Dominik Stöger

31 papers A* 5Misc 1Journal 22Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing. IEEE Trans. Inf. Theory Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie
2025	J	`jnl`	Linear regression with overparameterized linear neural networks: Tight upper and lower bounds for implicit ℓ CoRR Hannes Matt, Dominik Stöger
2025	J	`jnl`	Near-optimal estimates for the ℓ CoRR Sjoerd Dirksen, Patrick Finke, Paul Geuchen, Dominik Stöger, Felix Voigtländer
2025	A*	`conf`	Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity Extended Abstract. COLT Dominik Stöger, Yizhe Zhu
2024	—	`conf`	Linear Convergence of Iteratively Reweighted Least Squares for Nuclear Norm Minimization. SAM Christian Kümmerle, Dominik Stöger
2024	J	`jnl`	Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity. CoRR Dominik Stöger, Yizhe Zhu
2023	J	`jnl`	How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise? CoRR Julia Kostin, Felix Krahmer, Dominik Stöger
2023	J	`jnl`	How to induce regularization in generalized linear models: A guide to reparametrizing gradient flow. CoRR Hung-Hsu Chou, Johannes Maly, Dominik Stöger
2023	A*	`conf`	Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing. COLT Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie
2023	J	`jnl`	Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing. CoRR Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie
2023	J	`jnl`	Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing. SIAM J. Math. Data Sci. Kiryung Lee, Dominik Stöger
2023	J	`jnl`	Robust Recovery of Low-Rank Matrices and Low-Tubal-Rank Tensors from Noisy Sketches. SIAM J. Matrix Anal. Appl. Anna Ma, Dominik Stöger, Yizhe Zhu
2023	J	`jnl`	Upper and lower bounds for the Lipschitz constant of random neural networks. CoRR Paul Geuchen, Thomas Heindl, Dominik Stöger, Felix Voigtländer
2022	J	`jnl`	Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing. CoRR Kiryung Lee, Dominik Stöger
2022	J	`jnl`	Robust recovery of low-rank matrices and low-tubal-rank tensors from noisy sketches. CoRR Anna Ma, Dominik Stöger, Yizhe Zhu
2021	A*	`conf`	Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate. NeurIPS Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger
2021	J	`jnl`	Proof methods for robust low-rank matrix recovery. CoRR Tim Fuchs, David Gross, Peter Jung, Felix Krahmer, Richard Kueng, Dominik Stöger
2021	A*	`conf`	Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction. NeurIPS Dominik Stöger, Mahdi Soltanolkotabi
2021	J	`jnl`	Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction. CoRR Dominik Stöger, Mahdi Soltanolkotabi
2021	A*	`conf`	Understanding Over-parameterization in Generative Adversarial Networks. ICLR Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi
2021	J	`jnl`	Understanding Overparameterization in Generative Adversarial Networks. CoRR Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi
2020	J	`jnl`	Iteratively Reweighted Least Squares for 𝓁 CoRR Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger
2019	J	`jnl`	Complex phase retrieval from subgaussian measurements. CoRR Felix Krahmer, Dominik Stöger
2019	J	`jnl`	On the convex geometry of blind deconvolution and matrix completion. CoRR Felix Krahmer, Dominik Stöger
2019	J	`jnl`	Sparse power factorization: balancing peakiness and sample complexity. Adv. Comput. Math. Jakob Geppert, Felix Krahmer, Dominik Stöger
2018	Misc	`conf`	Blind Deconvolution: Convex Geometry and Noise Robustness. ACSSC Felix Krahmer, Dominik Stöger
2018	J	`jnl`	Blind Demixing and Deconvolution at Near-Optimal Rate. IEEE Trans. Inf. Theory Peter Jung, Felix Krahmer, Dominik Stöger
2018	—	`conf`	Sparse Power Factorization With Refined Peakiness Conditions. SSP Dominik Stöger, Jakob Geppert, Felix Krahmer
2018	J	`jnl`	Sparse Power Factorization: Balancing peakiness and sample complexity. CoRR Jakob Geppert, Felix Krahmer, Dominik Stöger
2017	J	`jnl`	Blind Demixing and Deconvolution at Near-Optimal Rate. CoRR Peter Jung, Felix Krahmer, Dominik Stöger
2017	—	`conf`	Blind Demixing and Deconvolution with Noisy Data: Near-optimal Rate. WSA Dominik Stöger, Peter Jung, Felix Krahmer