Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval and matrix completion

C Ma, K Wang, Y Chi, Y Chen - International Conference on …, 2018 - proceedings.mlr.press
Recent years have seen a flurry of activities in designing provably efficient nonconvex
optimization procedures for solving statistical estimation problems. For various problems like …
被引用次数：282 相关文章
[PDF] github.io

[PDF][PDF] Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind …

C Ma, K Wang, Y Chi, Y Chen - yuxinchen2020.github.io
Recent years have seen a flurry of activities in designing provably efficient nonconvex
procedures for solving statistical estimation problems. Due to the highly nonconvex nature of …
[PDF] cmu.edu

[PDF][PDF] Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion and Blind …

C Ma, K Wang, Y Chi, Y Chen - users.ece.cmu.edu
Recent years have seen a flurry of activities in designing provably efficient nonconvex
procedures for solving statistical estimation problems. Due to the highly nonconvex nature of …

Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution.

C Ma, K Wang, Y Chi, Y Chen - Foundations of …, 2020 - search.ebscohost.com
Recent years have seen a flurry of activities in designing provably efficient nonconvex
procedures for solving statistical estimation problems. Due to the highly nonconvex nature of …

Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution

C Ma, K Wang, Y Chi, Y Chen - arXiv preprint arXiv:1711.10467, 2017 - arxiv.org
Recent years have seen a flurry of activities in designing provably efficient nonconvex
procedures for solving statistical estimation problems. Due to the highly nonconvex nature of …

Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution

C Ma, K Wang, Y Chi, Y Chen - Foundations of Computational …, 2020 - oar.princeton.edu
Recent years have seen a flurry of activities in designing provably efficient nonconvex
procedures for solving statistical estimation problems. Due to the highly nonconvex nature of …

Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval and matrix completion

C Ma, K Wang, Y Chi, Y Chen - … on Machine Learning …, 2018 - collaborate.princeton.edu
Recent years have seen a flurry of activities in designing provably efficient nonconvex
optimization procedures for solving statistical estimation problems. For various problems like …

[引用][C] Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind …

C Ma, K Wang, Y Chi, Y Chen - Foundations of Computational …, 2019 - cir.nii.ac.jp
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges
Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution | CiNii Research …
[PDF] nsf.gov

[PDF][PDF] Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind …

C Ma, K Wang, Y Chi, Y Chen - Foundations of Computational …, 2019 - par.nsf.gov
Recent years have seen a flurry of activities in designing provably efficient nonconvex
procedures for solving statistical estimation problems. Due to the highly nonconvex nature of …

Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution

C Ma, K Wang, Y Chi, Y Chen - Foundations of Computational …, 2020 - go.gale.com
A wide spectrum of science and engineering applications calls for solutions to a nonlinear
system of equations. Imagine we have collected a set of data points [Formula omitted] …