Stochastic Multiple Target Sampling Gradient Descent. (arXiv:2206.01934v3 [cs.LG] UPDATED)

Sampling from an unnormalized target distribution is an essential problem
with many applications in probabilistic inference. Stein Variational Gradient
Descent (SVGD) has been shown to be a powerful method that iteratively updates
a set of particles to approximate the distribution of interest. Furthermore,
when analysing its asymptotic properties, SVGD reduces exactly to a
single-objective optimization problem and can be viewed as a probabilistic
version of this single-objective optimization problem. A natural question then
arises: "Can we derive a probabilistic version …

arxiv gradient sampling stochastic