Author

Saber Salehkaleybar

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#artificial intelligence Preprint Sep 2026

Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders

We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unknown number of exogenous latent confounders, bounded by a given maximum. We derive the covariance of the observed variables and introduce marginal quasi-equivalence, which...

Sadegh Khorasani, Ali Najar, Saber Salehkaleybar et al. · 0 citations

#machine learning Preprint Sep 2026

One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady State

We study the problem of recovering the parameters of a multivariate Ornstein-Uhlenbeck (OU) process from steady-state observational and interventional data. In many applications, such as large-scale gene perturbation experiments, only stationary"snapshot"measurements are available, making standard stochastic differenti...

Saber Salehkaleybar · 1 citation · ⚡1