Distributed Gradient Descent for Functional Learning-Reference-Cited by-同舟云学术

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Distributed Gradient Descent for Functional Learning

Published:2024-09 Issue:9 Volume:70 Page:6547-6571
ISSN:0018-9448
Container-title:IEEE Transactions on Information Theory
language:
Short-container-title:IEEE Trans. Inform. Theory

Author:

Yu Zhan¹^ORCID,Fan Jun¹^ORCID,Shi Zhongjie²^ORCID,Zhou Ding-Xuan³^ORCID

Affiliation:

1. Department of Mathematics, Hong Kong Baptist University, Hong Kong, China

2. Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China

3. School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia

Funder

InnoHK Initiative, the Government of the Government of the Hong Kong Special Administrative Region (HKSAR), Laboratory for AI-Powered Financial Technologies, the Research Grants Council of Hong Kong

National Natural Science Foundation of China

Research Grants Council of Hong Kong

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/18/10642977/10599555.pdf?arnumber=10599555

Reference57 articles.

1. Information Theory With Kernel Methods

2. On regularization algorithms in learning theory

3. Prediction in functional linear regression

4. Minimax and Adaptive Prediction for Functional Linear Regression

5. Distributed semi-supervised learning with kernel ridge regression;Chang;J. Mach. Learn. Res.,2017

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