AI RESEARCH
Transformed Latent Variable Multi-Output Gaussian Processes
arXiv CS.LG
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ArXi:2605.05133v1 Announce Type: new Multi-Output Gaussian Processes (MOGPs) provide a principled probabilistic framework for modelling correlated outputs but face scalability bottlenecks when applied to datasets with high-dimensional output spaces. To maintain tractability, existing methods typically resort to restrictive assumptions, such as employing low-rank or sum-of-separable kernels, which can limit expressiveness.