The paper investigates how to recover the parameters of a multivariate Ornstein-Uhlenbeck process using only steady-state observational and interventional data. It proves that a single intervention per strongly connected component of the drift graph is sufficient to identify all parameters generically, up to a global scaling factor, provided the SCC condensation graph is connected with a single root and certain spectral conditions hold. A recursive learning algorithm and a regularized least-squares estimator are proposed, and experiments confirm the theoretical results.
By Saber Salehkaleybar
arXiv:2603. 08311v2 Announce Type: replace-cross Abstract: We study identifiability in continuous-time linear stationary stochastic differential equations with a known causal structure.
By Gijs van Seeventer, Saber Salehkaleybar
arXiv:2604.27443v3 Announce Type: replace
Abstract: Generating continuous-time, continuous-space stochastic processes (e.g., videos, weather forecasts) conditioned on partial observations (e.g., firs...
By Gabe Guo, Thanawat Sornwanee, Lutong Hao, Elon Litman, Stefano Ermon, Jose Blanchet
arXiv:2606. 30467v1 Announce Type: cross Abstract: We consider sparse multivariate stochastic systems that evolve in continuous time according to a causal mechanism and present methodology to recover the system's time-infinitesimal transition mechanism from mere cross-sectional data.
By Richard Schwank, Mathias Drton
arXiv:2608. 04827v1 Announce Type: cross Abstract: We introduce the Intrinsic Hybrid Latent Diffusion Model (ILDM), a generative framework that integrates probabilistic dimensionality reduction with geometry-aware diffusion on unknown manifolds.
By Yizhu Wang, Mu Niu, Xiaochen Yang
arXiv:2605. 19805v2 Announce Type: replace-cross Abstract: Irregular multivariate time series impose a trade-off for long-horizon forecasting: discrete methods can distort temporal structure via re-gridding, while continuous-time models often require sequential solvers prone to drift.
By Zinuo You, Jin Zheng, John Cartlidge