arbitrary order k under a single-spike (or rank-one plus noise) model. On the one hand, we …

Given a high-dimensional data matrix, approximate message passing (AMP) algorithms
construct sequences of vectors,, indexed by by iteratively applying or and suitable nonlinear …

Recovering a sparse signal from an undersampled set of random linear measurements is
the main problem of interest in compressed sensing. In this paper, we consider the case …

This paper researches the cooperative localization in wireless sensor networks (WSNs) with-
periodic angle-of-arrival (AOA) measurements. Two types of localizers are developed from …