Control Variates for Stochastic Gradient MCMC

Understand

It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly with dataset size.

• A popular class of methods for solving this issue is stochastic gradient MCMC.
• These methods use a noisy estimate of the gradient of the log posterior, which reduces the per iteration computational cost of the algorithm.
• Despite this, there are a number of results suggesting that stochastic gradient Langevin dynamics (SGLD), probably the most popular of these methods, still has computational cost proportional to the dataset size.