150 citations · 423 across the 23 of their papers we have counts for
5 papers · 1 filter
Generic Error Bounds for the Generalized Lasso with Sub-Exponential Data
Martin Genzel, Christian Kipp
This work performs a non-asymptotic analysis of the generalized Lasso under the assumption of sub-exponential data. Our main results continue recent research on the benchmark case…
The Mismatch Principle: The Generalized Lasso Under Large Model Uncertainties
Martin Genzel, Gitta Kutyniok
We study the estimation capacity of the generalized Lasso, i.e., least squares minimization combined with a (convex) structural constraint. While Lasso-type estimators were origina…
Robust 1-Bit Compressed Sensing via Hinge Loss Minimization
Martin Genzel, Alexander Stollenwerk
This work theoretically studies the problem of estimating a structured high-dimensional signal from noisy -bit Gaussian measurements. Our recovery approac…
High-Dimensional Estimation of Structured Signals from Non-Linear Observations with General Convex Loss Functions
Martin Genzel
In this paper, we study the issue of estimating a structured signal from non-linear and noisy Gaussian observations. Supposing that is contained in a c…
Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data
Tim Conrad, Martin Genzel, Nada Cvetkovic +5
Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often inte…