13 citations · 36 across the 8 of their papers we have counts for
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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…
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…