Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models

24 March 2018

Hugh Salimbeni

Stefanos Eleftheriadis

J. Hensman

BDL

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Papers citing "Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models"

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Title
Spectral-factorized Positive-definite Curvature Learning for NN Training Wu Lin Felix Dangel Runa Eschenhagen Juhan Bae Richard E. Turner Roger B. Grosse 47 0 0 10 Feb 2025
Function-Space Regularization for Deep Bayesian Classification J. Lin Joe Watson Pascal Klink Jan Peters UQCV BDL 41 1 0 12 Jul 2023
DPVIm: Differentially Private Variational Inference Improved Joonas Jälkö Lukas Prediger Antti Honkela Samuel Kaski 34 3 0 28 Oct 2022
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models Oleg Arenz Philipp Dahlinger Zihan Ye Michael Volpp Gerhard Neumann 39 15 0 23 Sep 2022
Fast emulation of density functional theory simulations using approximate Gaussian processes S. Stetzler M. Grosskopf E. Lawrence 23 0 0 24 Aug 2022
Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations Marvin Stuede Moritz Schappler 22 3 0 14 Mar 2022
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning Matthew Ashman T. Bui Cuong V Nguyen Efstratios Markou Adrian Weller S. Swaroop Richard Turner FedML 19 12 0 24 Feb 2022
Dual Parameterization of Sparse Variational Gaussian Processes Vincent Adam Paul E. Chang Mohammad Emtiyaz Khan Arno Solin 19 20 0 05 Nov 2021
Spatio-Temporal Variational Gaussian Processes Oliver Hamelijnck William J. Wilkinson Niki A. Loppi Arno Solin Theodoros Damoulas AI4TS 19 31 0 02 Nov 2021
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees William J. Wilkinson Simo Särkkä Arno Solin BDL 21 15 0 02 Nov 2021
The Bayesian Learning Rule Mohammad Emtiyaz Khan Håvard Rue BDL 63 73 0 09 Jul 2021
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors Liwei Wang Suraj Yerramilli Akshay Iyer D. Apley Ping Zhu Wei Chen 38 25 0 26 Jun 2021
Multivariate Probabilistic Regression with Natural Gradient Boosting Michael O’Malley A. Sykulski R. Lumpkin Alejandro Schuler BDL 17 7 0 07 Jun 2021
GPflux: A Library for Deep Gaussian Processes Vincent Dutordoir Hugh Salimbeni Eric Hambro John Mcleod Felix Leibfried A. Artemev Mark van der Wilk J. Hensman M. Deisenroth S. T. John GP 35 23 0 12 Apr 2021
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations Luhuan Wu Andrew C. Miller Lauren Anderson Geoff Pleiss David M. Blei John P. Cunningham 24 8 0 28 Feb 2021
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss M. Jankowiak David Eriksson Anil Damle Jacob R. Gardner 19 43 0 19 Jun 2020
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models Théo Galy-Fajou F. Wenzel Manfred Opper 29 4 0 26 Feb 2020
Handling the Positive-Definite Constraint in the Bayesian Learning Rule Wu Lin Mark W. Schmidt Mohammad Emtiyaz Khan BDL 37 35 0 24 Feb 2020
Doubly Sparse Variational Gaussian Processes Vincent Adam Stefanos Eleftheriadis N. Durrande A. Artemev J. Hensman 24 24 0 15 Jan 2020
Partitioned Variational Inference: A unified framework encompassing federated and continual learning T. Bui Cuong V Nguyen S. Swaroop Richard Turner FedML 24 55 0 27 Nov 2018
Harmonizable mixture kernels with variational Fourier features Zheyan Shen Markus Heinonen Samuel Kaski 19 17 0 10 Oct 2018
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models Mohammad Emtiyaz Khan Didrik Nielsen BDL 34 62 0 12 Jul 2018