Accelerating Convergence of Stein Variational Gradient Descent via Deep
  Unfolding
Yuya Kawamura Satoshi Takabe
Stein variational gradient descent (SVGD) is a prominent particle-based
variational inference method used for sampling a target distribution. SVGD has
attracted interest for application in machine-learning techniques such as
Bayesian inference. In this paper, we propose novel trainable algorithms that
incorporate a deep-learning technique called deep unfolding,into SVGD. This
approach facilitates the learning of the internal parameters of SVGD, thereby
accelerating its convergence speed. To evaluate the proposed trainable SVGD
algorithms, we conducted numerical simulations of three tasks: sampling a
one-dimensional Gaussian mixture, performing Bayesian logistic regression, and
learning Bayesian neural networks. The results show that our proposed
algorithms exhibit faster convergence than the conventional variants of SVGD.