Interacting Contour Stochastic Gradient Langevin Dynamics

• URL: http://arxiv.org/abs/2202.09867v1
• Date: Sun, 20 Feb 2022 17:23:09 GMT
• Title: Interacting Contour Stochastic Gradient Langevin Dynamics
• Authors: Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang
• Abstract summary: We propose an interacting contour gradient Langevin dynamics (ICSGLD) 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.
• Score: 22.131194626068027
• License: http://creativecommons.org/licenses/by/4.0/
• 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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