mlsanityData to 7 Oct 2026

Product Kernel Interpolation for Scalable Gaussian Processes

arXiv 1802.08903 ↗First posted 24 Feb 2018cs.LG stat.MLcited by 31

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Abstract (arXiv)

Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs). Structured Kernel Interpolation (SKI) exploits these techniques by deriving approximate kernels with very fast MVMs. Unfortunately, such strategies suffer badly from the curse of dimensionality. We develop a new technique for MVM based learning that exploits product kernel structure. We demonstrate that this technique is broadly applicable, resulting in linear rather than exponential runtime with dimension for SKI, as well as state-of-the-art asymptotic complexity for multi-task GPs.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 2; 2023: 7; 2024: 5; 2025: 7; 2026: 2.

31 papers.

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  1. 2021Neural Operator: Learning Maps Between Function Spacescited by 413
  2. 2021High-Dimensional Gaussian Process Inference with Derivativescited by 16
  3. 2021Scaling Gaussian Processes with Derivative Information Using Variational Inferencecited by 15
  4. 2023Toward Large Kernel Modelscited by 12
  5. 2022Scalable First-Order Bayesian Optimization via Structured Automatic Differentiationcited by 10
  6. 2021SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processescited by 9
  7. 2021Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processescited by 7
  8. 2023CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebracited by 7
  9. 2024DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networkscited by 7
  10. 2021Faster Kernel Interpolation for Gaussian Processescited by 5
  11. 2021Input Dependent Sparse Gaussian Processescited by 5
  12. 2023Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regressioncited by 5
19 more
  1. 2025Scalable Gaussian Processes with Latent Kronecker Structurecited by 4
  2. 2021GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Datacited by 3
  3. 2023Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processescited by 3
  4. 2025Amortized Linear-time Exact Shapley Value for Product-Kernel Methodscited by 3
  5. 2022B'ezier Gaussian Processes for Tall and Wide Datacited by 2
  6. 2023Kernel Interpolation with Sparse Gridscited by 2
  7. 2024Fast training of large kernel models with delayed projectionscited by 2
  8. 2025New Bounds for Sparse Variational Gaussian Processescited by 2
  9. 2024High-Dimensional Gaussian Process Regression with Soft Kernel Interpolationcited by 1
  10. 2025Scaling Gaussian Process Regression with Full Derivative Observationscited by 1
  11. 2025nuGPR: GPU-Accelerated Gaussian Process Regression with Iterative Algorithms and Low-Rank Approximationscited by 1
  12. 2023Time-Varying Transition Matrices with Multi-task Gaussian Processescited by 0
  13. 2023A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulationcited by 0
  14. 2024Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regressioncited by 0
  15. 2024Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processescited by 0
  16. 2025Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processescited by 0
  17. 2025Fast Gaussian process inference by exact Mat'ern kernel decompositioncited by 0
  18. 2026Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process Classificationcited by 0
  19. 2026Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matricescited by 0