On Negative Transfer and Structure of Latent Functions in Multioutput Gaussian Processes
نویسندگان
چکیده
The multi-output Gaussian process ($\mathcal{MGP}$) is based on the assumption that outputs share commonalities, however, if this does not hold negative transfer will lead to decreased performance relative learning independently or in subsets. In article, we first define context of an $\mathcal{MGP}$ and then derive necessary conditions for model avoid transfer. Specifically, under convolution construction, show avoiding mainly dependent having a sufficient number latent functions $Q$ regardless flexibility kernel inference procedure used. However, slight increase leads large parameters be estimated. To end, propose two structures scale arbitrarily datasets, can allow any sparse approximations used within. These also regularization which provide consistent automatic selection related outputs.
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ژورنال
عنوان ژورنال: SIAM/ASA Journal on Uncertainty Quantification
سال: 2022
ISSN: ['2166-2525']
DOI: https://doi.org/10.1137/21m1436816