Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization-Reference-Cited by-同舟云学术

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Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization

Published:2024-04-29 Issue: Volume: Page:1-31
ISSN:1055-6788
Container-title:Optimization Methods and Software
language:en
Short-container-title:Optimization Methods and Software

Author:

Carlon André Gustavo¹,Espath Luis²,Tempone Raúl¹³⁴

Affiliation:

1. Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science & Technology (KAUST), Thuwal, Saudi Arabia

2. School of Mathematical Sciences, University of Nottingham, Nottingham, UK

3. Department of Mathematics, RWTH Aachen University, Aachen, Germany

4. Alexander von Humboldt Professor in Mathematics for Uncertainty Quantification, RWTH Aachen University, Aachen, Germany

Funder

KAUST

Alexander von Humboldt Foundation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10556788.2024.2339226

Reference31 articles.

1. Searching for exotic particles in high-energy physics with deep learning

2. R. Bollapragada J. Nocedal D. Mudigere H.J. Shi and P.T.P. Tang A progressive batching L-BFGS method for machine learning in International Conference on Machine Learning PMLR 2018 pp. 620–629.

3. Sgd-qn: Careful quasi-Newton stochastic gradient descent;Bordes A.;J. Mach. Learn. Res.,2009

4. Optimization Methods for Large-Scale Machine Learning

5. Convex Optimization

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