5 citations · 5 across the 5 of their papers we have counts for
5 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…
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,…
Markovian Gaussian Process Variational Autoencoders
Harrison Zhu, Carles Balsells Rodas, Yingzhen Li
Sequential VAEs have been successfully considered for many high-dimensional time series modelling problems, with many variant models relying on discrete-time mechanisms such as rec…
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…