Convergence of Sparse Variational Inference in Gaussian Processes Regression

1 August 2020

Papers citing "Convergence of Sparse Variational Inference in Gaussian Processes Regression"

18 / 18 papers shown

Title
STRIDE: Sparse Techniques for Regression in Deep Gaussian Processes Simon Urbainczyk Aretha L. Teckentrup Jonas Latz GP 27 0 0 16 May 2025
Low-rank computation of the posterior mean in Multi-Output Gaussian Processes Sebastian Esche Martin Stoll 43 0 0 30 Apr 2025
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes Yunyue Wei Vincent Zhuang Saraswati Soedarmadji Yanan Sui 251 0 0 31 Dec 2024
Memory-Based Dual Gaussian Processes for Sequential Learning Paul E. Chang Prakhar Verma S. T. John Arno Solin Mohammad Emtiyaz Khan GP 38 4 0 06 Jun 2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 43 25 0 24 Jan 2023
Uncertainty quantification for sparse spectral variational approximations in Gaussian process regression D. Nieman Botond Szabó Harry Van Zanten 47 5 0 21 Dec 2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees Alexander Terenin David R. Burt A. Artemev Seth Flaxman Mark van der Wilk C. Rasmussen Hong Ge 63 7 0 14 Oct 2022
Memory Safe Computations with XLA Compiler A. Artemev Tilman Roeder Mark van der Wilk 40 8 0 28 Jun 2022
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning Sattar Vakili Jonathan Scarlett Da-shan Shiu A. Bernacchia 38 18 0 08 Feb 2022
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression Giacomo Meanti Luigi Carratino Ernesto De Vito Lorenzo Rosasco 26 12 0 17 Jan 2022
Contraction rates for sparse variational approximations in Gaussian process regression D. Nieman Botond Szabó Harry Van Zanten 62 17 0 22 Sep 2021
Barely Biased Learning for Gaussian Process Regression David R. Burt A. Artemev Mark van der Wilk 21 0 0 20 Sep 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 26 107 0 24 Feb 2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients A. Artemev David R. Burt Mark van der Wilk 23 18 0 16 Feb 2021
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 20 58 0 08 Nov 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 31 60 0 17 May 2020
How Good are Low-Rank Approximations in Gaussian Process Regression? C. Daskalakis P. Dellaportas A. Panos 19 3 0 03 Apr 2020
Sharp analysis of low-rank kernel matrix approximations Francis R. Bach 94 281 0 09 Aug 2012