arXiv Machine Learning July 21, 2026 By Ivan Lerner, Jean Feydy, Alexandre Kalimouttou, Anita Burgun, Francis Bach

Differentiable latent structure discovery for interpretable forecasting in clinical time series

Read the original on arXiv Machine Learning →

arXiv:2604. 27967v2 Announce Type: replace Abstract: Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.

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CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation

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GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care

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