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Poster
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Difan Zou · Pan Xu · Quanquan Gu

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #160
in Probabilistic Methods -- MCMC »

Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) algorithms have received increasing attention in both theory and practice. In this paper, we propose a Stochastic Recursive Variance-Reduced gradient HMC (SRVR-HMC) algorithm. It makes use of a semi-stochastic gradient estimator that recursively accumulates the gradient information to reduce the variance of the stochastic gradient. We provide a convergence analysis of SRVR-HMC for sampling from a class of non-log-concave distributions and show that SRVR-HMC converges faster than all existing HMC-type algorithms based on underdamped Langevin dynamics. Thorough experiments on synthetic and real-world datasets validate our theory and demonstrate the superiority of SRVR-HMC.

Keywords: MCMC · Bayesian Theory · Probabilistic Methods
[ Paper]  [ Poster] 

Author Information

Difan Zou (University of California, Los Angeles)
Pan Xu (University of California, Los Angeles)
Quanquan Gu (UCLA)

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