Abstract Stein Variational Gradient Descent (SVGD) is a nonparametric particle-based
deterministic sampling algorithm. Despite its wide usage, understanding the theoretical …

The stein variational gradient descent (SVGD) algorithm is a deterministic particle method
for sampling. However, a mean-field analysis reveals that the gradient flow corresponding to …

Let a smooth function $ f: T^ d\times T^ d\to\mathbb {R} $ over the $ d $-torus and $\beta>
0$. Consider the min-max objective functional $ F_\beta (\mu,\nu)=\iint fd\mu d\nu+\beta^{-1} …

Particle gradient descent, which uses particles to represent a probability measure and
performs gradient descent on particles in parallel, is widely used to optimize functions of …

We provide finite-particle convergence rates for the Stein Variational Gradient Descent
(SVGD) algorithm in the Kernelized Stein Discrepancy ($\mathsf {KSD} $) and Wasserstein …

Environmental sound classification is a rapidly growing research area with numerous
applications. However, current models frequently face challenges in accurately identifying …

Performing inference in Bayesian models requires sampling algorithms to draw samples
from the posterior. This becomes prohibitively expensive as the size of data sets increase …

In this paper, we study the statistical and geometrical properties of the Kullback-Leibler
divergence with kernel covariance operators (KKL) introduced by Bach [2022]. Unlike the …

We investigate the minimal error in approximating a general probability measure $\mu $ on
$\mathbb {R}^ d $ by the uniform measure on a finite set with prescribed cardinality $ n …

Modern machine learning applications, such as generative modeling and probabilistic
inference, demand a new generation of methodologies for optimizing over the space of …