Active Learning for Deep Gaussian Process Surrogates

15 December 2020

Papers citing "Active Learning for Deep Gaussian Process Surrogates"

9 / 9 papers shown

Title
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes Markus Lange-Hegermann Christoph Zimmer AI4TS 47 0 0 17 May 2024
Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes Xiao Liu K. Yeo 23 3 0 08 Feb 2023
Active Learning of Piecewise Gaussian Process Surrogates Chiwoo Park R. Waelder Bonggwon Kang Benji Maruyama Soondo Hong R. Gramacy GP 27 1 0 20 Jan 2023
Bayesian Active Learning with Fully Bayesian Gaussian Processes Christoffer Riis Francisco Antunes F. B. Hüttel C. L. Azevedo Francisco Câmara Pereira GP 15 22 0 20 May 2022
Triangulation candidates for Bayesian optimization R. Gramacy Anna Sauer Nathan Wycoff 24 13 0 14 Dec 2021
A Sparse Expansion For Deep Gaussian Processes Liang Ding Rui Tuo Shahin Shahrampour 19 6 0 11 Dec 2021
Deep Gaussian Process Emulation using Stochastic Imputation Deyu Ming D. Williamson S. Guillas 11 30 0 04 Jul 2021
Local Gaussian process approximation for large computer experiments R. Gramacy D. Apley 127 391 0 02 Mar 2013
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,267 0 09 Jun 2012