Big Learning with Bayesian methods  

作　　者：Jun Zhu Jianfei Chen Wenbo Hu Bo Zhang 

机构地区：[1]TNList Lab, State Key Lab for Intelligent Technology and Systems, CBICR Center, Department of Computer Science and Technology, Tsinghua University

出　　处：《National Science Review》2017年第4期627-651,共25页国家科学评论（英文版）

基　　金：supported by the National Basic Research Program of China(2013CB329403);the National Natural Science Foundation of China(61620106010,61621136008 and 61332007);the Youth Top-notch Talent Support Program

摘　　要：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 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.

关 键 词：Big Bayesian Learning Bayesian non-parametrics regularized Bayesian inference scalable algorithms 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]