Hilbert Space Methods for Reduced-Rank Gaussian Process Regression

21 January 2014

Arno Solin

Simo Särkkä

Papers citing "Hilbert Space Methods for Reduced-Rank Gaussian Process Regression"

34 / 34 papers shown

Title
Geometry-aware Active Learning of Spatiotemporal Dynamic Systems Xizhuo Zhang AI4CE 26 0 0 26 Apr 2025
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference Jiayun Li Kay Pompetzki An T. Le Haolei Tong Jan Peters Georgia Chalvatzaki 33 0 0 07 Apr 2025
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems Zhidi Lin Ying Li Feng Yin Juan Maroñas Alexandre Thiéry 51 0 0 24 Mar 2025
When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquer Emily C. Hector Amanda Lenzi 41 1 0 31 Dec 2024
Optimizing Posterior Samples for Bayesian Optimization via Rootfinding Taiwo A. Adebiyi Bach Do Ruda Zhang 103 2 0 29 Oct 2024
Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine S. J. D. Rooij Frederiek Wesel B. Hunyadi AAML 21 0 0 01 Aug 2024
Dynamic Online Ensembles of Basis Expansions Daniel Waxman Petar M. Djurić 30 3 0 02 May 2024
Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression Clara Menzen Marnix Fetter Manon Kok 12 1 0 25 Oct 2023
Learning battery model parameter dynamics from data with recursive Gaussian process regression A. Aitio Dominik Jöst D. Sauer David A. Howey 9 5 0 26 Apr 2023
Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States Robert Lefringhausen Supitsana Srithasan Armin Lederer Sandra Hirche 20 4 0 31 Mar 2023
Generalised Linear Mixed Model Specification, Analysis, Fitting, and Optimal Design in R with the glmmr Packages S. Watson 15 3 0 22 Mar 2023
Gaussian Process-Gated Hierarchical Mixtures of Experts Yuhao Liu Marzieh Ajirak P. Djuric MoE 16 1 0 09 Feb 2023
Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functions Frida Viset R. Helmons Manon Kok 21 1 0 17 Oct 2022
Adjoint-aided inference of Gaussian process driven differential equations Paterne Gahungu Christopher W. Lanyon Mauricio A. Alvarez Engineer Bainomugisha M. Smith Richard D. Wilkinson 16 5 0 09 Feb 2022
Linear Time Kernel Matrix Approximation via Hyperspherical Harmonics J. Ryan Anil Damle 14 0 0 08 Feb 2022
When are Iterative Gaussian Processes Reliably Accurate? Wesley J. Maddox Sanyam Kapoor A. Wilson 11 10 0 31 Dec 2021
Stochastic Processes Under Linear Differential Constraints : Application to Gaussian Process Regression for the 3 Dimensional Free Space Wave Equation Iain Henderson P. Noble O. Roustant 14 1 0 23 Nov 2021
Geometry-Aware Hierarchical Bayesian Learning on Manifolds Yonghui Fan Yalin Wang 11 2 0 30 Oct 2021
GaussED: A Probabilistic Programming Language for Sequential Experimental Design Matthew A. Fisher Onur Teymur Chris J. Oates 27 1 0 15 Oct 2021
Efficient Fourier representations of families of Gaussian processes P. Greengard 31 3 0 28 Sep 2021
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions P. Greengard M. O’Neil 22 10 0 12 Aug 2021
Pathfinder: Parallel quasi-Newton variational inference Lu Zhang Bob Carpenter A. Gelman Aki Vehtari 41 40 0 09 Aug 2021
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 13 57 0 08 Nov 2020
Matérn Gaussian Processes on Graphs Viacheslav Borovitskiy I. Azangulov Alexander Terenin P. Mostowsky M. Deisenroth N. Durrande 13 78 0 29 Oct 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt C. Rasmussen Mark van der Wilk 11 68 0 01 Aug 2020
Sparse Gaussian Processes with Spherical Harmonic Features Vincent Dutordoir N. Durrande J. Hensman 13 54 0 30 Jun 2020
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming Gabriel Riutort-Mayol Paul-Christian Burkner Michael R. Andersen Arno Solin Aki Vehtari 21 68 0 23 Apr 2020
Linearly Constrained Neural Networks J. Hendriks Carl Jidling A. Wills Thomas B. Schon 8 33 0 05 Feb 2020
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes Cristian Guarnizo Lemus Mauricio A. Alvarez 11 15 0 18 May 2018
Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps Manon Kok Arno Solin 9 67 0 05 Apr 2018
Recursive nonlinear-system identification using latent variables Per Mattsson Dave Zachariah Petre Stoica 13 30 0 14 Jun 2016
Sharp analysis of low-rank kernel matrix approximations Francis R. Bach 75 277 0 09 Aug 2012
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,262 0 09 Jun 2012
A Framework for Evaluating Approximation Methods for Gaussian Process Regression Krzysztof Chalupka Christopher K. I. Williams Iain Murray GP 63 169 0 29 May 2012