Fast Sampling for Bayesian Max-Margin Models
This work addresses a computational bottleneck for researchers and practitioners using Bayesian max-margin models in applications like text categorization and social network prediction, representing an incremental improvement.
The paper tackles the challenge of slow Monte Carlo sampling for Bayesian max-margin models on large-scale datasets by introducing stochastic subgradient Hamiltonian Monte Carlo methods, which are shown to be computationally efficient and effective in experiments.
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.