Timezone: »

 
Oral
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Difan Zou · Pan Xu · Quanquan Gu

Thu Jul 12 05:00 AM -- 05:10 AM (PDT) @ A4
in Monte Carlo Methods 1 »
We propose a fast stochastic Hamilton Monte Carlo (HMC) method, for sampling from a smooth and strongly log-concave distribution. At the core of our proposed method is a variance reduction technique inspired by the recent advance in stochastic optimization. We show that, to achieve $\epsilon$ accuracy in 2-Wasserstein distance, our algorithm achieves $\tilde O\big(n+\kappa^{2}d^{1/2}/\epsilon+\kappa^{4/3}d^{1/3}n^{2/3}/\epsilon^{2/3}\big)$ gradient complexity (i.e., number of component gradient evaluations), which outperforms the state-of-the-art HMC and stochastic gradient HMC methods in a wide regime. We also extend our algorithm for sampling from smooth and general log-concave distributions, and prove the corresponding gradient complexity as well. Experiments on both synthetic and real data demonstrate the superior performance of our algorithm.
[ PDF]  [ Video] 

Author Information

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

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors