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

Alessio Benavoli, Dario Azzimonti
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, PMLR 244:333-348, 2024.

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.

Cite this Paper

BibTeX
@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.} }
Endnote
%0 Conference Paper %T Linearly Constrained Gaussian Processes are SkewGPs: application to Monotonic Preference Learning and Desirability %A Alessio Benavoli %A Dario Azzimonti %B Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2024 %E Negar Kiyavash %E Joris M. Mooij %F pmlr-v244-benavoli24a %I PMLR %P 333--348 %U https://proceedings.mlr.press/v244/benavoli24a.html %V 244 %X 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.
APA
Benavoli, A. & Azzimonti, D.. (2024). Linearly Constrained Gaussian Processes are SkewGPs: application to Monotonic Preference Learning and Desirability. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 244:333-348 Available from https://proceedings.mlr.press/v244/benavoli24a.html.

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