New Directions for Stochastic Differential Equations and Machine Learning

In recent years, the field of machine learning (ML) has seen tremendous progress, with many breakthroughs directly connected to the well-studied mathematical theory of Stochastic Differential Equations (SDEs). This increasingly fruitful relationship between SDEs and ML has produced several state-of-the-art innovations, ranging from Langevin algorithms in Bayesian learning to score-based diffusion models in computer vision. This workshop aims to bring the SDE and ML communities closer together and “sow the seeds” for future interdisciplinary and impactful research.

Topics: SDE-inspired learning algorithms and architectures • Computational or learning-based algorithms for SDEs • Theoretical connections between SDEs and machine learning • Applications and areas of opportunity between disciplines