Interacting Contour Stochastic Gradient Langevin Dynamics

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

Keywords: [ mcmc ] [ importance sampling ] [ stochastic gradient Langevin dynamics ]

[ Abstract ]
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Thu 28 Apr 6:30 p.m. PDT — 8:30 p.m. PDT


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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