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Calibrated Multiple-Output Quantile Regression with Representation Learning
[article]
2022
arXiv
pre-print
We develop a method to generate predictive regions that cover a multivariate response variable with a user-specified probability. Our work is composed of two components. First, we use a deep generative model to learn a representation of the response that has a unimodal distribution. Existing multiple-output quantile regression approaches are effective in such cases, so we apply them on the learned representation, and then transform the solution to the original space of the response. This
arXiv:2110.00816v2
fatcat:i2h7hliccnayjo44kju5i5vnyu