Fast sampling methods for Bayesian max-margin models

doi:10.1016/j.eswa.2016.10.036

2017-03-01

PRE

Wenbo Hu

Jun Zhu (Jun Zhu) *

Bo Zhang

DOI：10.1016/j.eswa.2016.10.036

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摘要

En 中文

Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, social network link prediction and crowdsourcing, and they conjoin the flexibility of Bayesian modeling and predictive strengths of max-margin learning. However, Monte Carlo sampling for these models still remains challenging, especially for applications that involve large-scale datasets. In this paper, we present the stochastic subgradient Hamiltonian Monte Carlo (HMC) methods, which are easy to implement and computationally efficient. We show the approximate detailed balance property of subgradient HMC which reveals a natural and validated generalization of the ordinary HMC. Furthermore, we investigate the variants that use stochastic subsampling and thermostats for better scalability and mixing. Using stochastic subgradient Markov Chain Monte Carlo (MCMC), we efficiently solve the posterior inference task of various Bayesian max-margin models and extensive experimental results demonstrate the effectiveness of our approach. (C) 2016 Elsevier Ltd. All rights reserved.

Keyword:

Inference

Stochastic MCMC

Subgradient MCMC

Bayesian max-margin models

Approximate detailed balance

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期刊

Expert Systems with Applications

IF:

7.5

论文数:

3.0W

被引数:

10.2W

机构

tsinghua university

学者数:

11.9W

论文数: 10.0W

被引数: 137

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