Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains. (arXiv:2202.12636v2 [stat.ML] UPDATED)

Web: http://arxiv.org/abs/2202.12636

Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian
model for learning correlated tasks effectively by transferring knowledge
across tasks. But current MTGPs are usually limited to the multi-task scenario
defined in the same input domain, leaving no space for tackling the
heterogeneous case, i.e., the features of input domains vary over tasks. To
this end, this paper presents a novel heterogeneous stochastic variational
linear model of coregionalization (\texttt{HSVLMC}) model for simultaneously
learning the tasks with varied input domains. Particularly, …

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