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 aimed to bring the SDE and ML communities closer together and “sow the seeds” for future interdisciplinary and impactful research. The following general themes were explored:

  • 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