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ORCIDDominik Stöger
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2020 – today
- 2025
[j5]Mahdi Soltanolkotabi
, Dominik Stöger, Changzhi Xie:
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing. IEEE Trans. Inf. Theory 71(4): 2991-3037 (2025)- 2024
- [c8]Christian Kümmerle, Dominik Stöger:
Linear Convergence of Iteratively Reweighted Least Squares for Nuclear Norm Minimization. SAM 2024: 1-5 - [i15]Dominik Stöger, Yizhe Zhu:
Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity. CoRR abs/2408.13276 (2024) - 2023
- [j4]Anna Ma, Dominik Stöger, Yizhe Zhu:
Robust Recovery of Low-Rank Matrices and Low-Tubal-Rank Tensors from Noisy Sketches. SIAM J. Matrix Anal. Appl. 44(4): 1566-1588 (2023) - [j3]Kiryung Lee, Dominik Stöger:
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing. SIAM J. Math. Data Sci. 5(3): 774-799 (2023) - [c7]Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie:
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing. COLT 2023: 5140-5142 - [i14]Julia Kostin, Felix Krahmer, Dominik Stöger:
How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise? CoRR abs/2303.10030 (2023) - [i13]Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie:
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing. CoRR abs/2303.14244 (2023) - [i12]Hung-Hsu Chou, Johannes Maly, Dominik Stöger:
How to induce regularization in generalized linear models: A guide to reparametrizing gradient flow. CoRR abs/2308.04921 (2023) - [i11]Paul Geuchen, Thomas Heindl, Dominik Stöger, Felix Voigtländer:
Upper and lower bounds for the Lipschitz constant of random neural networks. CoRR abs/2311.01356 (2023) - 2022
- [i10]Kiryung Lee, Dominik Stöger:
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing. CoRR abs/2204.11516 (2022) - [i9]Anna Ma, Dominik Stöger, Yizhe Zhu:
Robust recovery of low-rank matrices and low-tubal-rank tensors from noisy sketches. CoRR abs/2206.00803 (2022) - 2021
- [c6]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi:
Understanding Over-parameterization in Generative Adversarial Networks. ICLR 2021 - [c5]Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger:
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate. NeurIPS 2021: 2873-2886 - [c4]Dominik Stöger, Mahdi Soltanolkotabi:
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction. NeurIPS 2021: 23831-23843 - [i8]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi:
Understanding Overparameterization in Generative Adversarial Networks. CoRR abs/2104.05605 (2021) - [i7]Tim Fuchs, David Gross, Peter Jung, Felix Krahmer, Richard Kueng, Dominik Stöger:
Proof methods for robust low-rank matrix recovery. CoRR abs/2106.04382 (2021) - [i6]Dominik Stöger, Mahdi Soltanolkotabi:
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction. CoRR abs/2106.15013 (2021) - 2020
- [i5]Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger:
Iteratively Reweighted Least Squares for 𝓁1-minimization with Global Linear Convergence Rate. CoRR abs/2012.12250 (2020)
2010 – 2019
- 2019
- [j2]Jakob Geppert, Felix Krahmer, Dominik Stöger:
Sparse power factorization: balancing peakiness and sample complexity. Adv. Comput. Math. 45(3): 1711-1728 (2019) - [i4]Felix Krahmer, Dominik Stöger:
On the convex geometry of blind deconvolution and matrix completion. CoRR abs/1902.11156 (2019) - [i3]Felix Krahmer, Dominik Stöger:
Complex phase retrieval from subgaussian measurements. CoRR abs/1906.08385 (2019) - 2018
- [j1]Peter Jung, Felix Krahmer, Dominik Stöger:
Blind Demixing and Deconvolution at Near-Optimal Rate. IEEE Trans. Inf. Theory 64(2): 704-727 (2018) - [c3]Felix Krahmer, Dominik Stöger:
Blind Deconvolution: Convex Geometry and Noise Robustness. ACSSC 2018: 643-646 - [c2]Dominik Stöger, Jakob Geppert, Felix Krahmer:
Sparse Power Factorization With Refined Peakiness Conditions. SSP 2018: 816-820 - [i2]Jakob Geppert, Felix Krahmer, Dominik Stöger:
Sparse Power Factorization: Balancing peakiness and sample complexity. CoRR abs/1804.09097 (2018) - 2017
- [c1]Dominik Stöger, Peter Jung, Felix Krahmer:
Blind Demixing and Deconvolution with Noisy Data: Near-optimal Rate. WSA 2017: 1-5 - [i1]Peter Jung, Felix Krahmer, Dominik Stöger:
Blind Demixing and Deconvolution at Near-Optimal Rate. CoRR abs/1704.04178 (2017)
Coauthor Index
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