ARC@ORU - SDE Matching: Scalable Variational Inference for Stochastic Differential Equations
12 september 2025 13:15 – 14:30 Visual Lab, ARC
Christian A. Naesseth, biträdande professor i maskininlärning vid University of Amsterdam, presenterar en ny metod för att träna Latent SDE-modeller utan tidskrävande simuleringar. Metoden, SDE Matching, bygger på idéer från moderna generativa modeller och möjliggör effektivare analys av tidsserier och sekvensdata med bibehållen prestanda.
Speaker
Christian A. Naesseth, Assistant Professor of Machine Learning at the University of Amsterdam. Also a member of the Amsterdam Machine Learning Lab, the lab manager of the UvA-Bosch Delta Lab 2, and an ELLIS member.
Christans research interests span generative modeling, uncertainty quantification, reasoning, and machine learning, as well as their application to the sciences. He is currently working on generative models (diffusions, flows, generative flow networks, …), approximate inference (variational and Monte Carlo methods), probabilistic modelling (natural sciences, computer vision, health, etc.), uncertainty quantification and hypothesis testing (E-values, conformal prediction, …)
Previously, he was a postdoctoral research scientist with David Blei at the Data Science Institute, Columbia University. He completed his PhD in Electrical Engineering at Linköping University, advised by Fredrik Lindsten and Thomas Schön.
About the talk
The Latent Stochastic Differential Equation (SDE) is a powerful tool for time series and sequence modeling. However, training Latent SDEs typically relies on adjoint sensitivity methods, which depend on simulation, discretisation, and backpropagation through approximate SDE solutions, which limit scalability. In this work, we propose SDE Matching, a new simulation- and discretisation-free method for training Latent SDEs. Inspired by modern Score- and Flow Matching algorithms for learning generative dynamics, we extend these ideas to the domain of stochastic dynamics for time series and sequence modeling, eliminating the need for costly numerical simulations. The results demonstrate that SDE Matching achieves performance comparable to adjoint sensitivity methods while drastically reducing computational complexity.
Program
13.15 - 13.20 Welcome and introduction
13.20 - 14.00 Talk, Assistant Professor Christian A. Naesseth,
14.00 - 14.30 Questions and discussion
14.30 Coffee
Host: Amy Loutfi, professor in Computer Science, ARC Director