Generalized Multi-Output Gaussian Process Censored Regression. (arXiv:2009.04822v2 [stat.ML] UPDATED)

When modelling censored observations, a typical approach in current
regression methods is to use a censored-Gaussian (i.e. Tobit) model to describe
the conditional output distribution. In this paper, as in the case of missing
data, we argue that exploiting correlations between multiple outputs can enable
models to better address the bias introduced by censored data. To do so, we
introduce a heteroscedastic multi-output Gaussian process model which combines
the non-parametric flexibility of GPs with the ability to leverage information
from …

arxiv ml process regression