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.

本论文提出了交互轮廓随机梯度 Langevin 动力学 (ICSGLD) 采样器，是一种具有高效交互的令人尴尬的并行多链轮廓随机梯度 Langevin 动力学 (CSGLD) 采样器。我们证明，ICSGLD 在等效计算预算下在理论上可以比单链 CSGLD 更有效。我们还提出了一种新的随机场函数，它有助于在大数据中估计自适应参数并获得自由模式探测。实证结果表明，与流行的基准方法进行对比，ICSGLD 在大规模不确定性估计任务中具有巨大的潜力。