Dominik Stöger
Dominik Stöger
KU Eichstätt-Ingolstadt
Verified email at - Homepage
Cited by
Cited by
Blind demixing and deconvolution at near-optimal rate
P Jung, F Krahmer, D Stöger
IEEE Transactions on Information Theory 64 (2), 704-727, 2017
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
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction
D Stöger, M Soltanolkotabi
Advances in Neural Information Processing Systems 34, 2021
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
Complex phase retrieval from subgaussian measurements
F Krahmer, D Stöger
Journal of Fourier Analysis and Applications 26 (6), 89, 2020
Sparse Power Factorization: Balancing peakiness and sample complexity
J Geppert, F Krahmer, D Stöger
Advances in Computational Mathematics 45 (3), 1711-1728, 2019
Rigidity for perimeter inequality under spherical symmetrisation
F Cagnetti, M Perugini, D Stöger
Calculus of Variations and Partial Differential Equations 59, 1-53, 2020
Refined performance guarantees for sparse power factorization
JA Geppert, F Krahmer, D Stöger
2017 International Conference on Sampling Theory and Applications (SampTA …, 2017
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate
C Kümmerle, C Mayrink Verdun, D Stöger
Advances in Neural Information Processing Systems 34, 2873-2886, 2021
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
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
Proof methods for robust low-rank matrix recovery
T Fuchs, D Gross, P Jung, F Krahmer, R Kueng, D Stöger
Compressed Sensing in Information Processing, 37-75, 2012
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
Robust recovery of low-rank matrices and low-tubal-rank tensors from noisy sketches
A Ma, D Stöger, Y Zhu
arXiv preprint arXiv:2206.00803, 2022
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing
K Lee, D Stöger
arXiv preprint arXiv:2204.11516, 2022
Bilinear Compressed Sensing
D Stöger
Technische Universität München, 2019
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