mlsanityData to 14 Oct 2026

Additive Gaussian Processes Revisited

arXiv 2206.09861 ↗First posted 20 Jun 2022stat.ML cs.LGcited by 19

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Abstract (arXiv)

Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power. By using a Gaussian process with additive structure, complex responses can be modelled whilst retaining interpretability. Previous work showed that additive Gaussian process models require high-dimensional interaction terms. We propose the orthogonal additive kernel (OAK), which imposes an orthogonality constraint on the additive functions, enabling an identifiable, low-dimensional representation of the functional relationship. We connect the OAK kernel to functional ANOVA decomposition, and show improved convergence rates for sparse computation methods. With only a small number of additive low-dimensional terms, we demonstrate the OAK model achieves similar or better predictive performance compared to black-box models, while retaining interpretability.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 7 of its 34 references that are in the map. First posted on arXiv 20 Jun 2022.

Builds on

7 of 34 references.

References · 7

  1. 2015Variational Inference with Normalizing Flowscited by 2,170
  2. 2020TabTransformer: Tabular Data Modeling Using Contextual Embeddingscited by 409
  3. 2014Scalable Variational Gaussian Process Classificationcited by 228
  4. 2019Rates of Convergence for Sparse Variational Gaussian Process Regressioncited by 63
  5. 2020Sparse Gaussian Processes with Spherical Harmonic Featurescited by 43
  6. 2016Orthogonal Gaussian process modelscited by 10
  7. 2019lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal datacited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 3; 2025: 7; 2026: 2.

19 papers.

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  1. 2023Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?cited by 36
  2. 2023Improving Neural Additive Models with Bayesian Principlescited by 15
  3. 2023Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimizationcited by 9
  4. 2025High Dimensional Bayesian Optimization using Lasso Variable Selectioncited by 4
  5. 2023Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recoverycited by 3
  6. 2024Tensor Network-Constrained Kernel Machines as Gaussian Processescited by 3
  7. 2025Challenges in interpretability of additive modelscited by 3
  8. 2025Automated Model Discovery via Multi-modal & Multi-step Pipelinecited by 3
  9. 2023Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Riskcited by 2
  10. 2023Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regressioncited by 2
  11. 2023Beyond Intuition, a Framework for Applying GPs to Real-World Datacited by 1
  12. 2024Combining additivity and active subspaces for high-dimensional Gaussian process modelingcited by 1
7 more
  1. 2025Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimizationcited by 1
  2. 2026Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimizationcited by 1
  3. 2024Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regressioncited by 0
  4. 2025LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representationscited by 0
  5. 2025Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processescited by 0
  6. 2025On Thompson Sampling and Bilateral Uncertainty in Additive Bayesian Optimizationcited by 0
  7. 2026Meta Additive Model: Interpretable Sparse Learning With Auto Weightingcited by 0