Big Learning with Bayesian methods

The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine leaming algorithms, systems and applications with Big Data. Bayesian methods represent one important class of sta...

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Vydáno v:National science review Ročník 4; číslo 4; s. 627 - 651
Hlavní autoři: Zhu, Jun, Chen, Jianfei, Hu, Wenbo, Zhang, Bo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford University Press 01.07.2017
Témata:
scalable algorithms
regularized Bayesian inference
Big Bayesian Learning
Bayesian non-parametrics
ISSN:2095-5138, 2053-714X
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Abstract The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine leaming algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine leaming, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This artide provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning0 including non-parametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications. We also provide various new perspectives on the large-scale Bayesian modeling and inference.
AbstractList The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine leaming algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine leaming, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This artide provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning0 including non-parametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications. We also provide various new perspectives on the large-scale Bayesian modeling and inference.
Abstract The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including non-parametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications. We also provide various new perspectives on the large-scale Bayesian modeling and inference.
The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including non-parametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications. We also provide various new perspectives on the large-scale Bayesian modeling and inference.
Author Jun Zhu;Jianfei Chen;Wenbo Hu;Bo Zhang
AuthorAffiliation TNList Lab, State Key Lab for Intelligent Technology and Systems, CBICR Center, Department of Computer Science and Technology, Tsinghua University,Beijing 100084,China
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ContentType Journal Article
Copyright The Author(s) 2017. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2017
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Keywords scalable algorithms
regularized Bayesian inference
Big Bayesian Learning
Bayesian non-parametrics
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Notes Big Bayesian Learning, Bayesian non-parametrics, regularized Bayesian inference, scalablealgorithms
The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine leaming algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine leaming, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This artide provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning0 including non-parametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications. We also provide various new perspectives on the large-scale Bayesian modeling and inference.
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Snippet The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield...
Abstract The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging...
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