Institutionen der Katholischen Universität Eichstätt-Ingolstadt
Mathematisch-Geographische Fakultät > Mathematik > Juniorprofessur für Data Science

2024

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  Kümmerle, Christian ; Stöger, Dominik:
  Linear Convergence of Iteratively Reweighted Least Squares for Nuclear Norm Minimization.
  In: 2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM). - Corvallis, OR, USA : IEEE, 2024
  ISBN 979-8-3503-4481-3 ; 979-8-3503-4482-0
  10.1109/SAM60225.2024.10636588
  (Begutachteter Beitrag / peer-reviewed paper)

2023

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  Soltanolkotabi, Mahdi ; Stöger, Dominik ; Xie, Changzhi:
  Implicit Balancing and Regularization : Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing.
  In: Proceedings of Machine Learning Research. 195 (2023). - S. 5140-5142.
  ISSN 2640-3498
  (Peer-Review-Journal)
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  Lee, Kiryung ; Stöger, Dominik:
  Randomly Initialized Alternating Least Squares : Fast Convergence for Matrix Sensing.
  In: SIAM Journal on Mathematics of Data Science. 5 (2023) 3. - S. 774-799.
  ISSN 2577-0187
  10.1137/22M1506456
  (Peer-Review-Journal)
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  Ma, Anna ; Stöger, Dominik ; Zhu, Yizhe:
  Robust Recovery of Low-Rank Matrices and Low-Tubal-Rank Tensors from Noisy Sketches.
  In: SIAM journal on matrix analysis and applications / Society for Industrial and Applied Mathematics, SIAM. 44 (2023) 4. - S. 1566-1588.
  ISSN 1095-7162
  10.1137/22M150071X
  (Peer-Review-Journal)

2022

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  Fuchs, Tim ; Gross, David ; Jung, Peter ; Krahmer, Felix ; Kueng, Richard ; Stöger, Dominik:
  Proof Methods for Robust Low-Rank Matrix Recovery.
  In: Kutyniok, Gitta ; Rauhut, Holger ; Kunsch, Robert J. (Hrsg.): Compressed Sensing in Information Processing. - Cham : Birkhäuser, 2022. - S. 37-75. - (Applied and Numerical Harmonic Analysis)
  ISBN 978-3-031-09744-7
  ISSN 2296-5009
  10.1007/978-3-031-09745-4_2
  (Begutachteter Beitrag / peer-reviewed paper)

2021

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  Kümmerle, Christian ; Mayrink Verdun, Claudio ; Stöger, Dominik:
  Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate.
  In: Advances in Neural Information Processing Systems 34 (NeurIPS 2021). - 2021
  (Begutachteter Beitrag / peer-reviewed paper)
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  Stöger, Dominik ; Soltanolkotabi, Mahdi:
  Small random initialization is akin to spectral learning : Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction.
  In: Advances in Neural Information Processing Systems 34 (NeurIPS 2021). - 2021
  (Begutachteter Beitrag / peer-reviewed paper)
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  Balaji, Yogesh ; Sajedi, Mohammadmahdi ; Kalibhat, Neha Mukund ; Ding, Mucong ; Stöger, Dominik ; Soltanolkotabi, Mahdi ; Feizi, Soheil:
  Understanding Overparameterization in Generative Adversarial Networks.
  In: International Conference on Learning Representations 2021. - Vienna, Austria, 2021
  (Begutachteter Beitrag / peer-reviewed paper)

2020

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  Krahmer, Felix ; Stöger, Dominik:
  Complex phase retrieval from subgaussian measurements.
  In: The journal of Fourier analysis and applications. 26 (20. November 2020) 6: 89. - 27 S.
  ISSN 1069-5869 ; 1531-5851
  10.1007/s00041-020-09797-9
  (Peer-Review-Journal)
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  Cagnetti, Filippo ; Perugini, Matteo ; Stöger, Dominik:
  Rigidity for perimeter inequality under spherical symmetrisation.
  In: Calculus of variations and partial differential equations. 59 (4. August 2020): 139.
  ISSN 0944-2669 ; 1432-0835
  10.1007/s00526-020-01786-6
  (Peer-Review-Journal)
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  Krahmer, Felix ; Stöger, Dominik:
  On the convex geometry of blind deconvolution and matrix completion.
  In: Communications on pure and applied mathematics. 74 (2020) 4. - S. 790-832.
  ISSN 0010-3640 ; 1097-0312
  10.1002/cpa.21957
  (Peer-Review-Journal)

2019

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  Geppert, Jakob ; Krahmer, Felix ; Stöger, Dominik:
  Sparse power factorization : balancing peakiness and sample complexity.
  In: Advances in computational mathematics. 45 (1. Juni 2019). - S. 1711-1728.
  ISSN 1019-7168 ; 1572-9044
  10.1007/s10444-019-09698-6
  (Peer-Review-Journal)

2018

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  Jung, Peter ; Krahmer, Felix ; Stöger, Dominik:
  Blind demixing and deconvolution at near-optimal rate.
  In: IEEE transactions on information theory. 64 (2018) 2. - S. 704-727.
  ISSN 0018-9448 ; 1557-9654
  10.1109/TIT.2017.2784481
  (Peer-Review-Journal)
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  Stöger, Dominik ; Geppert, Jakob ; Krahmer, Felix:
  Sparse power factorization with refined peakiness conditions.
  In: 2018 IEEE Statistical Signal Processing Workshop (SSP). - Freiburg im Breisgau, 2018
  ISBN 978-1-5386-1571-3
  10.1109/SSP.2018.8450850
  (Begutachteter Beitrag / peer-reviewed paper)

2017

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  Geppert, Jakob ; Krahmer, Felix ; Stöger, Dominik:
  Refined performance guarantees for Sparse Power Factorization.
  In: 2017 International Conference on Sampling Theory and Applications (SampTA). - Tallinn, Estonia, 2017. - S. 509-513
  ISBN 978-1-5386-1565-2
  10.1109/SAMPTA.2017.8024391
  (Begutachteter Beitrag / peer-reviewed paper)

2016

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  Stöger, Dominik ; Jung, Peter ; Krahmer, Felix:
  Blind deconvolution and compressed sensing.
  In: 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa). - Aachen, 2016. - S. 24-27
  ISBN 978-1-5090-2920-4
  (Begutachteter Beitrag / peer-reviewed paper)