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Variable selection for Gaussian processes via sensitivity analysis of
  the posterior predictive distribution
v1v2v3 (latest)

Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution

International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
21 December 2017
Topi Paananen
Juho Piironen
Michael Riis Andersen
Aki Vehtari
ArXiv (abs)PDFHTML

Papers citing "Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution"

20 / 20 papers shown
Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control
Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control
Gabriele Fadini
Deepak Ingole
Tong Duy Son
Alisa Rupenyan
64
0
0
03 Dec 2025
Identifiability and Sensitivity Analysis of Kriging Weights for the
  Matern Kernel
Identifiability and Sensitivity Analysis of Kriging Weights for the Matern Kernel
Amanda Muyskens
Benjamin W. Priest
I. Goumiri
M. Schneider
137
1
0
10 Oct 2024
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process
  Regression
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
Lu Zou
Liang Ding
379
1
0
20 Mar 2024
Nowcasting with mixed frequency data using Gaussian processes
Nowcasting with mixed frequency data using Gaussian processes
Niko Hauzenberger
Massimiliano Marcellino
Michael Pfarrhofer
Anna Stelzer
208
0
0
16 Feb 2024
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
179
15
0
11 Jun 2023
Quantile-constrained Wasserstein projections for robust interpretability
  of numerical and machine learning models
Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning modelsElectronic Journal of Statistics (EJS), 2022
Marouane Il Idrissi
Nicolas Bousquet
Fabrice Gamboa
Bertrand Iooss
Jean-Michel Loubes
299
7
0
23 Sep 2022
Model Predictive Robustness of Signal Temporal Logic Predicates
Model Predictive Robustness of Signal Temporal Logic PredicatesIEEE Robotics and Automation Letters (RA-L), 2022
Yuan-Chuen Lin
Haoxuan Li
Matthias Althoff
AAML
302
15
0
16 Sep 2022
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and
  Inference in Sparsity-Aware Modeling
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware ModelingIEEE Signal Processing Magazine (IEEE Signal Process. Mag.), 2022
Lei Cheng
Feng Yin
Sergios Theodoridis
S. Chatzis
Tsung-Hui Chang
369
93
0
28 May 2022
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional
  Gaussian Processes
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes
Hugh Dance
Brooks Paige
GP
286
12
0
08 Nov 2021
Efficient Fourier representations of families of Gaussian processes
Efficient Fourier representations of families of Gaussian processes
P. Greengard
329
4
0
28 Sep 2021
Computationally Efficient High-Dimensional Bayesian Optimization via
  Variable Selection
Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection
Yi Shen
Carl Kingsford
338
15
0
20 Sep 2021
Screening the Discrepancy Function of a Computer Model
Screening the Discrepancy Function of a Computer Model
P. Barbillon
A. Forte
Rui Paulo
189
1
0
06 Sep 2021
Select Wisely and Explain: Active Learning and Probabilistic Local
  Post-hoc Explainability
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability
Aditya Saini
Ranjitha Prasad
BDL
287
21
0
16 Aug 2021
Benchmarking the Performance of Bayesian Optimization across Multiple
  Experimental Materials Science Domains
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domainsnpj Computational Materials (npj Comput Mater), 2021
Qiaohao Liang
Aldair E. Gongora
Zekun Ren
A. Tiihonen
Zhe Liu
...
K. Hippalgaonkar
Benji Maruyama
Keith A. Brown
John W Fisher Iii
Tonio Buonassisi
162
176
0
23 May 2021
Hierarchical Gaussian Processes with Wasserstein-2 Kernels
Hierarchical Gaussian Processes with Wasserstein-2 Kernels
S. Popescu
D. Sharp
James H. Cole
Ben Glocker
358
5
0
28 Oct 2020
Variable selection for Gaussian process regression through a sparse
  projection
Variable selection for Gaussian process regression through a sparse projection
Chiwoo Park
David J. Borth
Nicholas S. Wilson
Chad N. Hunter
221
8
0
25 Aug 2020
Gaussian Process Regression with Local Explanation
Gaussian Process Regression with Local Explanation
Yuya Yoshikawa
Tomoharu Iwata
FAtt
257
26
0
03 Jul 2020
lgpr: An interpretable nonparametric method for inferring covariate
  effects from longitudinal data
lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data
Juho Timonen
Henrik Mannerstrom
Aki Vehtari
Harri Lähdesmäki
252
20
0
07 Dec 2019
Uncertainty-aware Sensitivity Analysis Using Rényi Divergences
Uncertainty-aware Sensitivity Analysis Using Rényi DivergencesConference on Uncertainty in Artificial Intelligence (UAI), 2019
Topi Paananen
Michael Riis Andersen
Aki Vehtari
125
4
0
17 Oct 2019
Efficient estimation of divergence-based sensitivity indices with
  Gaussian process surrogates
Efficient estimation of divergence-based sensitivity indices with Gaussian process surrogates
A. Eggels
D. Crommelin
242
0
0
08 Apr 2019
1
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