7 papers
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…
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…
End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
Carles Balsells-Rodas, Zhengrui Xiang, Xavier Sumba +1
Learning identifiable representations in deep generative models remains a fundamental challenge, particularly for sequential data with regime-switching dynamics. Existing approache…
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,…
Causal Discovery from Conditionally Stationary Time Series
Carles Balsells-Rodas, Xavier Sumba, Tanmayee Narendra +4
Causal discovery, i.e., inferring underlying causal relationships from observational data, is highly challenging for AI systems. In a time series modeling context, traditional caus…
Identifying Nonstationary Causal Structures with High-Order Markov Switching Models
Carles Balsells-Rodas, Yixin Wang, Pedro A. M. Mediano +1
Causal discovery in time series is a rapidly evolving field with a wide variety of applications in other areas such as climate science and neuroscience. Traditional approaches assu…