When are Iterative Gaussian Processes Reliably Accurate?

When are Iterative Gaussian Processes Reliably Accurate?

31 December 2021

Wesley J. Maddox

Sanyam Kapoor

Papers citing "When are Iterative Gaussian Processes Reliably Accurate?"

10 / 10 papers shown

Title
High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation Chris Camaño Daniel Huang BDL GP 23 1 0 28 Oct 2024
Review of Recent Advances in Gaussian Process Regression Methods Chenyi Lyu Xingchi Liu Lyudmila Mihaylova GP 21 3 0 12 Sep 2024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec Javier Antorán José Miguel Hernández-Lobato 33 2 0 28 May 2024
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data Tim Gyger Reinhard Furrer Fabio Sigrist 19 2 0 23 May 2024
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression Lu Zou Liang Ding 29 0 0 20 Mar 2024
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes Sebastian W. Ober A. Artemev Marcel Wagenlander Rudolfs Grobins Mark van der Wilk GP 11 1 0 15 Feb 2024
Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models Pascal Kündig Fabio Sigrist 13 2 0 18 Oct 2023
Low-Precision Arithmetic for Fast Gaussian Processes Wesley J. Maddox Andres Potapczynski A. Wilson 12 12 0 14 Jul 2022
Parallel MCMC Without Embarrassing Failures Daniel Augusto R. M. A. de Souza Diego Mesquita Samuel Kaski Luigi Acerbi 36 11 0 22 Feb 2022
Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization Wesley J. Maddox Qing Feng Maximilian Balandat 17 7 0 29 Nov 2021