Natural Gradient Variational Bayes without Fisher Matrix Analytic Calculation and Its Inversion-Reference-Cited by-同舟云学术

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Natural Gradient Variational Bayes without Fisher Matrix Analytic Calculation and Its Inversion

Published:2024-08-22 Issue: Volume: Page:1-31
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Godichon-Baggioni A.¹,Nguyen D.²,Tran M-N.³

Affiliation:

1. Laboratoire de Probabilités Statistique et Modélisation, Sorbonne-Université, 75005, Paris, France;

2. Department of Mathematics, Marist College, 3399 North Road, Poughkeepsie NY 12601, United States;

3. Business Analytics discipline, The University of Sydney Business School, NSW 2006, Australia

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2024.2392904

Reference50 articles.

1. Alekh Agarwal Martin J Wainwright Peter Bartlett and Pradeep Ravikumar. Information-theoretic lower bounds on the oracle complexity of convex optimization. Advances in Neural Information Processing Systems 22:1–9 2009.

2. Shun-Ichi Amari. Natural gradient works efficiently in learning. Neural computation 10(2):251–276 1998.

3. Bernard Bercu Antoine Godichon and Bruno Portier. An efficient stochastic Newton algorithm for parameter estimation in logistic regressions. SIAM Journal on Control and Optimization 58(1):348–367 2020.

4. David M Blei Alp Kucukelbir and Jon D McAuliffe. Variational inference: A review for statisticians. Journal of the American statistical Association 112(518):859–877 2017.

5. Léon Bottou Frank E Curtis and Jorge Nocedal. Optimization methods for large-scale machine learning. Siam Review 60(2):223–311 2018.

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