Results 281 to 290 of about 110,828 (307)

Gaussian process regression with skewed errors

Journal of Computational and Applied Mathematics, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
M. T. Alodat, Mohammed K. Shakhatreh
openaire   +1 more source

Efficient sparsification for Gaussian process regression

Neurocomputing, 2016
Abstract Sparse Gaussian process models provide an efficient way to perform regression on large data sets. Sparsification approaches deal with the selection of a representative subset of available training data for inducing the sparse model approximation.
Jens Schreiter, Duy Nguyen-Tuong, Marc Toussaint   +2 more
openaire   +1 more source

Gaussian Process Regression with Fluid Hyperpriors

2004
A Gaussian process model can be learned from data by identifying the covariance matrix of its sample values. The matrix usually depends on some fixed parameters called input length scales. Their estimation is equivalent to finding the corresponding diffeomorphism of the process inputs.
Ramunas Girdziusas, Jorma Laaksonen
openaire   +1 more source

Asynchronous Distributed Gaussian Process Regression

Proceedings of the AAAI Conference on Artificial Intelligence
In this paper, we address a practical distributed Bayesian learning problem with asynchronous measurements and predictions due to diverse computational conditions. To this end, asynchronous distributed Gaussian process (AsyncDGP) regression is proposed, which is the first effective online distributed Gaussian processes (GPs) approach to improve the ...
Zewen Yang, Xiaobing Dai, Sandra Hirche
openaire   +1 more source

Nonparametric Regression via Variance-Adjusted Gradient Boosting Gaussian Process Regression

IEEE Transactions on Knowledge and Data Engineering, 2021
Hsin-Min Lu, Jih-Shin Chen, WEI-CHUN Liao   +2 more
exaly  

Robust Gaussian process regression with a bias model

Pattern Recognition, 2022
Chiwoo Park
exaly  

Sparse Spectrum Gaussian Process Regression.

J. Mach. Learn. Res., 2010
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algorithm for regression tasks. We compare the achievable trade-offs between predictive accuracy and computational requirements, and show that these are typically superior to ...
Lázaro-Gredilla, M., Quiñonero-Candela, J., Rasmussen, C., Figueiras-Vidal, A.   +3 more
openaire   +2 more sources

Convergence of Gaussian Process Regression with Estimated Hyper-Parameters and Applications in Bayesian Inverse Problems

SIAM-ASA Journal on Uncertainty Quantification, 2020
A L Teckentrup
exaly  

Dynamic Transfer Gaussian Process Regression

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022
Pengfei Wei 0001, Xinghua Qu, Wen Song 0004, Zejun Ma 0001   +3 more
openaire   +1 more source

Gaussian Process Regression for Materials and Molecules

Chemical Reviews, 2021
Volker L Deringer, Albert P Bartok, Noam Bernstein   +2 more
exaly