Central limit theorems for stochastic gradient descent with averaging for stable manifolds
Author:
Affiliation:
1. Institute for Mathematical Stochastics, Faculty of Mathematics and Computer Science, University of Münster, Germany
2. Faculty of Mathematics, University of Bielefeld, Germany
Publisher
Institute of Mathematical Statistics
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Reference39 articles.
1. Rajendra Bhatia, Matrix analysis, Graduate Texts in Mathematics, vol. 169, Springer-Verlag, New York, 1997.
2. Arthur E. Albert and Leland A. Gardner, Jr., Stochastic approximations and nonlinear regression, MIT Press Research Monographs, No. 42, The M.I.T. Press, Cambridge, Mass., 1967.
3. Albert Benveniste, Michel Métivier, and Pierre Priouret, Adaptive algorithms and stochastic approximations, Applications of Mathematics (New York), vol. 22, Springer-Verlag, Berlin, 1990. Translated from the French by Stephen S. Wilson.
4. Julius R. Blum, Approximation methods which converge with probability one, Ann. Math. Statistics 25 (1954), 382–386.
5. K. L. Chung, On a stochastic approximation method, Ann. Math. Statistics 25 (1954), 463–483.
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