  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.

Machine Learning;Machine Learning