Differentiable latent structure discovery for interpretable forecasting in clinical time series
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. We further propose LP-StructGP, which augments StructGP with latent pathways-shared, temporally shifted trajectories inferred via subject-specific coupling filters and a softmax gating mechanism-to capture cross-patient progression patterns. Both models are trained under sparsity and acyclicity constraints using scalable low-rank updates using likelihood-based objectives. Results: In simulations, graph recover...
| Authors | Ivan Lerner, Jean Feydy, Alexandre Kalimouttou et al. |
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| Primary category | cs.LG |
| Published | 30 April 2026 |
| Last updated at source | 20 July 2026 |
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