Online Covariance Matrix Estimation in Stochastic Gradient Descent-Reference-Cited by-同舟云学术

Online Covariance Matrix Estimation in Stochastic Gradient Descent

Published:2021-05-24 Issue: Volume: Page:1-30
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Zhu Wanrong¹,Chen Xi²,Wu Wei Biao¹

Affiliation:

1. Department of Statistics, University of Chicago;

2. Leonard N. Stern School of Business, New York University;

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2021.1933498

Reference43 articles.

1. Bach, F. R. and Moulines, E. (2013). Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) . In Advances in Neural Information Processing Systems.

2. Blum, J. R. (1954). Approximation methods which converge with probability one. Ann. Math. Statist., 25(2):382–386.

3. Bottou, L. (1998). Online learning and stochastic approximations. In On-line learning and stochastic approximations.

4. Chao, S.K. and Cheng, G. (2019). A generalization of regularized dual averaging and its dynamics. Preprint. Available at arXiv:1909.10072.

5. Chen, X., Lee, J. D., Tong, X. T., and Zhang, Y. (2020). Statistical inference for model parameters in stochastic gradient descent. Ann. Statist., 48(1):251–273.

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