Interacting Contour Stochastic Gradient Langevin Dynamics
Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang
Abstract
We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions. We show that ICSGLD can be theoretically more efficient than a single-chain CSGLD with an equivalent computational budget. We also present a novel random-field function, which facilitates the estimation of self-adapting parameters in big data and obtains free mode explorations. Empirically, we compare the proposed algorithm with popular benchmark methods for posterior sampling. The numerical results show a great potential of ICSGLD for large-scale uncertainty estimation tasks.
BibTeX
@inproceedings{
deng2022interacting,
title={Interacting Contour Stochastic Gradient Langevin Dynamics},
author={Wei Deng and Siqi Liang and Botao Hao and Guang Lin and Faming Liang},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=IK9ap6nxXr2}
}