mlsanityData to 14 Oct 2026

Constant-Time Predictive Distributions for Gaussian Processes

arXiv 1803.06058 ↗First posted 16 Mar 2018cs.LG stat.MLcited by 48

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Abstract (arXiv)

One of the most compelling features of Gaussian process (GP) regression is its ability to provide well-calibrated posterior distributions. Recent advances in inducing point methods have sped up GP marginal likelihood and posterior mean computations, leaving posterior covariance estimation and sampling as the remaining computational bottlenecks. In this paper we address these shortcomings by using the Lanczos algorithm to rapidly approximate the predictive covariance matrix. Our approach, which we refer to as LOVE (LanczOs Variance Estimates), substantially improves time and space complexity. In our experiments, LOVE computes covariances up to 2,000 times faster and draws samples 18,000 times faster than existing methods, all without sacrificing accuracy.

Key facts. Cited by 48 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Mar 2018.

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Citing papers per year: 2021: 13; 2022: 6; 2023: 5; 2024: 10; 2025: 9; 2026: 5.

48 papers.

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  1. 2021Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Informationcited by 33
  2. 2021Fast Adaptation with Linearized Neural Networkscited by 32
  3. 2021Bayesian Optimization with High-Dimensional Outputscited by 32
  4. 2022Posterior and Computational Uncertainty in Gaussian Processescited by 24
  5. 2021Preconditioning for Scalable Gaussian Process Hyperparameter Optimizationcited by 23
  6. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
  7. 2021Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradientscited by 20
  8. 2021Kernel Interpolation for Scalable Online Gaussian Processescited by 18
  9. 2021Bias-Free Scalable Gaussian Processes via Randomized Truncationscited by 16
  10. 2021Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processescited by 12
  11. 2021When are Iterative Gaussian Processes Reliably Accurate?cited by 12
  12. 2023Large-Scale Gaussian Processes via Alternating Projectioncited by 11
36 more
  1. 2022Low-Precision Arithmetic for Fast Gaussian Processescited by 10
  2. 2023Tuning Legged Locomotion Controllers via Safe Bayesian Optimizationcited by 9
  3. 2024Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inferencecited by 9
  4. 2025Robust and Conjugate Spatio-Temporal Gaussian Processescited by 9
  5. 2023Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Modelscited by 6
  6. 2024Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscited by 6
  7. 2021Faster Kernel Interpolation for Gaussian Processescited by 5
  8. 2021Sensitivity Prewarping for Local Surrogate Modelingcited by 4
  9. 2024Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Datacited by 4
  10. 2023Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processescited by 3
  11. 2024Rate-Informed Discovery via Bayesian Adaptive Multifidelity Samplingcited by 3
  12. 2022Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic maskingcited by 2
  13. 2022Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)cited by 2
  14. 2024Scaling up Probabilistic PDE Simulators with Structured Volumetric Informationcited by 2
  15. 2024Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regressioncited by 2
  16. 2024AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sitescited by 2
  17. 2024Batched Energy-Entropy acquisition for Bayesian Optimizationcited by 2
  18. 2025HiGP: A high-performance Python package for Gaussian Processcited by 2
  19. 2022Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variablescited by 1
  20. 2024Gradients of Functions of Large Matricescited by 1
  21. 2025nuGPR: GPU-Accelerated Gaussian Process Regression with Iterative Algorithms and Low-Rank Approximationscited by 1
  22. 2026Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimizationcited by 1
  23. 2021Barely Biased Learning for Gaussian Process Regressioncited by 0
  24. 2022GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Controlcited by 0
  25. 2023Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regressioncited by 0
  26. 2024Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matricescited by 0
  27. 2025GPRat: Gaussian Process Regression with Asynchronous Taskscited by 0
  28. 2025Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effectscited by 0
  29. 2025Scalable Deep Basis Kernel Gaussian Processescited by 0
  30. 2025Taking the GP Out of the Loopcited by 0
  31. 2025Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processescited by 0
  32. 2025DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processescited by 0
  33. 2026Lightweight Gaussian Process Inference in C++ on Metal and CUDAcited by 0
  34. 2026SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learningcited by 0
  35. 2026A green solvent screening tool for emerging materials via uncertainty aware, transformer enhanced transfer learningcited by 0
  36. 2026Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimizationcited by 0