Dominik Stöger

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 25

Top Topics

Sample Complexity Bounds

Blind Deconvolution

Top Venues

IEEE Trans. Inf. Theory

Publications

Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing. CoRR (2023)
Hung-Hsu Chou, Johannes Maly, Dominik Stöger
How to induce regularization in generalized linear models: A guide to reparametrizing gradient flow. CoRR (2023)
Kiryung Lee, Dominik Stöger
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing. SIAM J. Math. Data Sci. 5 (3) (2023)
Paul Geuchen, Thomas Heindl, Dominik Stöger, Felix Voigtländer
On the Lipschitz constant of random neural networks. CoRR (2023)
Julia Kostin, Felix Krahmer, Dominik Stöger
How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise? CoRR (2023)
Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing. COLT (2023)
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) (2023)
Kiryung Lee, Dominik Stöger
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing. CoRR (2022)
Anna Ma, Dominik Stöger, Yizhe Zhu
Robust recovery of low-rank matrices and low-tubal-rank tensors from noisy sketches. CoRR (2022)
Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi
Understanding Overparameterization in Generative Adversarial Networks. CoRR (2021)
Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi
Understanding Over-parameterization in Generative Adversarial Networks. ICLR (2021)
Dominik Stöger, Mahdi Soltanolkotabi
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction. CoRR (2021)
Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate. NeurIPS (2021)
Tim Fuchs, David Gross, Peter Jung, Felix Krahmer, Richard Kueng, Dominik Stöger
Proof methods for robust low-rank matrix recovery. CoRR (2021)
Dominik Stöger, Mahdi Soltanolkotabi
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction. NeurIPS (2021)
Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger
-minimization with Global Linear Convergence Rate. CoRR (2020)
Felix Krahmer, Dominik Stöger
On the convex geometry of blind deconvolution and matrix completion. CoRR (2019)
Jakob Geppert, Felix Krahmer, Dominik Stöger
Sparse power factorization: balancing peakiness and sample complexity. Adv. Comput. Math. 45 (3) (2019)
Felix Krahmer, Dominik Stöger
Complex phase retrieval from subgaussian measurements. CoRR (2019)
Dominik Stöger, Jakob Geppert, Felix Krahmer
Sparse Power Factorization With Refined Peakiness Conditions. SSP (2018)
Jakob Geppert, Felix Krahmer, Dominik Stöger
Sparse Power Factorization: Balancing peakiness and sample complexity. CoRR (2018)
Felix Krahmer, Dominik Stöger
Blind Deconvolution: Convex Geometry and Noise Robustness. ACSSC (2018)
Peter Jung, Felix Krahmer, Dominik Stöger
Blind Demixing and Deconvolution at Near-Optimal Rate. IEEE Trans. Inf. Theory 64 (2) (2018)
Dominik Stöger, Peter Jung, Felix Krahmer
Blind Demixing and Deconvolution with Noisy Data: Near-optimal Rate. WSA (2017)
Peter Jung, Felix Krahmer, Dominik Stöger
Blind Demixing and Deconvolution at Near-Optimal Rate. CoRR (2017)