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TU Berlin

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Publications

Monographs
No.
Title
3
The Mismatch Principle and L1-Analysis Compressed Sensing: 
A Unified Approach to Estimation Under Large Model Uncertainties and Structural Constraints

PhD-ThesisTechnische Universität Berlin, 2019. [pdf]
Supervisor: Prof. Dr. Gitta Kutyniok
2
Sparse Proteomics Analysis
Master's Thesis, Technische Universität Berlin, 2015.
Advisor: Prof. Dr. Gitta Kutyniok

1
Analysis von Inpainting mittels Hybrid-Shearlets und Clustered Sparsity
Bachelor's Thesis
(in german), Technische Universität Berlin, 2013.
Advisor: Prof. Dr. Gitta Kutyniok
Journal Articles
No.
Title
9
M. Genzel and G. Kutyniok
The Mismatch Principle: Statistical Learning Under Large Model Uncertainties
submitted (2018)
8
M. Genzel and A. Stollenwerk
Robust 1-Bit Compressed Sensing via Hinge Loss Minimization
submitted (2018)
7
M. Genzel, G. Kutyniok, and M. März
L1-Analysis Minimization and Generalized (Co-)Sparsity: When Does Recovery Succeed?
submitted (2017)
6
M. Genzel and P. Jung
Recovering Structured Data From Superimposed Non-Linear Measurements
submitted (2017)
5
M. Genzel
High-Dimensional Estimation of Structured Signals from Non-Linear Observations with General Convex Loss Functions
IEEE Trans. Inf. Theory 63.3 (2017), 1601-1619. [arXiv]
4
T. Conrad, M. Genzel, N. Cvetkovic, N. Wulkow, A. Leichtle, J. Vybiral, G. Kutyniok, and Ch. Schütte
Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data
BMC Bioinform. 18 (2017), 160. [arXiv]
3
M. Meixner, T. Fuss, M. Klaus, M. Genzel, and Ch. Genzel
Diffraction Analysis of strongly inhomogeneous residual stress depth distributions by modification of the stress scanning method. II. Experimental implementation
J. Appl. Cryst. 48.5 (2015), 1451-1461.
2
M. Genzel and G. Kutyniok
Asymptotic Analysis of Inpainting via Universal Shearlet Systems
SIAM J. Imaging Sci. 7.4 (2014), 2301-2339. [arXiv]

1
D. Apel, M. Klaus, M. Genzel, and Ch. Genzel.
Rietveld-based energy-dispersive residual stress evaluation: analysis of complex stress fields σij(z)
J. Appl. Cryst. 47.2 (2014), 511-526.
Conference Articles
No.
Title
3
P. Jung and M. Genzel
IEEE Statistical Signal Processing Workshop (SSP), June 10-13, 2018.
2
M. Genzel and Peter Jung
Blind sparse recovery from superimposed non-linear sensor measurements
Sampling Theory and Applications (SampTA), July 3-7, 2017.
1
Ch. Genzel, D. Apel, M. Klaus, M. Genze, and D. Balzar.
Keynote Lecture: Residual Stress Gradient Analysis by Multiple Diffraction Line Methods
International Conference on Residual Stresses 9 (ICRS 9), September 2013.

Other Articles
No.
Title
1
M. Genzel and G. Kutyniok
A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations
arXiv:1608:08852, 2016.


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