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Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees

2 November 2021

William J. Wilkinson

Simo Särkkä

Arno Solin

BDL

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Papers citing "Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees"

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Title
Dual Parameterization of Sparse Variational Gaussian Processes Vincent Adam Paul E. Chang Mohammad Emtiyaz Khan Arno Solin 75 23 0 05 Nov 2021
Spatio-Temporal Variational Gaussian Processes Oliver Hamelijnck William J. Wilkinson Niki A. Loppi Arno Solin Theodoros Damoulas AI4TS 62 35 0 02 Nov 2021
The Bayesian Learning Rule Mohammad Emtiyaz Khan Håvard Rue BDL 105 81 0 09 Jul 2021
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes Will Tebbutt Arno Solin Richard Turner 49 8 0 18 Jun 2021
Sparse Algorithms for Markovian Gaussian Processes William J. Wilkinson Arno Solin Vincent Adam 41 12 0 19 Mar 2021
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes William J. Wilkinson Paul E. Chang Michael Riis Andersen Arno Solin 42 13 0 12 Jul 2020
Fast Variational Learning in State-Space Gaussian Process Models Paul E. Chang William J. Wilkinson Mohammad Emtiyaz Khan Arno Solin BDL 44 24 0 09 Jul 2020
Handling the Positive-Definite Constraint in the Bayesian Learning Rule Wu Lin Mark Schmidt Mohammad Emtiyaz Khan BDL 77 36 0 24 Feb 2020
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures Wu Lin Mohammad Emtiyaz Khan Mark Schmidt 71 31 0 29 Oct 2019
Variational Bayes on Manifolds Minh-Ngoc Tran D. Nguyen Duy Nguyen 75 23 0 08 Aug 2019
Approximate Inference Turns Deep Networks into Gaussian Processes Mohammad Emtiyaz Khan Alexander Immer Ehsan Abedi M. Korzepa UQCV BDL 106 125 0 05 Jun 2019
Partitioned Variational Inference: A unified framework encompassing federated and continual learning T. Bui Cuong V Nguyen S. Swaroop Richard Turner FedML 69 56 0 27 Nov 2018
Gaussian process classification using posterior linearisation Á. F. García-Fernández Filip Tronarp Simo Särkkä 41 11 0 13 Sep 2018
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam Mohammad Emtiyaz Khan Didrik Nielsen Voot Tangkaratt Wu Lin Y. Gal Akash Srivastava ODL 154 272 0 13 Jun 2018
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models Hugh Salimbeni Stefanos Eleftheriadis J. Hensman BDL 73 86 0 24 Mar 2018
State Space Gaussian Processes with Non-Gaussian Likelihood H. Nickisch Arno Solin A. Grigorevskiy GP 40 32 0 13 Feb 2018
Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models Mohammad Emtiyaz Khan Wu Lin BDL 53 137 0 13 Mar 2017
Chained Gaussian Processes Alan D. Saul J. Hensman Aki Vehtari Neil D. Lawrence 40 59 0 18 Apr 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 287 4,807 0 04 Jan 2016
Stochastic Expectation Propagation Yingzhen Li Jose Miguel Hernandez-Lobato Richard Turner 136 115 0 12 Jun 2015
Probabilistic Numerics and Uncertainty in Computations Philipp Hennig Michael A. Osborne Mark Girolami 76 307 0 03 Jun 2015
Expectation Propagation in the large-data limit Guillaume P. Dehaene Simon Barthelmé 64 44 0 27 Mar 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.0K 150,312 0 22 Dec 2014
Scalable Variational Gaussian Process Classification J. Hensman A. G. Matthews Zoubin Ghahramani BDL 75 645 0 07 Nov 2014
Approximate Inference for Nonstationary Heteroscedastic Gaussian process Regression Ville Tolvanen Pasi Jylänki Aki Vehtari 61 61 0 22 Apr 2014
Quasi-Newton Methods: A New Direction Philipp Hennig Martin Kiefel 83 103 0 18 Jun 2012
Gaussian Process Regression with a Student-t Likelihood Pasi Jylänki J. Vanhatalo Aki Vehtari GP 102 165 0 22 Jun 2011
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference Matthias Seeger H. Nickisch 77 31 0 16 Dec 2010