Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

Score distillation sampling (SDS) has shown great promise in text-to-3D generation by
distilling pretrained large-scale text-to-image diffusion models, but suffers from over …

In this survey, we describe controlled interacting particle systems (CIPS) to approximate the
solution of the optimal filtering and the optimal control problems. Part I of the survey is …

Deep ensembles have recently gained popularity in the deep learning community for their
conceptual simplicity and efficiency. However, maintaining functional diversity between …

Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a
tailored sequence of learning tasks, starting from easy ones and subsequently increasing …

Bayesian inference problems require sampling or approximating high-dimensional
probability distributions. The focus of this paper is on the recently introduced Stein …

We present Path Integral Sampler~(PIS), a novel algorithm to draw samples from
unnormalized probability density functions. The PIS is built on the Schr\" odinger bridge …

Abstract Annealed Importance Sampling (AIS) and its Sequential Monte Carlo (SMC)
extensions are state-of-the-art methods for estimating normalizing constants of probability …

Connecting optimal transport and variational inference, we present a principled and
systematic framework for sampling and generative modelling centred around divergences …

Abstract Stein Variational Gradient Descent (SVGD), a popular sampling algorithm, is often
described as the kernelized gradient flow for the Kullback-Leibler divergence in the …