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…
cs.LG2026
Structured Coupling for Flow Matching
Xavier Sumba, Carles Balsells-Rodas, Yingzhen Li
Standard flow matching scales well but typically relies on an unstructured source distribution, limiting its ability to learn interpretable latent structure. Latent-variable models…
stat.ML2026
On the Identifiability of Regime-Switching Models with Multi-Lag Dependencies
Carles Balsells-Rodas, Toshiko Matsui, Pedro A. M. Mediano +2
Identifiability is central to the interpretability of deep latent variable models, ensuring parameterisations are uniquely determined by the data-generating distribution. However,…