The score function, which is the gradient of the log-density, provides a unique way to represent probability distributions. By working with distributions through score functions, researchers have been able to develop efficient tools for machine learning and statistics, collectively known as score-based methods.

Score-based methods have had a significant impact on vastly disjointed subfields of machine learning and statistics, such as generative modeling, Bayesian inference, hypothesis testing, control variates and Stein’s methods. For example, score-based generative models, or denoising diffusion models, have emerged as the state-of-the-art technique for generating high quality and diverse images. In addition, recent developments in Stein’s method and score-based approaches for stochastic differential equations (SDEs) have contributed to the developement of fast and robust Bayesian posterior inference in high dimensions. These have potential applications in engineering fields, where they could help improve simulation models.

At our workshop, we will bring together researchers from these various subfields to discuss the success of score-based methods, and identify common challenges across different research areas. We will also explore the potential for applying score-based methods to even more real-world applications, including in computer vision, signal processing, and computational chemistry. By doing so, we hope to folster collaboration among researchers and build a more cohesive research community focused on score-based methods.