mlsanityData to 7 Oct 2026

Incorporating Sum Constraints into Multitask Gaussian Processes

arXiv 2202.01793 ↗First posted 3 Feb 2022stat.ML cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 44 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

15 of 44 references.

References · 15

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  6. 2017Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equationscited by 72
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  10. 2015Modeling and interpolation of the ambient magnetic field by Gaussian processescited by 28
  11. 2018Algorithmic Linearly Constrained Gaussian Processescited by 20
  12. 2020Linearly Constrained Neural Networkscited by 18
3 more
  1. 2016Gaussian process emulators for computer experiments with inequality constraintscited by 16
  2. 2020Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processescited by 13
  3. 2019Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Featurescited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2023Time-Varying Transition Matrices with Multi-task Gaussian Processescited by 0
  2. 2026Multi-output Gaussian process prediction of physical fields under linear equality constraintscited by 0