GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models
This addresses a bottleneck in multi-output Gaussian process modeling for researchers and practitioners, offering an automated solution to reduce manual effort and bias, though it appears incremental as it builds on existing latent process frameworks.
The paper tackles the problem of manually selecting the number and kernels of latent processes in multi-output Gaussian process models, which is time-consuming and biased, by proposing GP-ALPS to automatically choose latent processes by turning off irrelevant ones, with preliminary experiments demonstrating its suitability.
A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved latent GPs. An issue with this approach is choosing the number of latent processes and their kernels. These choices are typically done manually, which can be time consuming and prone to human biases. We propose Gaussian Process Automatic Latent Process Selection (GP-ALPS), which automatically chooses the latent processes by turning off those that do not meaningfully contribute to explaining the data. We develop a variational inference scheme, assess the quality of the variational posterior by comparing it against the gold standard MCMC, and demonstrate the suitability of GP-ALPS in a set of preliminary experiments.