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1006.0868
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Slice sampling covariance hyperparameters of latent Gaussian models
4 June 2010
Iain Murray
Ryan P. Adams
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Papers citing
"Slice sampling covariance hyperparameters of latent Gaussian models"
50 / 91 papers shown
Deep Gaussian Processes for Functional Maps
Matthew Lowery
Zhitong Xu
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Daniel S. Johnson
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181
0
0
24 Oct 2025
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International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Nicolas Hoischen
Max Beier
Armin Lederer
A. Capone
Roland Toth
Sandra Hirche
AI4TS
396
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0
10 Feb 2025
Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Manan Saxena
Tinghua Chen
Justin D. Silverman
165
1
0
07 Oct 2024
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
Jian Xu
Zhiqi Lin
Min Chen
Junmei Yang
Delu Zeng
John Paisley
506
4
0
12 Aug 2024
Stein Random Feature Regression
Houston Warren
Rafael Oliveira
Fabio Ramos
BDL
443
0
0
01 Jun 2024
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data
Navid Ziaei
Behzad Nazari
Uri T. Eden
A. Widge
Ali Yousefi
383
5
0
29 Jan 2024
Bayesian Non-linear Latent Variable Modeling via Random Fourier Features
M. Zhang
Gregory W. Gundersen
Barbara Engelhardt
BDL
246
5
0
14 Jun 2023
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
International Conference on Machine Learning (ICML), 2023
Louis C. Tiao
Vincent Dutordoir
Victor Picheny
BDL
282
1
0
27 Apr 2023
GaPT: Gaussian Process Toolkit for Online Regression with Application to Learning Quadrotor Dynamics
IEEE International Conference on Robotics and Automation (ICRA), 2023
Francesco Crocetti
Jeffrey Mao
Alessandro Saviolo
G. Costante
Giuseppe Loianno
GP
193
8
0
14 Mar 2023
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Neural Information Processing Systems (NeurIPS), 2022
V. Lalchand
W. Bruinsma
David R. Burt
C. Rasmussen
GP
259
9
0
04 Nov 2022
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data
Mengying Lei
A. Labbe
Lijun Sun
261
1
0
21 Aug 2022
Gaussian Process Surrogate Models for Neural Networks
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
Michael Y. Li
Erin Grant
Thomas Griffiths
BDL
SyDa
360
10
0
11 Aug 2022
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
International Conference on Machine Learning (ICML), 2022
D. Long
Liang Luo
Aditi S. Krishnapriyan
Robert M. Kirby
Shandian Zhe
Michael W. Mahoney
AI4CE
417
23
0
24 Feb 2022
Bayesian Optimisation for Active Monitoring of Air Pollution
AAAI Conference on Artificial Intelligence (AAAI), 2022
Sigrid Passano Hellan
Christopher G. Lucas
Nigel H. Goddard
285
10
0
15 Feb 2022
A Bayesian take on option pricing with Gaussian processes
Martin Tegnér
Stephen J. Roberts
GP
153
2
0
07 Dec 2021
Validating Gaussian Process Models with Simulation-Based Calibration
John Mcleod
F. Simpson
248
3
0
27 Oct 2021
Fast and Scalable Inference for Spatial Extreme Value Models
Mei-Ching Chen
R. Ramezan
Martin Lysy
325
1
0
13 Oct 2021
Bayesian data combination model with Gaussian process latent variable model for mixed observed variables under NMAR missingness
Masaki Mitsuhiro
T. Hoshino
150
1
0
01 Sep 2021
Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression
Bayesian Analysis (BA), 2021
Mengying Lei
A. Labbe
Lijun Sun
462
8
0
31 Aug 2021
Machine Learning based optimization for interval uncertainty propagation
Mechanical systems and signal processing (MSSP), 2021
Alice Cicirello
Filippo Giunta
115
13
0
21 Jun 2021
How Bayesian Should Bayesian Optimisation Be?
George De Ath
Richard Everson
J. Fieldsend
207
8
0
03 May 2021
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Luhuan Wu
Andrew C. Miller
Lauren Anderson
Geoff Pleiss
David M. Blei
John P. Cunningham
343
9
0
28 Feb 2021
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
Knowledge Discovery and Data Mining (KDD), 2020
Valerio Perrone
Huibin Shen
Aida Zolic
I. Shcherbatyi
Amr Ahmed
...
Barbara Pogorzelska
Miroslav Miladinovic
K. Kenthapadi
Matthias Seeger
Cédric Archambeau
349
16
0
15 Dec 2020
Marginalised Gaussian Processes with Nested Sampling
Neural Information Processing Systems (NeurIPS), 2020
F. Simpson
V. Lalchand
C. Rasmussen
GP
334
12
0
30 Oct 2020
Learning Insulin-Glucose Dynamics in the Wild
Machine Learning in Health Care (MLHC), 2020
Andrew C. Miller
N. Foti
E. Fox
AI4TS
216
24
0
06 Aug 2020
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
Simone Rossi
Markus Heinonen
Edwin V. Bonilla
Zheyan Shen
Maurizio Filippone
UQCV
BDL
433
0
0
06 Mar 2020
Approximate Inference for Fully Bayesian Gaussian Process Regression
Symposium on Advances in Approximate Bayesian Inference (AABI), 2019
V. Lalchand
C. Rasmussen
GP
352
59
0
31 Dec 2019
Function-Space Distributions over Kernels
Neural Information Processing Systems (NeurIPS), 2019
Gregory W. Benton
Wesley J. Maddox
Jayson Salkey
J. Albinati
A. Wilson
BDL
GP
170
26
0
29 Oct 2019
Gaussian Processes with Errors in Variables: Theory and Computation
Journal of machine learning research (JMLR), 2019
Shuang Zhou
D. Pati
Tianying Wang
Yun Yang
R. Carroll
363
9
0
14 Oct 2019
Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models
Bayesian Analysis (BA), 2019
B. Hrafnkelsson
S. Siegert
Raphael Huser
H. Bakka
Árni V. Jóhannesson
249
18
0
27 Jul 2019
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
IEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2019
M. Zhang
Bianca Dumitrascu
Sinead Williamson
Barbara E. Engelhardt
755
10
0
24 May 2019
Neutron Transmission Strain Tomography for Non-Constant Stress-Free Lattice Spacing
Nuclear Instruments and Methods in Physics Reseach B (NIM B), 2019
J. Hendriks
Carl Jidling
Thomas B. Schon
A. Wills
C. Wensrich
E. Kisi
177
7
0
15 May 2019
Bayesian Optimization for Policy Search via Online-Offline Experimentation
Benjamin Letham
E. Bakshy
OffRL
297
62
0
01 Apr 2019
Bayesian prediction of jumps in large panels of time series data
Angelos N. Alexopoulos
P. Dellaportas
O. Papaspiliopoulos
AI4TS
350
5
0
28 Mar 2019
Combinatorial Bayesian Optimization using the Graph Cartesian Product
Neural Information Processing Systems (NeurIPS), 2019
Changyong Oh
Jakub M. Tomczak
E. Gavves
Max Welling
280
137
0
01 Feb 2019
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language
Jamie Fairbrother
Christopher Nemeth
M. Rischard
Johanni Brea
Thomas Pinder
GP
VLM
282
31
0
21 Dec 2018
Sequential sampling of Gaussian process latent variable models
Martin Tegnér
Benjamin Bloem-Reddy
Stephen J. Roberts
170
2
0
13 Jul 2018
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning
Danil Kuzin
Le Yang
Olga Isupova
Lyudmila Mihaylova
211
7
0
09 Jul 2018
Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization
Advances in Intelligent Systems and Computing (AISC), 2018
Dipti Jasrasaria
Edward O. Pyzer-Knapp
260
24
0
03 Jul 2018
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi
Shengyang Sun
Jun Zhu
340
100
0
07 Jun 2018
BOCK : Bayesian Optimization with Cylindrical Kernels
Changyong Oh
E. Gavves
Max Welling
364
150
0
05 Jun 2018
Posterior Inference for Sparse Hierarchical Non-stationary Models
K. Monterrubio-Gómez
L. Roininen
S. Wade
Theo Damoulas
Mark Girolami
384
28
0
04 Apr 2018
Dependent relevance determination for smooth and structured sparse regression
Anqi Wu
Oluwasanmi Koyejo
Jonathan W. Pillow
425
8
0
28 Nov 2017
A determinant-free method to simulate the parameters of large Gaussian fields
L. Ellam
Heiko Strathmann
Mark Girolami
Iain Murray
285
3
0
11 Sep 2017
Discretization-free Knowledge Gradient Methods for Bayesian Optimization
Jian Wu
P. Frazier
BDL
190
9
0
20 Jul 2017
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes
E.C. Garrido-Merchán
Daniel Hernández-Lobato
266
281
0
12 Jun 2017
Asynchronous Distributed Variational Gaussian Processes for Regression
Hao Peng
Shandian Zhe
Y. Qi
186
30
0
22 Apr 2017
Efficient acquisition rules for model-based approximate Bayesian computation
Marko Jarvenpaa
Michael U. Gutmann
Arijus Pleska
Aki Vehtari
Pekka Marttinen
TPM
521
72
0
03 Apr 2017
Practical Bayesian Optimization for Variable Cost Objectives
Mark McLeod
Michael A. Osborne
Stephen J. Roberts
177
32
0
13 Mar 2017
Inference for log Gaussian Cox processes using an approximate marginal posterior
Shinichiro Shirota
A. Gelfand
264
7
0
30 Nov 2016
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