mlsanityData to 7 Oct 2026

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

arXiv 1302.4922 ↗First posted 20 Feb 2013stat.ML cs.LG stat.MEcited by 86

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Abstract (arXiv)

Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art. We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels. We present a method for searching over this space of structures which mirrors the scientific discovery process. The learned structures can often decompose functions into interpretable components and enable long-range extrapolation on time-series datasets. Our structure search method outperforms many widely used kernels and kernel combination methods on a variety of prediction tasks.

Key facts. Cited by 86 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2013.

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Citing papers per year: 2021: 22; 2022: 15; 2023: 12; 2024: 10; 2025: 14; 2026: 13.

86 papers.

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  2. 2021Priors in Bayesian Deep Learning: A Reviewcited by 101
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  11. 2025Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMscited by 15
  12. 2022Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equationscited by 14
38 more
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  5. 2022Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transportcited by 8
  6. 2022Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classificationcited by 7
  7. 2025ActiveInitSplat: How Active Image Selection Helps Gaussian Splattingcited by 7
  8. 2021Measuring the robustness of Gaussian processes to kernel choicecited by 6
  9. 2022Compositional optimization of quantum circuits for quantum kernels of support vector machinescited by 6
  10. 2023Biological Sequence Kernels with Guaranteed Flexibilitycited by 6
  11. 2024Boundary Exploration for Bayesian Optimization With Unknown Physical Constraintscited by 6
  12. 2026BOute: Cost-Efficient LLM Serving with Heterogeneous LLMs and GPUs via Multi-Objective Bayesian Optimizationcited by 6
  13. 2021Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Surveycited by 5
  14. 2022Gaussian Process Surrogate Models for Neural Networkscited by 5
  15. 2023Sequential Monte Carlo Learning for Time Series Structure Discoverycited by 5
  16. 2023Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processescited by 5
  17. 2024Dynamic Online Ensembles of Basis Expansionscited by 5
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  21. 2023Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systemscited by 4
  22. 2023A Bayesian Take on Gaussian Process Networkscited by 4
  23. 2024Multi-fidelity Gaussian process surrogate modeling for regression problems in physicscited by 4
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  26. 2021The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Timecited by 3
  27. 2021Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interfacecited by 3
  28. 2022Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localizationcited by 3
  29. 2022Periodic Extrapolative Generalisation in Neural Networkscited by 3
  30. 2022Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecastingcited by 3
  31. 2025Automated Model Discovery via Multi-modal & Multi-step Pipelinecited by 3
  32. 2021MetaKernel: Learning Variational Random Features with Limited Labelscited by 2
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  35. 2023On the Identifiability and Interpretability of Gaussian Process Modelscited by 2
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  38. 2026Structure Detection for Contextual Reinforcement Learningcited by 2

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