Blind Demixing and Deconvolution with Noisy Data: Near-optimal Rate. - Dominik Stöger; Peter Jung 001; Felix Krahmer (WSA, 2017)

Blind Deconvolution: Convex Geometry and Noise Robustness. - Felix Krahmer; Dominik Stöger (ACSSC, 2018)

Sparse Power Factorization With Refined Peakiness Conditions. - Dominik Stöger; Jakob Geppert; Felix Krahmer (SSP, 2018)

Blind Demixing and Deconvolution at Near-Optimal Rate. - Peter Jung 001; Felix Krahmer; Dominik Stöger (IEEE Trans. Inf. Theory, 2018)

Sparse power factorization: balancing peakiness and sample complexity. - Jakob Geppert; Felix Krahmer; Dominik Stöger (Adv. Comput. Math., 2019)

On the convex geometry of blind deconvolution and matrix completion. - Felix Krahmer; Dominik Stöger (CoRR, 2019)

Complex phase retrieval from subgaussian measurements. - Felix Krahmer; Dominik Stöger (CoRR, 2019)

Iteratively Reweighted Least Squares for 𝓁1-minimization with Global Linear Convergence Rate. - Christian Kümmerle; Claudio Mayrink Verdun; Dominik Stöger (CoRR, 2020)

Understanding Over-parameterization in Generative Adversarial Networks. - Yogesh Balaji; Mohammadmahdi Sajedi; Neha Mukund Kalibhat; Mucong Ding; Dominik Stöger; Mahdi Soltanolkotabi; Soheil Feizi (ICLR, 2021)

Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate. - Christian Kümmerle; Claudio Mayrink Verdun; Dominik Stöger (NeurIPS, 2021)

Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction. - Dominik Stöger; Mahdi Soltanolkotabi (NeurIPS, 2021)

Understanding Overparameterization in Generative Adversarial Networks. - Yogesh Balaji; Mohammadmahdi Sajedi; Neha Mukund Kalibhat; Mucong Ding; Dominik Stöger; Mahdi Soltanolkotabi; Soheil Feizi (CoRR, 2021)

Proof methods for robust low-rank matrix recovery. - Tim Fuchs; David Gross 003; Peter Jung 001; Felix Krahmer; Richard Kueng; Dominik Stöger (CoRR, 2021)

How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise? - Julia Kostin; Felix Krahmer; Dominik Stöger (CoRR, 2023)

How to induce regularization in generalized linear models: A guide to reparametrizing gradient flow. - Hung-Hsu Chou; Johannes Maly; Dominik Stöger (CoRR, 2023)

Upper and lower bounds for the Lipschitz constant of random neural networks. - Paul Geuchen; Thomas Heindl; Dominik Stöger; Felix Voigtländer (CoRR, 2023)

Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing. - Kiryung Lee; Dominik Stöger (SIAM J. Math. Data Sci., 2023)

Robust Recovery of Low-Rank Matrices and Low-Tubal-Rank Tensors from Noisy Sketches. - Anna Ma; Dominik Stöger; Yizhe Zhu (SIAM J. Matrix Anal. Appl., 2023)

Linear Convergence of Iteratively Reweighted Least Squares for Nuclear Norm Minimization. - Christian Kümmerle; Dominik Stöger (SAM, 2024)

Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity. - Dominik Stöger; Yizhe Zhu (CoRR, 2024)

Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity Extended Abstract. - Dominik Stöger; Yizhe Zhu (COLT, 2025)

Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing. - Mahdi Soltanolkotabi; Dominik Stöger; Changzhi Xie (IEEE Trans. Inf. Theory, 2025)

Linear regression with overparameterized linear neural networks: Tight upper and lower bounds for implicit ℓ1-regularization. - Hannes Matt; Dominik Stöger (CoRR, 2025)

Near-optimal estimates for the ℓp-Lipschitz constants of deep random ReLU neural networks. - Sjoerd Dirksen; Patrick Finke; Paul Geuchen; Dominik Stöger; Felix Voigtländer (CoRR, 2025)