The dynamic Schrödinger bridge problem seeks a stochastic process that defines a
transport between two target probability measures, while optimally satisfying the criteria of …

We establish a connection between stochastic optimal control and generative models based
on stochastic differential equations (SDEs), such as recently developed diffusion …

Recently, a series of papers proposed deep learning-based approaches to sample from
target distributions using controlled diffusion processes, being trained only on the …

We tackle the problem of sampling from intractable high-dimensional density functions, a
fundamental task that often appears in machine learning and statistics. We extend recent …

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

Typical generative diffusion models rely on a Gaussian diffusion process for training the
backward transformations, which can then be used to generate samples from Gaussian …

Monte Carlo methods, Variational Inference, and their combinations play a pivotal role in
sampling from intractable probability distributions. However, current studies lack a unified …

Generative modelling paradigms based on denoising diffusion processes have emerged as
a leading candidate for conditional sampling in inverse problems. In many real-world …

We propose a framework for probabilistic forecasting of dynamical systems based on
generative modeling. Given observations of the system state over time, we formulate the …

Building upon score-based learning, new interest in stochastic localization techniques has
recently emerged. In these models, one seeks to noise a sample from the data distribution …