Fetching the paper…
Reading the bibliography…
We consider solving the low rank matrix sensing problem with Factorized Gradient Descend (FGD) method when the true rank is unknown and over-specified, which we refer to as over-parameterized matrix sensing.
Weak Convergence and Empirical Processes: With Applications to Statistics
A. W. van der Vaart and J. A. Wellner · 2000
Earlier work this paper cites.
A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization
S. Burer and R. D. Monteiro · 2003
Earlier work this paper cites.
Convex optimization
S. Boyd, S. P. Boyd, and L. Vandenberghe · 2004
Earlier work this paper cites.
Local minima and convergence in low-rank semidefinite programming
S. Burer and R. D. Monteiro · 2005
Earlier work this paper cites.
Quantum state tomography via compressed sensing
D. Gross, Y.-K. Liu, S. T. Flammia, S. Becker, and J. Eisert · 2010
Earlier work this paper cites.
Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization
B. Recht, M. Fazel, and P. A. Parrilo · 2010
Earlier work this paper cites.
Tight oracle inequalities for low-rank matrix recovery from a minimal number of noisy random measurements
E. J. Candes and Y. Plan · 2011
Earlier work this paper cites.
Robust principal component analysis?
E. J. Candès, X. Li, Y. Ma, and J. Wright · 2011
Earlier work this paper cites.
Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
V. Koltchinskii, K. Lounici, A. B. Tsybakov, et al · 2011
Earlier work this paper cites.
Estimation of (near) low-rank matrices with noise and high-dimensional scaling
S. Negahban and M. J. Wainwright · 2011
Earlier work this paper cites.
Sparcs: Recovering low-rank and sparse matrices from compressive measurements
A. E. Waters, A. C. Sankaranarayanan, and R. Baraniuk · 2011
Cited alongside, same era.
Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
S. Negahban and M. J. Wainwright · 2012
Cited alongside, same era.
User-friendly tail bounds for sums of random matrices
J. A. Tropp · 2012
Cited alongside, same era.
Low-rank matrix recovery from errors and erasures
Y. Chen, A. Jalali, S. Sanghavi, and C. Caramanis · 2013
Cited alongside, same era.
Low-rank matrix completion using alternating minimization
P. Jain, P. Netrapalli, and S. Sanghavi · 2013
Cited alongside, same era.
Understanding alternating minimization for matrix completion
M. Hardt · 2014
Cited alongside, same era.
Low-rank solutions of linear matrix equations via procrustes flow
S. Tu, R. Boczar, M. Simchowitz, M. Soltanolkotabi, and B. Recht · 2016
Later among the works it cites.
Q. Zheng and J. Lafferty · 2016
Later among the works it cites.
Statistical guarantees for the EM algorithm: From population to sample-based analysis
S. Balakrishnan, M. J. Wainwright, and B. Yu · 2017
Later among the works it cites.
Algorithmic regularization in over-parameterized matrix sensing and neural networks with quadratic activations
Y. Li, T. Ma, and H. Zhang · 2018
Later among the works it cites.
High Dimensional Probability. An Introduction with Applications in Data Science
R. Vershynin · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Chen and M. J. Wainwright · 2015
Cited alongside, same era.
Quantum tomography protocols with positivity are compressed sensing protocols
A. Kalev, R. L. Kosut, and I. H. Deutsch · 2015
Cited alongside, same era.
A convergent gradient descent algorithm for rank minimization and semidefinite programming from random linear measurements
Q. Zheng and J. Lafferty · 2015
Cited alongside, same era.
Matrix completion has no spurious local minimum
R. Ge, J. D. Lee, and T. Ma · 2016
Cited alongside, same era.
Dropping convexity for faster semi-definite optimization
S. Bhojanapalli, A. Kyrillidis, and S. Sanghavi
Cited in the paper.
Global optimality of local search for low rank matrix recovery
S. Bhojanapalli, B. Neyshabur, and N. Srebro
Cited in the paper.
Nonconvex optimization meets low-rank matrix factorization: An overview
Y. Chi, Y. M. Lu, and Y. Chen · 2019
Later among the works it cites.
High-Dimensional Statistics: A Non-Asymptotic Viewpoint
M. J. Wainwright · 2019
Later among the works it cites.
Sharp restricted isometry bounds for the inexistence of spurious local minima in nonconvex matrix recovery
R. Y. Zhang, S. Sojoudi, and J. Lavaei · 2019
Later among the works it cites.
On the minimax optimality of the EM algorithm for learning two-component mixed linear regression
J. Kwon, N. Ho, and C. Caramanis · 2020
Later among the works it cites.
How many samples is a good initial point worth in low-rank matrix recovery?
J. Zhang and R. Zhang · 2020
Later among the works it cites.