Variational Inference for Gaussian Process Models with Linear Complexity

28 November 2017

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

11 / 11 papers shown

Title
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel Yindong Chen Yiwei Wang Lulu Kang Chun Liu 26 1 0 21 Nov 2021
Dual Parameterization of Sparse Variational Gaussian Processes Vincent Adam Paul E. Chang Mohammad Emtiyaz Khan Arno Solin 24 20 0 05 Nov 2021
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices Jan Povala Ieva Kazlauskaite Eky Febrianto F. Cirak Mark Girolami 32 22 0 22 Oct 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 18 58 0 08 Nov 2020
Scalable Gaussian Process Variational Autoencoders Metod Jazbec Matthew Ashman Vincent Fortuin Michael Pearce Stephan Mandt Gunnar Rätsch DRL BDL 26 25 0 26 Oct 2020
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks Sujay Thakur Cooper Lorsung Yaniv Yacoby Finale Doshi-Velez Weiwei Pan BDL UQCV 33 4 0 21 Jun 2020
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak Jacob R. Gardner UQCV BDL 29 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 12 226 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 20 10 0 18 Dec 2018
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo Marton Havasi José Miguel Hernández-Lobato J. J. Murillo-Fuentes BDL 19 96 0 14 Jun 2018