论文标题
相互作用的轮廓随机梯度Langevin动力学
Interacting Contour Stochastic Gradient Langevin Dynamics
论文作者
论文摘要
我们提出了一种相互作用的轮廓随机梯度Langevin Dynamics（ICSGLD）采样器，这是一种具有有效相互作用的令人尴尬的平行多链轮廓随机梯度Langevin Dynamics（CSGLD）采样器。我们表明，与具有同等计算预算的单链CSGLD相比，ICSGLD在理论上可以更有效。我们还提出了一种新颖的随机函数，该功能促进了大数据中自适应参数的估计，并获得了自由模式探索。从经验上讲，我们将所提出的算法与流行的基准方法进行比较。数值结果显示了ICSGLD在大规模不确定性估计任务中的巨大潜力。
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.
