6 accepted papers
Heavy tailed and contaminated data are common in various applications of machine learning. A standard technique to handle regression tasks that involve such data, is to use robust losses, e.g., the popular Huber’s loss.
We consider the problem of hypothesis testing for detection of a signal in Gaussian noise. We assume that the vector of measurements is unobserved, and that our observations consist of phaseless inner products with a set of known measurement vectors. This is typical of the phase retrieval problem, w…
In this paper we study the hybrid precoding design problem for a frequency selective massive MIMO channel, e.g., the millimeter wave (mmWave) massive MIMO channel. In contrast to a traditional MIMO system, a hybrid analog-digital MIMO scheme is preferred for massive MIMO systems due to the high cost…
In this paper we address target detection in correlated non-Gaussian noise. We introduce a powerful class of multivariate complex valued distribution that allows us to specify flexible non-Gaussian marginals, as well as correlation between the variables, while preserving circular symmetry. For noise…
We consider the joint estimation of structured covariance matrices. We assume the structure is unknown and perform the estimation using heterogeneous training sets. More precisely, we are given groups of measurements coming from centered normal populations with different covariance matrices. Assumin…
This paper analyzes the performance of Tyler's M-estimator of the scatter matrix in elliptical populations. We focus on non-asymptotic performance analysis of Tyler's estimator. Given n samples of dimension p <; n, we show that the squared Frobenius norm of the error of the inverse estimator is prop…