mlsanityData to 14 Oct 2026

Deep Huber quantile regression networks

arXiv 2306.10306 ↗First posted 17 Jun 2023stat.ML cs.LG stat.APcited by 3

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Abstract (arXiv)

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. DHQRN can predict Huber quantiles, which are more general functionals in the sense that they nest quantiles and expectiles as limiting cases. The main idea is to train a DL algorithm with the Huber quantile scoring function, which is consistent for the Huber quantile functional. As a proof of concept, DHQRN are applied to predict house prices in Melbourne, Australia and Boston, United States (US). In this context, predictive performances of three DL architectures are discussed along with evidential interpretation of results from two economic case studies. Additional simulation experiments and applications to real-world case studies using open datasets demonstrate a satisfactory absolute performance of DHQRN.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 109 references that are in the map. First posted on arXiv 17 Jun 2023.

Builds on

14 of 109 references.

References · 14

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2 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learningcited by 2
  2. 2025Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognosticscited by 1
  3. 2025Variable transformations in consistent loss functionscited by 0