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arXiv · 2202.09867

Interacting Contour Stochastic Gradient Langevin Dynamics

Wei Deng·Siqi Liang·Botao Hao·Guang Lin·Faming Liang
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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.

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Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang. 2022-02-20. Interacting Contour Stochastic Gradient Langevin Dynamics. https://arxiv.org/abs/2202.09867

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