Dominik Stöger
Cited by
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Blind demixing and deconvolution at near-optimal rate
P Jung, F Krahmer, D Stöger
IEEE Transactions on Information Theory 64 (2), 704-727, 2017
On the convex geometry of blind deconvolution and matrix completion
F Krahmer, D Stöger
Communications on Pure and Applied Mathematics 74 (4), 790-832, 2021
Blind deconvolution and compressed sensing
D Stöger, P Jung, F Krahmer
2016 4th International Workshop on Compressed Sensing Theory and its …, 2016
Sparse Power Factorization: Balancing peakiness and sample complexity
J Geppert, F Krahmer, D Stöger
Advances in Computational Mathematics 45 (3), 1711-1728, 2019
Understanding overparameterization in generative adversarial networks
Y Balaji, M Sajedi, NM Kalibhat, M Ding, D Stöger, M Soltanolkotabi, ...
International Conference on Learning Representations 1, 2021
Refined performance guarantees for sparse power factorization
JA Geppert, F Krahmer, D Stöger
2017 International Conference on Sampling Theory and Applications (SampTA …, 2017
Blind demixing and deconvolution with noisy data: Near-optimal rate
D Stöger, P Jung, F Krahmer
WSA 2017; 21th International ITG Workshop on Smart Antennas, 1-5, 2017
Blind Demixing and Deconvolution with Noisy Data: Near-optimal Rate
P Jung, F Krahmer, D Stoeger
WSA 2017; 21th International ITG Workshop on Smart Antennas; Proceedings of, 1-5, 2017
Complex phase retrieval from subgaussian measurements
F Krahmer, D Stöger
Journal of Fourier Analysis and Applications 26 (6), 1-27, 2020
Rigidity for perimeter inequality under spherical symmetrisation
F Cagnetti, M Perugini, D Stöger
Calculus of Variations and Partial Differential Equations 59 (4), 1-53, 2020
Blind Deconvolution: Convex Geometry and Noise Robustness
F Krahmer, D Stöger
2018 52nd Asilomar Conference on Signals, Systems, and Computers, 643-646, 2018
Sparse power factorization with refined peakiness conditions
D Stöger, J Geppert, F Krahmer
2018 IEEE Statistical Signal Processing Workshop (SSP), 816-820, 2018
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction
D Stöger, M Soltanolkotabi
arXiv preprint arXiv:2106.15013, 2021
Proof methods for robust low-rank matrix recovery
T Fuchs, D Gross, P Jung, F Krahmer, R Kueng, D Stöger
arXiv preprint arXiv:2106.04382, 2021
Iteratively Reweighted Least Squares for -minimization with Global Linear Convergence Rate
C Kümmerle, CM Verdun, D Stöger
arXiv preprint arXiv:2012.12250, 2020
Bilinear Compressed Sensing
D Stöger
Technische Universität München, 2019
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