In this paper we address the problem of selecting variables or features in a regression model in the presence of both additive (vertical) and leverage outliers. Since variable sel...
The recent upsurge of research toward compressive sampling and parsimonious signal representations hinges on signals being sparse, either naturally, or, after projecting them on a...
Georgios B. Giannakis, Gonzalo Mateos, Shahrokh Fa...
This paper develops theoretical results regarding noisy 1-bit compressed sensing and sparse binomial regression. We demonstrate that a single convex program gives an accurate estim...
It is well known that in situations involving the study of large datasets where influential observations or outliers maybe present, regression models based on the Maximum Likeliho...
Traditional non-parametric statistical learning techniques are often computationally attractive, but lack the same generalization and model selection abilities as state-of-the-art...