Approximate Inference in Related Multi-output Gaussian Process Regression (original) (raw)
Related papers
Gaussian Process Regression with Kernels Learned from Data
SIAM Conference on Computational Science and Engineering (CSE23), Amsterdam, February 26 - March 3, 2023, 2023
Multiple output gaussian process regression
2005
Sparse Physics-based Gaussian Process for Multi-output Regression using Variational Inference
Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, 2016
Additive kernels for high-dimensional gaussian process modeling
A Framework for Evaluating Approximation Methods for Gaussian Process Regression
2012
PHYSICS-BASED COVARIANCE MODELS FOR GAUSSIAN PROCESSES WITH MULTIPLE OUTPUTS
International Journal for Uncertainty Quantification, 2013
Scalable High-Order Gaussian Process Regression
2019
Approximation Methods for Gaussian Process Regression
Journal of Machine Learning for Modeling and Computing, 2021
Approximation of Gaussian Process Regression Models after Training
2008
Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics
Journal of Computational Physics, 2022
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
Journal of Machine Learning for Modeling and Computing
Using the Equivalent Kernel to Understand Gaussian Process Regression
2005
Gaussian Processes for Regression and Optimisation
Online Sparse Multi-Output Gaussian Process Regression and Learning
IEEE Transactions on Signal and Information Processing over Networks
Gaussian process regression with functional covariates and multivariate response
Chemometrics and Intelligent Laboratory Systems, 2017
Efficient multioutput Gaussian processes through variational inducing kernels
2011
Gaussian Process Regression for Structured Data Sets
Yermek Kapushev, Evgeny Burnaev
Fast large scale Gaussian process regression using the improved fast Gauss transform
Approximate Inference for Fully Bayesian Gaussian Process Regression
ArXiv, 2019
Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels
Lecture Notes in Computer Science, 2019
Adding Flight Mechanics to Flight Loads Surrogate Model using Multi-Output Gaussian Processes
17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2016
Computationally Efficient Algorithm for Gaussian Process Regression in Case of Structured Samples 1
A Mixed-Categorical Correlation Kernel for Gaussian Process
SSRN Electronic Journal
Two-step Gaussian Process Regression Improving Performance of Training and Prediction
Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018), 2018
Variational inference for uncertainty on the inputs of gaussian process models
Hierarchical Gaussian process mixtures for regression
2005
Non-linear process convolutions for multi-output Gaussian processes
2019
Efficient Marginal Likelihood Computation for Gaussian Process Regression
2011
Comprising Prior Knowledge in Dynamic Gaussian Process Models