A General Descent Aggregation Framework for Gradient-Based Bi-Level Optimization-Reference-Cited by-同舟云学术

A General Descent Aggregation Framework for Gradient-Based Bi-Level Optimization

Published:2023-01-01 Issue:1 Volume:45 Page:38-57
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Liu Risheng¹^ORCID,Mu Pan²^ORCID,Yuan Xiaoming³^ORCID,Zeng Shangzhi⁴,Zhang Jin⁵^ORCID

Affiliation:

1. DUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian, China

2. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

3. Department of Mathematics, The University of Hong Kong, Hong Kang, China

4. Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada

5. Department of Mathematics, SUSTech International Center for Mathematics, National Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen, Guangdong, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Shenzhen Science and Technology Program

Shenzhen Natural Science Fund

Basic and Applied Basic Research Foundation of Guangdong Province

Pacific Institute for the Mathematical Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/9970415/09669130.pdf?arnumber=9669130

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4. On the iteration complexity of hypergradient computation;grazzi;Proc Int Conf Mach Learn,2020

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