Dominik Stöger

According to our database, Dominik Stöger authored at least 21 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity.

Dominik Stöger, Yizhe Zhu

CoRR, 2024

Linear Convergence of Iteratively Reweighted Least Squares for Nuclear Norm Minimization.

Christian Kümmerle, Dominik Stöger

Proceedings of the 13th IEEE Sensor Array and Multichannel Signal Processing Workshop, 2024

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., December, 2023

Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing.

Kiryung Lee, Dominik Stöger

SIAM J. Math. Data Sci., September, 2023

Upper and lower bounds for the Lipschitz constant of random neural networks.

Paul Geuchen, Thomas Heindl, Dominik Stöger, Felix Voigtländer

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

How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise?

Julia Kostin, Felix Krahmer, Dominik Stöger

CoRR, 2023

Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing.

Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2021

Proof methods for robust low-rank matrix recovery.

Tim Fuchs, David Gross, Peter Jung, Felix Krahmer, Richard Kueng, Dominik Stöger

CoRR, 2021

Understanding Overparameterization in Generative Adversarial Networks.

Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

CoRR, 2021

Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction.

Dominik Stöger, Mahdi Soltanolkotabi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate.

Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Understanding Over-parameterization in Generative Adversarial Networks.

Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Iteratively Reweighted Least Squares for 𝓁₁-minimization with Global Linear Convergence Rate.

Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger

CoRR, 2020

2019

Complex phase retrieval from subgaussian measurements.

Felix Krahmer, Dominik Stöger

CoRR, 2019

On the convex geometry of blind deconvolution and matrix completion.

Felix Krahmer, Dominik Stöger

CoRR, 2019

Sparse power factorization: balancing peakiness and sample complexity.

Jakob Geppert, Felix Krahmer, Dominik Stöger

Adv. Comput. Math., 2019

2018

Blind Demixing and Deconvolution at Near-Optimal Rate.

Peter Jung, Felix Krahmer, Dominik Stöger

IEEE Trans. Inf. Theory, 2018

Sparse Power Factorization With Refined Peakiness Conditions.

Dominik Stöger, Jakob Geppert, Felix Krahmer

Proceedings of the 2018 IEEE Statistical Signal Processing Workshop, 2018

Blind Deconvolution: Convex Geometry and Noise Robustness.

Felix Krahmer, Dominik Stöger

Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

2017

Blind Demixing and Deconvolution with Noisy Data: Near-optimal Rate.

Dominik Stöger, Peter Jung, Felix Krahmer

Proceedings of the WSA 2017, 2017