Closing the Approximation Gap in Simulation-free Latent SDEs
Recovering dynamical systems from noisy observations is a recurring challenge across scientific domains, including neuroscience and physics. Latent stochastic differential equations (SDEs) address this by modeling the system as an unobserved state that evolves according to a learnable SDE and generates the observations. Variational inference (VI) provides a tractable objective for fitting latent SDEs. Traditional VI algorithms evaluate this objective by numerical simulation over a time discretization, trading fidelity for computational cost. A recent class of algorithms, simulation-free VI, sidesteps this tradeoff by parameterizing the posterior through its instantaneous marginals rather ...
| Authors | Henry D. Smith, Brian L. Trippe, Scott W. Linderman |
|---|---|
| Primary category | stat.ML |
| Published | 15 June 2026 |
| Last updated at source | 30 September 2026 |
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