On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes

27 April 2015

Papers citing "On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes"

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Title
A Tutorial on Sparse Gaussian Processes and Variational Inference Felix Leibfried Vincent Dutordoir S. T. John N. Durrande GP 42 49 0 27 Dec 2020
Bayesian Graph Neural Networks for Molecular Property Prediction George Lamb Brooks Paige 31 12 0 25 Nov 2020
Understanding Variational Inference in Function-Space David R. Burt Sebastian W. Ober Adrià Garriga-Alonso Mark van der Wilk BDL 14 41 0 18 Nov 2020
Probabilistic selection of inducing points in sparse Gaussian processes Anders Kirk Uhrenholt V. Charvet B. S. Jensen 11 12 0 19 Oct 2020
Recyclable Gaussian Processes P. Moreno-Muñoz Antonio Artés-Rodríguez Mauricio A. Alvarez BDL 12 1 0 06 Oct 2020
A statistical theory of cold posteriors in deep neural networks Laurence Aitchison UQCV BDL 17 69 0 13 Aug 2020
Stochastic Bayesian Neural Networks Abhinav Sagar BDL UQCV 17 0 0 12 Aug 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt C. Rasmussen Mark van der Wilk 29 69 0 01 Aug 2020
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes Jake C. Snell R. Zemel 33 63 0 20 Jul 2020
Probabilistic Active Meta-Learning Jean Kaddour Steindór Sæmundsson M. Deisenroth 27 34 0 17 Jul 2020
Orthogonally Decoupled Variational Fourier Features Dario Azzimonti Manuel Schürch A. Benavoli Marco Zaffalon 8 0 0 13 Jul 2020
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes Andrew Y. K. Foong W. Bruinsma Jonathan Gordon Yann Dubois James Requeima Richard Turner BDL 14 77 0 02 Jul 2020
Prediction with Approximated Gaussian Process Dynamical Models Thomas Beckers Sandra Hirche AI4CE 14 18 0 25 Jun 2020
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation Victor Picheny Vincent Dutordoir A. Artemev N. Durrande 11 2 0 25 Jun 2020
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes Manuel Haussmann S. Gerwinn Andreas Look Barbara Rakitsch M. Kandemir 25 16 0 17 Jun 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 26 60 0 17 May 2020
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations Simone Rossi Markus Heinonen Edwin V. Bonilla Zheyan Shen Maurizio Filippone UQCV BDL 16 0 0 06 Mar 2020
A Framework for Interdomain and Multioutput Gaussian Processes Mark van der Wilk Vincent Dutordoir S. T. John A. Artemev Vincent Adam J. Hensman 40 94 0 02 Mar 2020
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights Theofanis Karaletsos T. Bui BDL 20 23 0 10 Feb 2020
Doubly Sparse Variational Gaussian Processes Vincent Adam Stefanos Eleftheriadis N. Durrande A. Artemev J. Hensman 24 24 0 15 Jan 2020
CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models Jones Yirui Liu Xinghao Qiao Jessica Lam TPM 14 3 0 13 Jan 2020
Continual Multi-task Gaussian Processes P. Moreno-Muñoz A. Artés-Rodríguez Mauricio A. Alvarez 6 13 0 31 Oct 2019
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison Yi Huang I. Chattopadhyay AI4TS 11 1 0 26 Sep 2019
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes Creighton Heaukulani Mark van der Wilk BDL 32 15 0 22 Jun 2019
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances Csaba Tóth Harald Oberhauser 11 9 0 19 Jun 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models Alessandro Davide Ialongo Mark van der Wilk J. Hensman C. Rasmussen 33 30 0 13 Jun 2019
Deep Gaussian Processes with Importance-Weighted Variational Inference Hugh Salimbeni Vincent Dutordoir J. Hensman M. Deisenroth BDL 23 43 0 14 May 2019
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features Arno Solin Manon Kok 10 23 0 10 Apr 2019
Robust Deep Gaussian Processes Jeremias Knoblauch GP 22 17 0 04 Apr 2019
Generalized Variational Inference: Three arguments for deriving new Posteriors Jeremias Knoblauch Jack Jewson Theodoros Damoulas DRL BDL 39 105 0 03 Apr 2019
Functional Variational Bayesian Neural Networks Shengyang Sun Guodong Zhang Jiaxin Shi Roger C. Grosse BDL 22 235 0 14 Mar 2019
Rates of Convergence for Sparse Variational Gaussian Process Regression David R. Burt C. Rasmussen Mark van der Wilk 26 151 0 08 Mar 2019
Bayesian Image Classification with Deep Convolutional Gaussian Processes Vincent Dutordoir Mark van der Wilk A. Artemev J. Hensman UQCV BDL 24 32 0 15 Feb 2019
Gaussian processes with linear operator inequality constraints C. Agrell 10 38 0 10 Jan 2019
Non-Factorised Variational Inference in Dynamical Systems Alessandro Davide Ialongo Mark van der Wilk J. Hensman C. Rasmussen 19 6 0 14 Dec 2018
Closed-form Inference and Prediction in Gaussian Process State-Space Models Alessandro Davide Ialongo Mark van der Wilk C. Rasmussen 24 8 0 10 Dec 2018
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
Understanding and Comparing Scalable Gaussian Process Regression for Big Data Haitao Liu Jianfei Cai Yew-Soon Ong Yi Wang 27 24 0 03 Nov 2018
Gaussian Process Conditional Density Estimation Vincent Dutordoir Hugh Salimbeni M. Deisenroth J. Hensman 11 52 0 30 Oct 2018
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds David Reeb Andreas Doerr S. Gerwinn Barbara Rakitsch GP 11 33 0 29 Oct 2018
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models Kaspar Märtens Kieran R. Campbell C. Yau 12 0 0 16 Oct 2018
Orthogonally Decoupled Variational Gaussian Processes Hugh Salimbeni Ching-An Cheng Byron Boots M. Deisenroth 19 43 0 24 Sep 2018
Learning Invariances using the Marginal Likelihood Mark van der Wilk Matthias Bauer S. T. John J. Hensman 36 83 0 16 Aug 2018
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes Christian Donner Manfred Opper 35 35 0 02 Aug 2018
A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation Behnoosh Parsa K. Rajasekaran Franziska Meier A. Banerjee 17 5 0 11 Jul 2018
Fully Scalable Gaussian Processes using Subspace Inducing Inputs A. Panos P. Dellaportas Michalis K. Titsias 19 11 0 06 Jul 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 16 681 0 03 Jul 2018
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees Jonathan H. Huggins Trevor Campbell Mikolaj Kasprzak Tamara Broderick 35 15 0 26 Jun 2018
Efficient Bayesian Inference for a Gaussian Process Density Model Christian Donner Manfred Opper 29 14 0 29 May 2018
Large-Scale Cox Process Inference using Variational Fourier Features S. T. John J. Hensman 15 30 0 03 Apr 2018