3 papers
stat.ML2026
Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
Roel Hulsman, Carles Balsells-Rodas, Sara Magliacane
Temporal systems often exhibit non-stationary behaviour, such as seasonal climate variation or glucose fluctuations in patients with type-1 diabetes. One way to model non-stationar…
stat.ML2026
Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing
Danru Xu, Sébastien Lachapelle, Sara Magliacane
Causal representation learning (CRL) aims to identify the underlying latent variables from high-dimensional observations, even when variables are dependent with each other. We stud…
math.ST2025
Finite sample-optimal adjustment sets in linear Gaussian causal models
Nadja Rutsch, Sara Magliacane, Stéphanie van der Pas
Traditional covariate selection methods for causal inference focus on achieving unbiasedness and asymptotic efficiency. In many practical scenarios, researchers must estimate causa…