3 papers
stat.ML2026
Wasserstein Residuals: Learning Gradient Flows from Population Dynamics
Markus Heinonen, Yair Shenfeld, Ricardo Baptista +4
Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient flow (WGF): a curve of distribu…
stat.ML2026
Dynestyx: A Probabilistic Programming Library for Dynamical Systems
Daniel Waxman, Dmitry Batenkov, John Feser +4
State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning.…
cs.LG2024
Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
Kurt Butler, Daniel Waxman, Petar M. DjuriÄ
Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have…