Automatic monotonicity detection for Gaussian Processes

2016/10/18 by Eero Siivola, Siivola, Eero, Juho Piironen +3 · 1 citation
Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.1610.05440

openalex publication_date 2016/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a new method for automatically detecting monotonic input-output relationships from data using Gaussian Process (GP) models with virtual derivative observations. Our results on synthetic and real datasets show that the proposed method detects monotonic directions from input spaces with high accuracy. We expect the method to be useful especially for improving explainability of the models and improving the accuracy of regression and classification tasks, especially near the edges of the data or when extrapolating.

Citations

• Rejoinder to "Least angle regression" by Efron et al

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