PAGE: A simple and optimal probabilistic gradient estimator for nonconvex optimization
In this paper, we propose a novel stochastic gradient estimator---ProbAbilistic Gradient Estimator (PAGE)---for nonconvex optimization. PAGE is easy to implement as it is designed via a small adjustment to vanilla SGD: in each iteration, PAGE uses the vanilla minibatch SGD update with probability $p...
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Main Authors: | LI, Zhize, BAO, Hongyan, ZHANG, Xiangliang, RICHTARIK, Peter |
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Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2021
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Online Access: | https://ink.library.smu.edu.sg/sis_research/8683 https://ink.library.smu.edu.sg/context/sis_research/article/9686/viewcontent/ICML21_full_page.pdf |
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Institution: | Singapore Management University |
Language: | English |
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