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arXiv Machine Learning By Olivier Truffinet (CEA Saclay), Karim Ammar (CEA Saclay), Jean-Philippe Argaud (EDF R&D), Bertrand Bouriquet (EDF)

Exact and general decoupled solutions of the LMC Multitask Gaussian Process model

Read the original on arXiv Machine Learning →

The paper presents an exact, efficient solution for the Linear Model of Co‑regionalization (LMC) multitask Gaussian Process by decoupling latent processes under a mild noise‑model assumption. It introduces a full parametrization of the resulting projected LMC, enabling linear‑time optimization and simplifying tasks such as training updates and leave‑one‑out cross‑validation. Experiments on synthetic and real data demonstrate that projected LMC is competitive with state‑of‑the‑art multitask GP models while offering greater interpretability and computational ease.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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