References for Computation-Aware GPs

Gaussian Processes And Approximations

  • C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. [link]
  • C. Williams and M. Seeger. Using the Nyström Method to Speed Up Kernel Machines. Advances in Neural Information Processing Systems, 13, 2000. [link]
  • M. Titsias. Variational Learning of Inducing Variables in Sparse Gaussian Processes. In Artificial Intelligence and Statistics, pages 567–574. PMLR, 2009. [link]
  • A. Wilson and H. Nickisch. Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP). In International Conference on Machine Learning, pages 1775–1784. PMLR, 2015. [link]
  • J. Gardner, G. Pleiss, K. Q. Weinberger, D. Bindel, and A. G. Wilson. GPy- Torch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. Advances in Neural Information Processing Systems, 31, 2018. [link]

Iterative & Probabilistic Linear Solvers

  • D. M. Young. Iterative Solution of Large Linear Systems. Academic Press, 1971. [link]
  • M. R. Hestenes, E. Stiefel, et al. Methods of Conjugate Gradients for Solving Linear Systems. Journal of Research of the National Bureau of Standards, 49(6):409–436, 1952. [link]
  • P. Hennig, M. A. Osborne, and H. P. Kersting. Probabilistic Numerics: Computation as Machine Learning. Cambridge University Press, 2022. [link]
  • C. J. Oates and T. J. Sullivan. A modern Retrospective on Probabilistic Numerics. Statistics and Computing, 29(6):1335–1351, 2019. [link]
  • J. Cockayne, C. J. Oates, I. C. F. Ipsen, and M. Girolami. A Bayesian Conjugate Gradient Method (with Discussion). Bayesian Analysis, 14(3):937–1012, 2019. ISSN 1936-0975, 1931-6690. [link]
  • J. Cockayne, I. C. Ipsen, C. J. Oates, and T. W. Reid. Probabilistic Iterative Methods for Linear Systems. Journal of Machine Learning Research, 22(232):1–34, 2021. [link]
  • J. Cockayne, M. M. Graham, C. J. Oates, T. J. Sullivan, and O. Teymur. Testing Whether a Learning Procedure is Calibrated. Journal of Machine Learning Research, 23(203):1–36, 2022. [link]
  • D. Hegde, M. Pförtner, and J. Cockayne. Affine Tracing: A New Paradigm for Probabilistic Linear Solvers. arXiv Preprint arXiv:2605.10566, 2026. [link]

Computation-Aware Gaussian Processes

  • J. Wenger, G. Pleiss, M. Pförtner, P. Hennig, and J. P. Cunningham. Posterior and Computational Uncertainty in Gaussian Processes. Advances in Neural Information Processing Systems, 35:10876–10890, 2022. [link]
  • D. Hegde, M. Adil, and J. Cockayne. Calibrated Computation-Aware Gaussian Processes. In International Conference on Artificial Intelligence and Statistics, pages 2098–2106. PMLR, 2025. [link]
  • J. Wenger, K. Wu, P. Hennig, J. R. Gardner, G. Pleiss, and J. P. Cunningham. Computation-Aware Gaussian Processes: Model Selection and Linear-Time Inference. Advances in Neural Information Processing Systems, 37:3131631349, 2024. [link]
  • M. Sinaga, J. Martinelli, and S. Kaski. Robust and Computation-Aware Gaussian Processes. Advances in Neural Information Processing Systems, 38:648683, 2025. [link]
  • W. Laplante, M. Altamirano, A. B. Duncan, J. Knoblauch, and F.-X. Briol. Robust and Conjugate Spatio-Temporal Gaussian Processes. In International Conference on Machine Learning, pages 32562–32592. PMLR, 2025. [link]
  • N. Vyas, D. Hegde, and J. Cockayne. Randomised Postiterations for Calibrated BayesCG. In First International Conference on Probabilistic Numerics, pages 75–83. PMLR, 2025. [link]
  • M. Pförtner, J. Wenger, J. Cockayne, and P. Hennig. Computation-Aware Kalman Filtering and Smoothing. In International Conference on Artificial Intelligence and Statistics, pages 2071–2079. PMLR, 2025. [link]
  • J. Huml, J. Wenger, and J. P. Cunningham. Computation-Aware Kalman Filtering with Model Selection for Neural Dynamics. In Second International Conference on Probabilistic Numerics, 2026. [link]
  • L. Tatzel, J. Wenger, F. Schneider, and P. Hennig. Accelerating Non-Conjugate Gaussian Processes by Trading Off Computation for Uncertainty. Transactions on Machine Learning Research, 2025. [link]
  • D. Hegde and J. Cockayne. Learning to Solve Related Linear Systems. In First International Conference on Probabilistic Numerics, pages 103–121. PMLR, 2025. [link]