Universal halting times in optimization and machine learning
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[1] M. Hestenes,et al. Methods of conjugate gradients for solving linear systems , 1952 .
[2] A. Greenbaum. Behavior of slightly perturbed Lanczos and conjugate-gradient recurrences , 1989 .
[3] Anne Greenbaum,et al. Predicting the Behavior of Finite Precision Lanczos and Conjugate Gradient Computations , 2015, SIAM J. Matrix Anal. Appl..
[4] C. Tracy,et al. Level-spacing distributions and the Airy kernel , 1992, hep-th/9211141.
[5] R. Adler,et al. Random Fields and Geometry , 2007 .
[6] Antonio Auffinger,et al. Random Matrices and Complexity of Spin Glasses , 2010, 1003.1129.
[7] Léon Bottou,et al. Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.
[8] N. Pillai,et al. Universality of covariance matrices , 2011, 1110.2501.
[9] Joshua Correll,et al. A neural computation model for decision-making times , 2012 .
[10] H. Yau,et al. On the principal components of sample covariance matrices , 2014, 1404.0788.
[11] P. Deift,et al. Universality in numerical computations with random data , 2014, Proceedings of the National Academy of Sciences.
[12] Yann LeCun,et al. Explorations on high dimensional landscapes , 2014, ICLR.
[13] P. Deift,et al. On the condition number of the critically-scaled Laguerre Unitary Ensemble , 2015, 1507.00750.
[14] P. Deift,et al. How long does it take to compute the eigenvalues of a random, symmetric matrix? , 2012, 1203.4635.
[15] P. Deift,et al. Universality for the Toda algorithm to compute the eigenvalues of a random matrix , 2016 .
[16] Thomas Trogdon,et al. Universality for Eigenvalue Algorithms on Sample Covariance Matrices , 2017, SIAM J. Numer. Anal..