Variance Reduced Methods for Non-convex Composition Optimization-Reference-Cited by-同舟云学术

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Variance Reduced Methods for Non-convex Composition Optimization

Published:2021 Issue: Volume: Page:1-1
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 Liu¹,Liu Ji²,Tao Dacheng³

Affiliation:

1. Engineering and Information Technologies, University of Sydney, 4334 Sydney, New South Wales, Australia, (e-mail: liu.liu1@sydney.edu.au)

2. Computer Science, University of Rochester, Rochester, New York, United States, (e-mail: ji.liu.uwisc@gmail.com)

3. Engineering and Information Technologies, University of Sydney, 4334 Sydney, New South Wales, Australia, (e-mail: dacheng.tao@sydney.edu.au)

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/ielx7/34/4359286/09398583.pdf?arnumber=9398583

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1. Variance Reduced Domain Randomization for Reinforcement Learning With Policy Gradient;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-02

2. Addressing Machine Learning Problems in the Non-Negative Orthant;IEEE Transactions on Emerging Topics in Computational Intelligence;2024

3. Hybrid SGD algorithms to solve stochastic composite optimization problems with application in sparse portfolio selection problems;Journal of Computational and Applied Mathematics;2024-01

4. Stochastic variance reduced gradient with hyper-gradient for non-convex large-scale learning;Applied Intelligence;2023-10-10

5. Distributed stochastic compositional optimization problems over directed networks;Computational Optimization and Applications;2023-08-03

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