Hybrid stochastic-deterministic minibatch proximal gradient: Less-than-single-pass optimization with nearly optimal generalization

Stochastic variance-reduced gradient (SVRG) algorithms have been shown to work favorably in solving large-scale learning problems. Despite the remarkable success, the stochastic gradient complexity of SVRG-type algorithms usually scales linearly with data size and thus could still be expensive for h...

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Main Authors:	ZHOU, Pan, YUAN, Xiaotong
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2020
主題:	Databases and Information Systems
在線閱讀:	https://ink.library.smu.edu.sg/sis_research/9030 https://ink.library.smu.edu.sg/context/sis_research/article/10033/viewcontent/2020_ICML_HSDN.pdf
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機構:	Singapore Management University
語言:	English

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spelling	sg-smu-ink.sis_research-100332024-07-25T08:01:39Z Hybrid stochastic-deterministic minibatch proximal gradient: Less-than-single-pass optimization with nearly optimal generalization ZHOU, Pan YUAN, Xiaotong Stochastic variance-reduced gradient (SVRG) algorithms have been shown to work favorably in solving large-scale learning problems. Despite the remarkable success, the stochastic gradient complexity of SVRG-type algorithms usually scales linearly with data size and thus could still be expensive for huge data. To address this deficiency, we propose a hybrid stochastic-deterministic minibatch proximal gradient (HSDMPG) algorithm for strongly-convex problems that enjoys provably improved data-size-independent complexity guarantees. 2020-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9030 https://ink.library.smu.edu.sg/context/sis_research/article/10033/viewcontent/2020_ICML_HSDN.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Databases and Information Systems
spellingShingle	Databases and Information Systems ZHOU, Pan YUAN, Xiaotong Hybrid stochastic-deterministic minibatch proximal gradient: Less-than-single-pass optimization with nearly optimal generalization
description	Stochastic variance-reduced gradient (SVRG) algorithms have been shown to work favorably in solving large-scale learning problems. Despite the remarkable success, the stochastic gradient complexity of SVRG-type algorithms usually scales linearly with data size and thus could still be expensive for huge data. To address this deficiency, we propose a hybrid stochastic-deterministic minibatch proximal gradient (HSDMPG) algorithm for strongly-convex problems that enjoys provably improved data-size-independent complexity guarantees.
format	text
author	ZHOU, Pan YUAN, Xiaotong
author_facet	ZHOU, Pan YUAN, Xiaotong
author_sort	ZHOU, Pan
title	Hybrid stochastic-deterministic minibatch proximal gradient: Less-than-single-pass optimization with nearly optimal generalization
title_short	Hybrid stochastic-deterministic minibatch proximal gradient: Less-than-single-pass optimization with nearly optimal generalization
title_full	Hybrid stochastic-deterministic minibatch proximal gradient: Less-than-single-pass optimization with nearly optimal generalization
title_fullStr	Hybrid stochastic-deterministic minibatch proximal gradient: Less-than-single-pass optimization with nearly optimal generalization
title_full_unstemmed	Hybrid stochastic-deterministic minibatch proximal gradient: Less-than-single-pass optimization with nearly optimal generalization
title_sort	hybrid stochastic-deterministic minibatch proximal gradient: less-than-single-pass optimization with nearly optimal generalization
publisher	Institutional Knowledge at Singapore Management University
publishDate	2020
url	https://ink.library.smu.edu.sg/sis_research/9030 https://ink.library.smu.edu.sg/context/sis_research/article/10033/viewcontent/2020_ICML_HSDN.pdf
_version_	1814047712357646336

Hybrid stochastic-deterministic minibatch proximal gradient: Less-than-single-pass optimization with nearly optimal generalization

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