An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing
J. Decke, A. Jenß, B. Sick, and C. Gruhl. International Conference on Architecture of Computing Systems (ARCS), Springer, (2024)(accepted).
Abstract
This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.
%0 Conference Paper
%1 decke2024efficient
%A Decke, Jens
%A Jenß, Arne
%A Sick, Bernhard
%A Gruhl, Christian
%B International Conference on Architecture of Computing Systems (ARCS)
%D 2024
%I Springer
%K imported itegpub isac-www QuantileRegression QuantileCrossing OrganicComputing Self-Awareness DifferentiableSorting
%T An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing
%X This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.
@inproceedings{decke2024efficient,
abstract = {This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.},
added-at = {2024-04-11T14:53:25.000+0200},
author = {Decke, Jens and Jenß, Arne and Sick, Bernhard and Gruhl, Christian},
biburl = {https://www.bibsonomy.org/bibtex/2e069d535438db13d7d37fd4dc45e9425/ies},
booktitle = {International Conference on Architecture of Computing Systems (ARCS)},
interhash = {ceb6a3a602af9ddff882d088aad22e17},
intrahash = {e069d535438db13d7d37fd4dc45e9425},
keywords = {imported itegpub isac-www QuantileRegression QuantileCrossing OrganicComputing Self-Awareness DifferentiableSorting},
note = {(accepted)},
publisher = {Springer},
timestamp = {2024-04-11T14:53:25.000+0200},
title = {An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing},
year = 2024
}