Big Learning with Bayesian Methods
This is an incremental survey that addresses the problem of scalability in Bayesian methods for researchers and practitioners dealing with large-scale machine learning applications.
The article surveys recent advances in Big Bayesian Learning, tackling the challenge of scalable machine learning with Big Data by covering nonparametric Bayesian methods for adaptive model complexity, regularized Bayesian inference for flexibility, and scalable algorithms using stochastic subsampling and distributed computing.
Explosive growth in data and 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 statistic 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 nonparametric 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.