Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GP-GRIEF with a dense inducing-point grid and p=4 eigenfunctions instead of VFE with m=4 inducing points | GP regression with a squared-exponential kernel · Two-dimensional test problem | RMSE: 0.47 -> 0.34 | Helped |
| Add the diagonal correction term to the GRIEF kernel (ablation) | GP-GRIEF · Not specified | performance | No clear effect |
| Use grid-structured Nyström inducing points to fill out the input space rather than uniformly sampling inducing points from training data | Nyström kernel reconstruction · 100-dimensional squared-exponential kernel; training and joint train/test covariance | covariance reconstruction error | Helped |
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