  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.
