Big Learning with Bayesian methods - Zhu, Jun; Chen, Jianfei; Hu, Wenbo; Zhang, Bo (National science review, 2017)
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.