Variational Inference for Gaussian Process Models with Linear Complexity

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Variational Inference for Gaussian Process Models with Linear Complexity

28 November 2017

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Papers citing "Variational Inference for Gaussian Process Models with Linear Complexity"

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Title
Variational Nearest Neighbor Gaussian Process Luhuan Wu Geoff Pleiss John P. Cunningham BDL 94 15 0 03 Feb 2022
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel Yindong Chen Yiwei Wang Lulu Kang Chun Liu 129 2 0 21 Nov 2021
Dual Parameterization of Sparse Variational Gaussian Processes Vincent Adam Paul E. Chang Mohammad Emtiyaz Khan Arno Solin 89 23 0 05 Nov 2021
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices Jan Povala Ieva Kazlauskaite Eky Febrianto F. Cirak Mark Girolami 91 23 0 22 Oct 2021
Latent structure blockmodels for Bayesian spectral graph clustering Francesco Sanna Passino N. Heard 62 1 0 04 Jul 2021
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data Jens Petersen Gregor Koehler David Zimmerer Fabian Isensee Paul F. Jäger Klaus H. Maier-Hein BDL AI4TS 71 3 0 09 Jun 2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients A. Artemev David R. Burt Mark van der Wilk 74 19 0 16 Feb 2021
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 102 61 0 08 Nov 2020
Scalable Gaussian Process Variational Autoencoders Metod Jazbec Matthew Ashman Vincent Fortuin Michael Pearce Stephan Mandt Gunnar Rätsch DRL BDL 89 29 0 26 Oct 2020
Stein Variational Gaussian Processes Thomas Pinder Christopher Nemeth David Leslie BDL 37 7 0 25 Sep 2020
Prediction with Approximated Gaussian Process Dynamical Models Thomas Beckers Sandra Hirche AI4CE 62 19 0 25 Jun 2020
Efficiently Sampling Functions from Gaussian Process Posteriors James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 81 165 0 21 Feb 2020
Deep Sigma Point Processes M. Jankowiak Geoff Pleiss Jacob R. Gardner BDL 67 22 0 21 Feb 2020
Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods Haitao Liu Yew-Soon Ong Ziwei Yu Jianfei Cai Xiaobo Shen 52 3 0 14 Sep 2019
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak Jacob R. Gardner UQCV BDL 56 3 0 31 May 2019
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 62 230 0 19 Mar 2019
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature Franz J. Király Bilal A. Mateen R. Sonabend 95 10 0 18 Dec 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 133 697 0 03 Jul 2018
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo Marton Havasi José Miguel Hernández-Lobato J. J. Murillo-Fuentes BDL 62 97 0 14 Jun 2018