| Active learning for deep Gaussian process surrogates A Sauer, RB Gramacy, D Higdon Technometrics 65 (1), 4-18, 2023 | 90 | 2023 |
| Vecchia-approximated deep Gaussian processes for computer experiments A Sauer, A Cooper, RB Gramacy Journal of Computational and Graphical Statistics 32 (3), 824-837, 2023 | 41 | 2023 |
| Triangulation candidates for bayesian optimization RB Gramacy, A Sauer, N Wycoff Advances in Neural Information Processing Systems 35, 35933-35945, 2022 | 20 | 2022 |
| Non-stationary Gaussian process surrogates A Sauer, A Cooper, RB Gramacy arXiv preprint arXiv:2305.19242, 2023 | 11 | 2023 |
| Contour Location for Reliability in Airfoil Simulation Experiments using Deep Gaussian Processes AS Booth, SA Renganathan, RB Gramacy arXiv preprint arXiv:2308.04420, 2023 | 7 | 2023 |
| deepgp: Deep Gaussian Processes using MCMC AS Booth R package version 1 (1), 2023 | 5 | 2023 |
| Gradient-enhanced reliability analysis of transonic aeroelastic flutter B Stanford, A Sauer, K Jacobson, J Warner AIAA SCITECH 2022 Forum, 0632, 2022 | 5 | 2022 |
| Deep Gaussian Process Surrogates for Computer Experiments AE Sauer Virginia Tech, 2023 | 3 | 2023 |
| Actively learning deep Gaussian process models for failure contour and probability estimation. AS Booth, R Gramacy, A Renganathan AIAA SCITECH 2024 Forum, 0577, 2024 | 2 | 2024 |
| Voronoi Candidates for Bayesian Optimization N Wycoff, JW Smith, AS Booth, RB Gramacy arXiv preprint arXiv:2402.04922, 2024 | 1 | 2024 |
| Bayesian" Deep" Process Convolutions: An Application in Cosmology KR Moran, R Payne, E Lawrence, D Higdon, SA Walsh, AS Booth, J Kwan, ... arXiv preprint arXiv:2411.14747, 2024 | | 2024 |
| Hybrid Monte Carlo for Failure Probability Estimation with Gaussian Process Surrogates AS Booth, SA Renganathan arXiv preprint arXiv:2410.04496, 2024 | | 2024 |
| Monotonic warpings for additive and deep Gaussian processes SD Barnett, LJ Beesley, AS Booth, RB Gramacy, D Osthus arXiv preprint arXiv:2408.01540, 2024 | | 2024 |
