A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel. (arXiv:2111.10722v2 [stat.ML] UPDATED)

We propose a novel deterministic sampling method to approximate a target
distribution $\rho^*$ by minimizing the kernel discrepancy, also known as the
Maximum Mean Discrepancy (MMD). By employing the general \emph{energetic
variational inference} framework (Wang et al., 2021), we convert the problem of
minimizing MMD to solving a dynamic ODE system of the particles. We adopt the
implicit Euler numerical scheme to solve the ODE systems. This leads to a
proximal minimization problem in each iteration of updating the particles, …

arxiv flow kernel mean sampling