Incorporating Sum Constraints into Multitask Gaussian Processes

Philipp Pilar, Carl Jidling, Thomas B. Schön, Niklas Wahlström

Abstract: Machine learning models can be improved by adapting them to respect existing background knowledge. In this paper we consider multitask Gaussian processes, with background knowledge in the form of constraints that require a specific sum of the outputs to be constant. This is achieved by conditioning the prior distribution on the constraint fulfillment. The approach allows for both linear and nonlinear constraints. We demonstrate that the constraints are fulfilled with high precision and that the construction can improve the overall prediction accuracy as compared to the standard Gaussian process.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission: We de-anonymized the paper and submitted the camera ready version.

Code: https://github.com/ppilar/SumConstraint

Assigned Action Editor: ~Thang_D_Bui1

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Submission Number: 328