Linearly Constrained Gaussian Processes are SkewGPs: application to Monotonic Preference Learning and Desirability

@InProceedings{pmlr-v244-benavoli24a,
  title = 	 {Linearly Constrained Gaussian Processes are SkewGPs: application to Monotonic Preference Learning and Desirability},
  author =       {Benavoli, Alessio and Azzimonti, Dario},
  booktitle = 	 {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {333--348},
  year = 	 {2024},
  editor = 	 {Kiyavash, Negar and Mooij, Joris M.},
  volume = 	 {244},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {15--19 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v244/main/assets/benavoli24a/benavoli24a.pdf},
  url = 	 {https://proceedings.mlr.press/v244/benavoli24a.html},
  abstract = 	 {We show that existing approaches to Linearly Constrained Gaussian Processes (LCGP) for regression, based on imposing constraints on a finite set of operational points, can be seen as Skew Gaussian Processes (SkewGPs). In particular, focusing on inequality constraints and building upon a recent unification of regression, classification, and preference learning through SkewGPs, we extend LCGP to handle monotonic preference learning and desirability, crucial for  understanding and predicting human decision making.  We demonstrate the efficacy of the proposed model on simulated and real data.}
}