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08/09/2023
How to induce regularization in generalized linear models: A guide to reparametrizing gradient flow
In this work, we analyze the relation between reparametrizations of grad...
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03/24/2023
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing
Recently, there has been significant progress in understanding the conve...
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03/17/2023
How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise?
In this paper, we study the problem of recovering two unknown signals fr...
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06/02/2022
Robust recovery of low-rank matrices and low-tubal-rank tensors from noisy sketches
A common approach for compressing large-scale data is through matrix ske...
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04/25/2022
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing
We consider the problem of reconstructing rank-one matrices from random ...
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06/28/2021
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction
Recently there has been significant theoretical progress on understandin...
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06/08/2021
Proof methods for robust low-rank matrix recovery
Low-rank matrix recovery problems arise naturally as mathematical formul...
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04/12/2021
Understanding Overparameterization in Generative Adversarial Networks
A broad class of unsupervised deep learning methods such as Generative A...
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12/22/2020
Iteratively Reweighted Least Squares for ℓ_1-minimization with Global Linear Convergence Rate
Iteratively Reweighted Least Squares (IRLS), whose history goes back mor...
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06/19/2019
Complex phase retrieval from subgaussian measurements
Phase retrieval refers to the problem of reconstructing an unknown vecto...
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02/28/2019
On the convex geometry of blind deconvolution and matrix completion
Low-rank matrix recovery from structured measurements has been a topic o...
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04/24/2018
