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

Alessio Benavoli, Dario Azzimonti

Keywords: linearly constrained, inequality, Gaussian Process, Skew Gaussian Process, preference, monotonicity, desiderability

TL;DR: We show that linearly Constrained Gaussian Processes are SkewGPs and we apply them to Monotonic Preference Learning and Desirability

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.

List Of Authors: Benavoli, Alessio and Azzimonti, Dario

Submission Number: 487