Deep Huber quantile regression networks

Understand

Typical machine learning regression applications aim to report the mean or the median of the predictive probability distribution, via training with a squared or an absolute error scoring function.

• The importance of issuing predictions of more functionals of the predictive probability distribution (quantiles and expectiles) has been recognized as a means to quantify the uncertainty of the prediction.
• In deep learning (DL) applications, that is possible through quantile and expectile regression neural networks (QRNN and ERNN respectively).
• Here we introduce deep Huber quantile regression networks (DHQRN) that nest QRNN and ERNN as edge cases.