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Rates of contraction of posterior distributions based on Gaussian
  process priors

Rates of contraction of posterior distributions based on Gaussian process priors

18 June 2008
Van der Vaart
V. Zanten
ArXiv (abs)PDFHTML

Papers citing "Rates of contraction of posterior distributions based on Gaussian process priors"

50 / 196 papers shown
Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process models
Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process models
Takashi Hayakawa
Satoshi Asai
119
0
0
09 Nov 2025
Bayesian Optimization for Dynamic Pricing and Learning
Bayesian Optimization for Dynamic Pricing and Learning
Anush Anand
Pranav Agrawal
Tejas Bodas
192
0
0
14 Oct 2025
Relative Information Gain and Gaussian Process Regression
Relative Information Gain and Gaussian Process Regression
Hamish Flynn
156
0
0
05 Oct 2025
BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
BOW: Bayesian Optimization over Windows for Motion Planning in Complex EnvironmentsIEEE Robotics and Automation Letters (IEEE RA-L), 2025
Sourav Raxit
Abdullah Al Redwan Newaz
Paulo Padrao
Jose Fuentes
Leonardo Bobadilla
134
1
0
18 Aug 2025
Adaptive finite element type decomposition of Gaussian processes
Adaptive finite element type decomposition of Gaussian processes
Jaehoan Kim
A. Bhattacharya
D. Pati
113
0
0
29 May 2025
Adaptive sparse variational approximations for Gaussian process regression
Adaptive sparse variational approximations for Gaussian process regression
Dennis Nieman
Botond Szabó
433
0
0
04 Apr 2025
Koopman-Equivariant Gaussian Processes
Koopman-Equivariant Gaussian ProcessesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Nicolas Hoischen
Max Beier
Armin Lederer
A. Capone
Roland Toth
Sandra Hirche
AI4TS
387
6
0
10 Feb 2025
On strong posterior contraction rates for Besov-Laplace priors in the
  white noise model
On strong posterior contraction rates for Besov-Laplace priors in the white noise model
Emanuele Dolera
Stefano Favaro
Matteo Giordano
277
3
0
11 Nov 2024
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
M. Risser
M. Noack
Hengrui Luo
Ronald Pandolfi
GP
421
1
0
07 Nov 2024
Gaussian Processes for Observational Dose-Response Inference
Gaussian Processes for Observational Dose-Response Inference
Jake R. Dailey
175
0
0
25 Sep 2024
Meta-Posterior Consistency for the Bayesian Inference of Metastable System
Meta-Posterior Consistency for the Bayesian Inference of Metastable System
Zachary P Adams
Sayan Mukherjee
279
0
0
03 Aug 2024
Contraction rates for conjugate gradient and Lanczos approximate
  posteriors in Gaussian process regression
Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression
Bernhard Stankewitz
Botond Szabo
299
3
0
18 Jun 2024
Scalable Bayesian inference for heat kernel Gaussian processes on
  manifolds
Scalable Bayesian inference for heat kernel Gaussian processes on manifolds
Junhui He
Guoxuan Ma
Jian Kang
Ying Yang
328
3
0
22 May 2024
Contraction rates and projection subspace estimation with Gaussian
  process priors in high dimension
Contraction rates and projection subspace estimation with Gaussian process priors in high dimension
Elie Odin
François Bachoc
A. Lagnoux
402
0
0
06 Mar 2024
Deep Horseshoe Gaussian Processes
Deep Horseshoe Gaussian Processes
Ismael Castillo
Thibault Randrianarisoa
BDLUQCV
397
7
0
04 Mar 2024
Deep Gaussian Process Priors for Bayesian Inference in Nonlinear Inverse
  Problems
Deep Gaussian Process Priors for Bayesian Inference in Nonlinear Inverse Problems
Kweku Abraham
Neil Deo
188
5
0
21 Dec 2023
Adaptation using spatially distributed Gaussian Processes
Adaptation using spatially distributed Gaussian Processes
Botond Szabó
Amine Hadji
A. van der Vaart
339
4
0
21 Dec 2023
Posterior Concentration for Gaussian Process Priors under Rescaled and
  Hierarchical Matérn and Confluent Hypergeometric Covariance Functions
Posterior Concentration for Gaussian Process Priors under Rescaled and Hierarchical Matérn and Confluent Hypergeometric Covariance FunctionsElectronic Journal of Statistics (EJS), 2023
Xiao Fang
A. Bhadra
250
0
0
12 Dec 2023
Variational Gaussian Processes For Linear Inverse Problems
Variational Gaussian Processes For Linear Inverse ProblemsNeural Information Processing Systems (NeurIPS), 2023
Thibault Randrianarisoa
Botond Szabó
341
6
0
01 Nov 2023
Lipschitz Interpolation: Non-parametric Convergence under Bounded
  Stochastic Noise
Lipschitz Interpolation: Non-parametric Convergence under Bounded Stochastic Noise
J. Huang
Stephen J. Roberts
Jan-Peter Calliess
183
0
0
10 Oct 2023
Pointwise uncertainty quantification for sparse variational Gaussian
  process regression with a Brownian motion prior
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion priorNeural Information Processing Systems (NeurIPS), 2023
Luke Travis
Kolyan Ray
473
5
0
29 Sep 2023
Posterior Contraction Rates for Matérn Gaussian Processes on
  Riemannian Manifolds
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian ManifoldsNeural Information Processing Systems (NeurIPS), 2023
Paul Rosa
Viacheslav Borovitskiy
Alexander Terenin
Judith Rousseau
417
14
0
19 Sep 2023
Spatiotemporal Besov Priors for Bayesian Inverse Problems
Spatiotemporal Besov Priors for Bayesian Inverse ProblemsJournal of the American Statistical Association (JASA), 2023
Shiwei Lan
M. Pasha
Shuyi Li
Weining Shen
323
11
0
28 Jun 2023
Additive Multi-Index Gaussian process modeling, with application to
  multi-physics surrogate modeling of the quark-gluon plasma
Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasmaJournal of the American Statistical Association (JASA), 2023
Kevin Li
Simon Mak
J. Paquet
S. Bass
AI4CE
177
15
0
11 Jun 2023
Parametrization, Prior Independence, and the Semiparametric
  Bernstein-von Mises Theorem for the Partially Linear Model
Parametrization, Prior Independence, and the Semiparametric Bernstein-von Mises Theorem for the Partially Linear Model
C. D. Walker
393
1
0
06 Jun 2023
Masked Bayesian Neural Networks : Theoretical Guarantee and its
  Posterior Inference
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior InferenceInternational Conference on Machine Learning (ICML), 2023
Insung Kong
Dongyoon Yang
Jongjin Lee
Ilsang Ohn
Gyuseung Baek
Yongdai Kim
BDL
279
8
0
24 May 2023
Direct Bayesian Regression for Distribution-valued Covariates
Direct Bayesian Regression for Distribution-valued CovariatesElectronic Journal of Statistics (EJS), 2023
Bohao Tang
Sandipan Pramanik
Yi Zhao
B. Caffo
A. Datta
216
0
0
11 Mar 2023
Hierarchical shrinkage Gaussian processes: applications to computer code
  emulation and dynamical system recovery
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery
T. Tang
Simon Mak
David B. Dunson
283
5
0
01 Feb 2023
Semiparametric inference using fractional posteriors
Semiparametric inference using fractional posteriorsJournal of machine learning research (JMLR), 2023
Alice L'Huillier
Luke Travis
I. Castillo
Kolyan Ray
371
6
0
19 Jan 2023
Uncertainty quantification for sparse spectral variational
  approximations in Gaussian process regression
Uncertainty quantification for sparse spectral variational approximations in Gaussian process regressionElectronic Journal of Statistics (EJS), 2022
D. Nieman
Botond Szabó
Harry Van Zanten
362
6
0
21 Dec 2022
Heavy-Tailed Density Estimation
Heavy-Tailed Density Estimation
S. Tokdar
Sheng Jiang
Erika L Cunningham
160
3
0
16 Nov 2022
Optimal parameter estimation for linear SPDEs from multiple measurements
Optimal parameter estimation for linear SPDEs from multiple measurementsAnnals of Statistics (Ann. Stat.), 2022
R. Altmeyer
Anton Tiepner
Martin Wahl
228
12
0
04 Nov 2022
Consistent inference for diffusions from low frequency measurements
Consistent inference for diffusions from low frequency measurementsAnnals of Statistics (Ann. Stat.), 2022
Richard Nickl
396
10
0
24 Oct 2022
Optimal plug-in Gaussian processes for modelling derivatives
Optimal plug-in Gaussian processes for modelling derivatives
Zejian Liu
Meng Li
345
5
0
20 Oct 2022
Equispaced Fourier representations for efficient Gaussian process
  regression from a billion data points
Equispaced Fourier representations for efficient Gaussian process regression from a billion data points
P. Greengard
M. Rachh
A. Barnett
360
14
0
18 Oct 2022
Structured Optimal Variational Inference for Dynamic Latent Space Models
Structured Optimal Variational Inference for Dynamic Latent Space Models
Penghui Zhao
A. Bhattacharya
D. Pati
Bani Mallick
BDL
378
5
0
29 Sep 2022
On free energy barriers in Gaussian priors and failure of cold start
  MCMC for high-dimensional unimodal distributions
On free energy barriers in Gaussian priors and failure of cold start MCMC for high-dimensional unimodal distributions
Afonso S. Bandeira
Antoine Maillard
Richard Nickl
Sven Wang
382
18
0
05 Sep 2022
Besov-Laplace priors in density estimation: optimal posterior
  contraction rates and adaptation
Besov-Laplace priors in density estimation: optimal posterior contraction rates and adaptationElectronic Journal of Statistics (EJS), 2022
M. Giordano
402
8
0
30 Aug 2022
Polynomial time guarantees for sampling based posterior inference in
  high-dimensional generalised linear models
Polynomial time guarantees for sampling based posterior inference in high-dimensional generalised linear models
R. Altmeyer
280
8
0
28 Aug 2022
Masked Bayesian Neural Networks : Computation and Optimality
Insung Kong
Dongyoon Yang
Jongjin Lee
Ilsang Ohn
Yongdai Kim
TPM
352
1
0
02 Jun 2022
Fast Instrument Learning with Faster Rates
Fast Instrument Learning with Faster RatesNeural Information Processing Systems (NeurIPS), 2022
Ziyu Wang
Yuhao Zhou
Chao Ding
422
5
0
22 May 2022
On the inability of Gaussian process regression to optimally learn
  compositional functions
On the inability of Gaussian process regression to optimally learn compositional functionsNeural Information Processing Systems (NeurIPS), 2022
M. Giordano
Kolyan Ray
Johannes Schmidt-Hieber
389
18
0
16 May 2022
Optimal recovery and uncertainty quantification for distributed Gaussian
  process regression
Optimal recovery and uncertainty quantification for distributed Gaussian process regression
Amine Hadji
Tammo Hesselink
Botond Szabó
394
3
0
06 May 2022
Strong posterior contraction rates via Wasserstein dynamics
Strong posterior contraction rates via Wasserstein dynamicsProbability theory and related fields (PTRF), 2022
Emanuele Dolera
Stefano Favaro
E. Mainini
290
4
0
21 Mar 2022
Posterior Consistency for Bayesian Relevance Vector Machines
Posterior Consistency for Bayesian Relevance Vector MachinesJournal of machine learning research (JMLR), 2022
X. Fang
M. Ghosh
BDL
153
0
0
11 Feb 2022
Gaussian Process Regression in the Flat Limit
Gaussian Process Regression in the Flat LimitAnnals of Statistics (Ann. Stat.), 2022
Simon Barthelmé
P. Amblard
Nicolas M Tremblay
K. Usevich
GP
402
6
0
04 Jan 2022
Posterior contraction rates for constrained deep Gaussian processes in
  density estimation and classication
Posterior contraction rates for constrained deep Gaussian processes in density estimation and classication
François Bachoc
A. Lagnoux
295
5
0
14 Dec 2021
Laplace priors and spatial inhomogeneity in Bayesian inverse problems
Laplace priors and spatial inhomogeneity in Bayesian inverse problems
S. Agapiou
Sven Wang
436
21
0
10 Dec 2021
Improved inference for doubly robust estimators of heterogeneous
  treatment effects
Improved inference for doubly robust estimators of heterogeneous treatment effects
Hee-Choon Shin
Joseph Antonelli
265
7
0
05 Nov 2021
Continuous logistic Gaussian random measure fields for spatial
  distributional modelling
Continuous logistic Gaussian random measure fields for spatial distributional modelling
Athénais Gautier
D. Ginsbourger
259
0
0
06 Oct 2021
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