13 citations · 36 across the 8 of their papers we have counts for
9 papers
Tight Majorizations and Convergence Rates of Nuclear Norm Minimization IRLS
Christian Kümmerle, Tomas Masak, Dominik Stöger
Iteratively reweighted least squares (IRLS) methods constitute a natural approach to nuclear norm minimization, but their convergence rates and the role of the weight operator have…
Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity
Dominik Stöger, Yizhe Zhu
For the problem of reconstructing a low-rank matrix from a few linear measurements, two classes of algorithms have been widely studied in the literature: convex approaches based on…
How to induce regularization in linear models: A guide to reparametrizing gradient flow
Hung-Hsu Chou, Johannes Maly, Dominik Stöger
In this work, we analyze the relation between reparametrizations of gradient flow and the induced implicit bias in linear models, which encompass various basic regression tasks. In…
How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise?
Julia Kostin, Felix Krahmer, Dominik Stöger
In this paper, we study the problem of recovering two unknown signals from their convolution, which is commonly referred to as blind deconvolution. Reformulation of blind deconvolu…
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing
Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie
Recently, there has been significant progress in understanding the convergence and generalization properties of gradient-based methods for training overparameterized learning model…
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction
Dominik Stöger, Mahdi Soltanolkotabi
Recently there has been significant theoretical progress on understanding the convergence and generalization of gradient-based methods on nonconvex losses with overparameterized mo…
Understanding Overparameterization in Generative Adversarial Networks
Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat +4
A broad class of unsupervised deep learning methods such as Generative Adversarial Networks (GANs) involve training of overparameterized models where the number of parameters of th…
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate
Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger
The recovery of sparse data is at the core of many applications in machine learning and signal processing. While such problems can be tackled using -regularization as in th…
Sparse Power Factorization: Balancing peakiness and sample complexity
Jakob Geppert, Felix Krahmer, Dominik Stöger
In many applications, one is faced with an inverse problem, where the known signal depends in a bilinear way on two unknown input vectors. Often at least one of the input vectors i…