Slice sampling covariance hyperparameters of latent Gaussian models

4 June 2010

Papers citing "Slice sampling covariance hyperparameters of latent Gaussian models"

26 / 26 papers shown

Title
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data Mengying Lei A. Labbe Lijun Sun 23 1 0 21 Aug 2022
AutoIP: A United Framework to Integrate Physics into Gaussian Processes D. Long Zihan Wang Aditi S. Krishnapriyan Robert M. Kirby Shandian Zhe Michael W. Mahoney AI4CE 32 14 0 24 Feb 2022
Bayesian Optimisation for Active Monitoring of Air Pollution Sigrid Passano Hellan Christopher G. Lucas Nigel H. Goddard 27 10 0 15 Feb 2022
A Bayesian take on option pricing with Gaussian processes Martin Tegnér Stephen J. Roberts GP 11 2 0 07 Dec 2021
Validating Gaussian Process Models with Simulation-Based Calibration John Mcleod F. Simpson 22 3 0 27 Oct 2021
Bayesian data combination model with Gaussian process latent variable model for mixed observed variables under NMAR missingness Masaki Mitsuhiro T. Hoshino 11 1 0 01 Sep 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
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization Valerio Perrone Huibin Shen Aida Zolic I. Shcherbatyi Amr Ahmed ... Barbara Pogorzelska Miroslav Miladinovic K. Kenthapadi Matthias Seeger Cédric Archambeau 31 16 0 15 Dec 2020
Approximate Inference for Fully Bayesian Gaussian Process Regression V. Lalchand C. Rasmussen GP 33 51 0 31 Dec 2019
Gaussian Processes with Errors in Variables: Theory and Computation Shuang Zhou D. Pati Tianying Wang Yun Yang R. Carroll 24 3 0 14 Oct 2019
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language Jamie Fairbrother Christopher Nemeth M. Rischard Johanni Brea Thomas Pinder GP VLM 21 24 0 21 Dec 2018
A Spectral Approach to Gradient Estimation for Implicit Distributions Jiaxin Shi Shengyang Sun Jun Zhu 17 90 0 07 Jun 2018
BOCK : Bayesian Optimization with Cylindrical Kernels Changyong Oh E. Gavves Max Welling 15 134 0 05 Jun 2018
Posterior Inference for Sparse Hierarchical Non-stationary Models K. Monterrubio-Gómez L. Roininen S. Wade Theo Damoulas Mark Girolami 27 27 0 04 Apr 2018
Gaussian Processes for Survival Analysis T. Fernandez Nicolás Rivera Yee Whye Teh GP 24 75 0 02 Nov 2016
Auxiliary gradient-based sampling algorithms Michalis K. Titsias O. Papaspiliopoulos 22 40 0 30 Oct 2016
Asymptotically exact inference in differentiable generative models Matthew M. Graham Amos J. Storkey BDL 21 33 0 25 May 2016
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) Maurizio Filippone Raphael Engler 24 31 0 22 Jan 2015
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces Kevin Swersky David Duvenaud Jasper Snoek Frank Hutter Michael A. Osborne BDL 96 79 0 14 Sep 2014
Bayesian Optimization with Unknown Constraints M. Gelbart Jasper Snoek Ryan P. Adams 38 441 0 22 Mar 2014
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data Thomas A. Lasko 27 45 0 19 Feb 2014
Bayesian Conditional Density Filtering S. Qamar Rajarshi Guhaniyogi David B. Dunson 45 11 0 15 Jan 2014
Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox J. Vanhatalo J. Riihimaki Jouni Hartikainen Pasi Jylänki Ville Tolvanen Aki Vehtari GP 33 46 0 25 Jun 2012
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 75 7,845 0 13 Jun 2012
Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks Vinayak A. Rao Yee Whye Teh 41 47 0 14 Feb 2012
Bayesian Nonparametric Covariance Regression E. Fox David B. Dunson 64 103 0 11 Jan 2011