Automatic monotonicity detection for Gaussian Processes

Eero Siivola, Juho Piironen, Aki Vehtari

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publicationSubmitted
Publication statusUnpublished - 2016
MoE publication typeA4 Article in a conference publication

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