| 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 | 50 | 2021 |
| Blind demixing and deconvolution at near-optimal rate P Jung, F Krahmer, D Stöger IEEE Transactions on Information Theory 64 (2), 704-727, 2017 | 47 | 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 | 29 | 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 | 16 | 2021 |
| 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 | 13* | 2021 |
| Blind deconvolution and compressed sensing D Stöger, P Jung, F Krahmer 2016 4th International Workshop on Compressed Sensing Theory and its …, 2016 | 12 | 2016 |
| Complex phase retrieval from subgaussian measurements F Krahmer, D Stöger Journal of Fourier Analysis and Applications 26 (6), 89, 2020 | 11 | 2020 |
| Rigidity for perimeter inequality under spherical symmetrisation F Cagnetti, M Perugini, D Stöger Calculus of Variations and Partial Differential Equations 59, 1-53, 2020 | 11 | 2020 |
| Sparse Power Factorization: Balancing peakiness and sample complexity J Geppert, F Krahmer, D Stöger Advances in Computational Mathematics 45 (3), 1711-1728, 2019 | 11 | 2019 |
| Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing M Soltanolkotabi, D Stöger, C Xie arXiv preprint arXiv:2303.14244, 2023 | 7 | 2023 |
| Refined performance guarantees for sparse power factorization JA Geppert, F Krahmer, D Stöger 2017 International Conference on Sampling Theory and Applications (SampTA …, 2017 | 7 | 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, 2022 | 6 | 2022 |
| 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 | 6* | 2017 |
| Randomly initialized alternating least squares: Fast convergence for matrix sensing K Lee, D Stöger SIAM Journal on Mathematics of Data Science 5 (3), 774-799, 2023 | 4 | 2023 |
| 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 | 4 | 2017 |
| Blind Deconvolution: Convex Geometry and Noise Robustness F Krahmer, D Stöger 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 643-646, 2018 | 2 | 2018 |
| Robust Recovery of Low-Rank Matrices and Low-Tubal-Rank Tensors from Noisy Sketches A Ma, D Stöger, Y Zhu SIAM Journal on Matrix Analysis and Applications 44 (4), 1566-1588, 2023 | 1 | 2023 |
| Sparse power factorization with refined peakiness conditions D Stöger, J Geppert, F Krahmer 2018 IEEE Statistical Signal Processing Workshop (SSP), 816-820, 2018 | 1 | 2018 |
| On the Lipschitz constant of random neural networks P Geuchen, T Heindl, D Stöger, F Voigtlaender arXiv preprint arXiv:2311.01356, 2023 | | 2023 |
| How to induce regularization in generalized linear models: A guide to reparametrizing gradient flow HH Chou, J Maly, D Stöger arXiv preprint arXiv:2308.04921, 2023 | | 2023 |
Felix KrahmerAssociate Professor for Optimization and Data Analysis, Technische Universität München在 ma.tum.de 的电子邮件经过验证
