Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning

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Authors Zhanxing Zhu, Jianhong Wang, Yaodong Yang, Rui Luo, Jun Wang
Journal/Conference Name NeurIPS 2018 12
Paper Category
Paper Abstract We propose a new sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal distributions. It simulates the Nos\'e-Hoover dynamics of a continuously-tempered Hamiltonian system built on the distribution of interest. A significant advantage of this method is that it is not only able to efficiently draw representative i.i.d. samples when the distribution contains multiple isolated modes, but capable of adaptively neutralising the noise arising from mini-batches and maintaining accurate sampling. While the properties of this method have been studied using synthetic distributions, experiments on three real datasets also demonstrated the gain of performance over several strong baselines with various types of neural networks plunged in.
Date of publication 2017
Code Programming Language Python
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