This tutorial provides a comprehensive survey of methods for fine-tuning diffusion models to
optimize downstream reward functions. While diffusion models are widely known to provide …

Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but
they have thus far been held back by limitations in their simulation-based maximum …

We propose Image-to-Image Schr\" odinger Bridge (I $^ 2$ SB), a new class of conditional
diffusion models that directly learn the nonlinear diffusion processes between two given …

Generative flow networks (GFlowNets) are amortized variational inference algorithms that
are trained to sample from unnormalized target distributions over compositional objects. A …

This paper presents a tutorial overview of path integral (PI) approaches for stochastic
optimal control and trajectory optimization. We concisely summarize the theoretical …

Diffusion models excel at capturing complex data distributions, such as those of natural
images and proteins. While diffusion models are trained to represent the distribution in the …

Diffusion Schrödinger bridges (DSBs) have recently emerged as a powerful framework for
recovering stochastic dynamics via their marginal observations at different time points …

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 …

Abstract Mean-Field Game (MFG) serves as a crucial mathematical framework in modeling
the collective behavior of individual agents interacting stochastically with a large population …